yao-meta-skill/reports/skill-interpretation.json

{
  "skill_summary": {
    "name": "yao-meta-skill",
    "title": "Yao Meta Skill",
    "display_name": "Yao Meta Skill",
    "description": "Create, refactor, evaluate, and package agent skills from workflows, prompts, transcripts, docs, or notes. Use when asked to create a skill, turn a repeated process into a reusable skill, improve an existing skill, add evals, or package a skill for team reuse.",
    "maturity": "governed",
    "updated_at": "2026-03-31",
    "core_value": "把一次性经验沉淀为可复用、可评估、可迁移的 Skill 包体。",
    "audience": "Skill 作者、复用团队和后续 reviewer。",
    "deliverables": [
      "SKILL.md",
      "agents/interface.yaml",
      "skill-ir/examples/yao-meta-skill.json",
      "reports/compiled_targets.md",
      "reports/output_quality_scorecard.md",
      "reports/output_execution_runs.md",
      "reports/output_blind_review_pack.md",
      "reports/output_review_kit.md",
      "reports/output_review_adjudication.md",
      "reports/benchmark_reproducibility.md",
      "reports/output_blind_answer_key.json",
      "reports/conformance_matrix.md",
      "reports/security_trust_report.md",
      "reports/runtime_permission_probes.md",
      "reports/skill_atlas.html",
      "reports/registry_audit.md",
      "reports/package_verification.md",
      "reports/install_simulation.md",
      "reports/upgrade_check.md",
      "reports/adoption_drift_report.md",
      "reports/review_waivers.md",
      "reports/world_class_evidence_plan.md",
      "reports/world_class_evidence_ledger.md",
      "reports/review_annotations.md",
      "reports/review-studio.html",
      "reports/skill-interpretation.html",
      "reports/skill-overview.html",
      "reports/skill-interpretation.json"
    ],
    "flow": [
      "输入材料",
      "Skill 包体",
      "可复用能力"
    ]
  },
  "scorecard": {
    "completeness_score": {
      "label": "完整度",
      "score": 100,
      "reasons": [
        "SKILL.md 已存在，是 Skill 的入口。",
        "README.md 已存在，便于人工阅读。",
        "agents/interface.yaml 已存在，便于跨平台适配。",
        "manifest.json 已存在，生命周期信息可追踪。",
        "reports/ 已存在，生成证据可以随包体迁移。"
      ]
    },
    "trigger_score": {
      "label": "触发清晰",
      "score": 100,
      "reasons": [
        "frontmatter description 已存在，具备基础路由面。",
        "description 有足够长度说明任务边界。",
        "description 已包含使用场景或排除边界信号。",
        "evals/ 已存在，可承载触发样例或质量检查。",
        "intent-confidence 报告已生成，可辅助判断触发稳定性。"
      ]
    },
    "evidence_score": {
      "label": "证据充分",
      "score": 100,
      "reasons": [
        "已生成 20 / 20 类报告证据。",
        "skill-ir.json 已存在。",
        "compiled_targets.json 已存在。",
        "intent-dialogue.json 已存在。"
      ]
    },
    "maintainability_score": {
      "label": "可维护性",
      "score": 100,
      "reasons": [
        "SKILL.md 约 360 个词/字。",
        "入口文件保持克制，可维护性较好。",
        "references/ 已承载扩展指导。",
        "scripts/ 已承载确定性逻辑。",
        "evals/ 已承载可迁移检查。"
      ]
    },
    "portability_score": {
      "label": "可迁移性",
      "score": 100,
      "reasons": [
        "agents/interface.yaml 已存在。",
        "manifest.json 已存在。",
        "目标平台或 adapter target 已声明。",
        "入口文件未发现明显私有绝对路径。"
      ]
    },
    "context_cost": {
      "label": "上下文成本",
      "score": 42,
      "reasons": [
        "入口约 360 个词/字，references 约 16914 个词/字。",
        "分数越高代表上下文成本越低。",
        "上下文成本偏高，建议压缩入口或拆分 references。"
      ]
    }
  },
  "capability_profile": {
    "archetype": "governed",
    "task_family": "Execution operation",
    "maturity": "governed",
    "trigger_strength": "手动触发 + description 路由",
    "reuse_scope": "跨平台",
    "matrix": {
      "execution_certainty": 72,
      "knowledge_density": 80
    }
  },
  "principle_model": {
    "nodes": [
      {
        "title": "意图澄清",
        "body": "Turn repeated workflows, prompts, transcripts, runbooks, documents, or existing skill packages into routeable, evaluable, packageable, and governable agent skills for personal, team, library, or governed reuse."
      },
      {
        "title": "边界路由",
        "body": "用 frontmatter description 决定是否触发，并写明相邻非目标。"
      },
      {
        "title": "资产分层",
        "body": "把入口、参考、脚本、评估和报告拆到各自目录，避免 SKILL.md 膨胀。"
      },
      {
        "title": "证据回路",
        "body": "Ask only the highest-leverage clarification before adding package weight."
      },
      {
        "title": "漂移观察",
        "body": "Add near-neighbor exclusions and route evals before expanding workflow steps."
      },
      {
        "title": "杠杆升级",
        "body": "Name the recurring job, expected input, output, and strongest non-goal in compact language."
      }
    ],
    "layers": [
      "入口层",
      "参考层",
      "脚本层",
      "评估层",
      "报告层"
    ]
  },
  "contract_boundary": {
    "trigger": {
      "description": "Create, refactor, evaluate, and package agent skills from workflows, prompts, transcripts, docs, or notes. Use when asked to create a skill, turn a repeated process into a reusable skill, improve an existing skill, add evals, or package a skill for team reuse.",
      "activation": "manual",
      "execution": "inline",
      "shell": "bash"
    },
    "inputs": [
      "rough workflow notes, SOPs, runbooks, prompts, transcripts, documents, or repeated task descriptions",
      "an existing skill directory that needs refactor, evaluation, packaging, or governance hardening",
      "target platform requirements such as OpenAI, Claude, generic Agent Skills, or team distribution",
      "benchmark references, local constraints, desired maturity tier, and review standards"
    ],
    "outputs": [
      "A working skill package with lean SKILL.md, aligned agents/interface.yaml, justified references, scripts only when useful, eval evidence, reports, packaging metadata, and clear next iteration recommendations.",
      "结构化 Skill 目录，共 8 类关键资产。"
    ],
    "should_trigger": [
      "把重复流程沉淀为可复用的 agent skill。",
      "把分散提示词、对话记录或操作规范整理为稳定能力。",
      "团队复用前，需要明确触发边界、质量证据和维护责任。"
    ],
    "should_not_trigger": [
      "只需要一次性回答、没有复用价值的临时请求。",
      "要求直接执行相邻任务，而不是沉淀或使用这个 Skill。",
      "缺少必要事实且用户不允许澄清的场景。"
    ],
    "boundary_cards": [
      {
        "label": "Owned",
        "body": "Create, refactor, evaluate, and package agent skills from workflows, prompts, transcripts, docs, or notes. Use when asked to create a skill, turn a repeated process into a reusable skill, improve an existing skill, add evals, or package a skill for team reuse."
      },
      {
        "label": "Adjacent",
        "body": "相邻任务需要先确认是否应转为独立 Skill。"
      },
      {
        "label": "Excluded",
        "body": "不替代人工事实核查，也不静默扩大职责。"
      }
    ]
  },
  "quality_review": {
    "strengths": [
      "触发面保持精简，并锚定在 frontmatter description。",
      "已生成 Skill IR，核心语义可先于平台打包被审查和迁移。",
      "已生成目标编译报告，可审查 IR 到 OpenAI、Claude、generic 等目标契约的映射。",
      "已生成 Output Eval Lab scorecard，可比较 with-skill 与 baseline 输出质量。",
      "已生成 Output Execution Runs，可区分记录样本、命令执行和模型执行证据。",
      "已生成 Output Review Adjudication，可记录盲评决策、一致率和待评审项。"
    ],
    "gaps": [
      "上下文成本需要补强：入口约 360 个词/字，references 约 16914 个词/字。"
    ],
    "recommendations": [
      "先改触发边界，再扩展工作流。",
      "只把重复且稳定的步骤沉淀为脚本。",
      "每次升级后重新生成报告并检查分数原因。"
    ],
    "artifact_design": {
      "design_system": "metric editorial",
      "highlights": [
        "Execution-focused technical artifact with environment assumptions, copyable commands, expected outputs, and side effects made explicit.",
        "Name the working directory and required inputs for commands.",
        "Mark destructive, networked, or external side-effect operations.",
        "Prefer the smallest runnable snippet over broad framework scaffolding."
      ]
    },
    "prompt_quality": {
      "overall_quality_score": 89.0,
      "highlights": [
        "Primary prompt task family: Execution operation.",
        "Complexity: expert — multiple task families plus governance, evaluation, or expert-level constraints",
        "Completeness: 100/100.",
        "Clarity: 85/100."
      ]
    },
    "system_model": {
      "stability": {
        "score": 100,
        "band": "system-ready"
      },
      "highlights": [
        "Stability: system-ready (100/100).",
        "Owned job: Turn repeated workflows, prompts, transcripts, runbooks, documents, or existing skill packages into routeable, evaluable, packageable, and governable agent skills for personal, team, library, or governed reuse.",
        "Leverage: Tune the frontmatter description — Name the recurring job, expected input, output, and strongest non-goal in compact language.",
        "Leverage: Install output self-repair checks — Add only the checks that prevent recurring output mistakes."
      ]
    }
  },
  "risk_governance": {
    "risks": [
      {
        "name": "误触发风险",
        "impact": 3,
        "probability": 1,
        "signal": "frontmatter description 已存在，具备基础路由面。",
        "response": "先补证据和边界，再增加包体复杂度。"
      },
      {
        "name": "输出漂移风险",
        "impact": 2,
        "probability": 1,
        "signal": "已生成 20 / 20 类报告证据。",
        "response": "先补证据和边界，再增加包体复杂度。"
      },
      {
        "name": "证据不足风险",
        "impact": 3,
        "probability": 1,
        "signal": "已生成 20 / 20 类报告证据。",
        "response": "先补证据和边界，再增加包体复杂度。"
      },
      {
        "name": "包体膨胀风险",
        "impact": 2,
        "probability": 1,
        "signal": "SKILL.md 约 360 个词/字。",
        "response": "先补证据和边界，再增加包体复杂度。"
      },
      {
        "name": "跨平台迁移风险",
        "impact": 3,
        "probability": 1,
        "signal": "agents/interface.yaml 已存在。",
        "response": "先补证据和边界，再增加包体复杂度。"
      }
    ],
    "risk_families": [
      {
        "key": "markdown_readability",
        "label": "Markdown readability",
        "matched_keywords": [
          "md",
          "report",
          "doc"
        ],
        "score": 3,
        "risks": [
          "Tables can render as dense grids with weak hierarchy or poor mobile readability.",
          "Long bullets can make the output look complete while hiding the actual decision logic.",
          "Mixed heading levels can reduce scanability."
        ],
        "constraints": [
          "Use tables only when comparison is the main job; otherwise prefer compact cards or grouped bullets.",
          "Keep table cells short and move explanations below the table.",
          "Use heading levels consistently and keep each section anchored to a user-facing outcome."
        ],
        "self_repair": [
          "Preview whether each table still reads well when columns are narrow.",
          "Convert any table with paragraph-length cells into bullets or cards."
        ]
      },
      {
        "key": "citation_clutter",
        "label": "Citation and footnote clutter",
        "matched_keywords": [
          "source",
          "reference"
        ],
        "score": 2,
        "risks": [
          "Footnote markers or dense citation notes can interrupt the reading flow.",
          "Evidence can be over-attached to obvious statements and under-attached to risky claims.",
          "Source notes may become more prominent than the tutorial itself."
        ],
        "constraints": [
          "Attach citations only to claims that need evidence, not to every sentence.",
          "Group source notes at the end of a section when inline markers would hurt readability.",
          "Keep the main sentence readable without requiring the reader to chase a footnote."
        ],
        "self_repair": [
          "Remove decorative citations that do not support a material claim.",
          "Move repeated source explanations into one compact source note."
        ]
      },
      {
        "key": "visual_capture",
        "label": "Screenshot and visual capture",
        "matched_keywords": [
          "capture"
        ],
        "score": 1,
        "risks": [
          "Screenshots can be captured from the wrong state, wrong viewport, or wrong crop.",
          "Missing screenshots can cause the skill to invent visual references instead of declaring the gap.",
          "Image descriptions can omit the action-relevant region."
        ],
        "constraints": [
          "Never invent a screenshot; state when visual evidence is missing.",
          "Record the source, viewport, and crop intent for any screenshot-dependent output.",
          "Describe what the reader should inspect in the image, not just that an image exists."
        ],
        "self_repair": [
          "Check that every screenshot reference points to a real provided or generated asset.",
          "Reword any visual instruction that depends on an unseen screen state."
        ]
      },
      {
        "key": "code_or_command_safety",
        "label": "Code and command safety",
        "matched_keywords": [
          "script"
        ],
        "score": 1,
        "risks": [
          "Commands can omit environment assumptions, working directory, or rollback notes.",
          "Code snippets can look runnable while missing required inputs.",
          "Error handling can be either absent or over-engineered."
        ],
        "constraints": [
          "Name the working directory, required inputs, and expected output for each command.",
          "Mark destructive or external side-effect operations explicitly.",
          "Prefer the smallest runnable snippet over broad framework code."
        ],
        "self_repair": [
          "Scan each command for cwd, input, output, and side-effect assumptions.",
          "Remove speculative error handling that is not tied to a real failure mode."
        ]
      },
      {
        "key": "tone_and_specificity",
        "label": "Tone and specificity",
        "matched_keywords": [
          "summary"
        ],
        "score": 1,
        "risks": [
          "Headings and summaries can drift into generic, interchangeable language.",
          "The output can sound polished but lose the user's actual taste, audience, or scenario.",
          "Strong claims can appear without examples or constraints."
        ],
        "constraints": [
          "Anchor titles and summaries in the user's audience, object, and concrete outcome.",
          "Avoid placeholder phrases such as comprehensive guide, ultimate solution, or key insights unless the source demands them.",
          "Preserve one distinctive phrase, constraint, or standard from the user's input."
        ],
        "self_repair": [
          "Replace generic title candidates with scenario-specific alternatives.",
          "Delete any polished sentence that could fit almost any project unchanged."
        ]
      }
    ],
    "human_judgment_boundary": [
      "Ask one focused clarification when the real job, output, or exclusion boundary is unclear.",
      "Escalate visible tradeoffs when benchmark patterns conflict with local privacy, naming, or governance constraints.",
      "Do not silently broaden the skill into adjacent jobs just because the examples are nearby."
    ]
  },
  "world_class_readiness": {
    "ready": false,
    "decision": "evidence-pending",
    "entry_count": 4,
    "pending_count": 4,
    "accepted_count": 0,
    "external_pending_count": 3,
    "human_pending_count": 1,
    "source_check_count": 13,
    "source_pass_count": 6,
    "conclusion_zh": "世界级证据尚未完成：4 项待补，0 项已接受。",
    "conclusion_en": "World-class evidence is not complete: 4 pending, 0 accepted.",
    "entries": [
      {
        "key": "provider-holdout",
        "label_zh": "提供商留出",
        "label_en": "Provider Holdout",
        "category": "external",
        "category_zh": "外部证据",
        "category_en": "External evidence",
        "status": "pending",
        "summary_zh": "缺少真实 provider 模型运行和 token metadata。",
        "summary_en": "Missing a real provider model run and token metadata.",
        "blocked_checks": [
          "Provider model run",
          "Token usage observed"
        ]
      },
      {
        "key": "human-adjudication",
        "label_zh": "人工盲评",
        "label_en": "Human Adjudication",
        "category": "human",
        "category_zh": "人工证据",
        "category_en": "Human evidence",
        "status": "pending",
        "summary_zh": "盲评 pair 仍待真实 reviewer 决策。",
        "summary_en": "Blind-review pairs still need real reviewer decisions.",
        "blocked_checks": [
          "No pending decisions",
          "Judgments complete"
        ]
      },
      {
        "key": "native-permission-enforcement",
        "label_zh": "原生权限",
        "label_en": "Native Permission",
        "category": "external",
        "category_zh": "外部证据",
        "category_en": "External evidence",
        "status": "pending",
        "summary_zh": "原生 runtime enforcement 仍待目标客户端或外部安装器证明。",
        "summary_en": "Native runtime enforcement still needs target-client or external-installer proof.",
        "blocked_checks": [
          "Native enforcement"
        ]
      },
      {
        "key": "native-client-telemetry",
        "label_zh": "原生遥测",
        "label_en": "Native Telemetry",
        "category": "external",
        "category_zh": "外部证据",
        "category_en": "External evidence",
        "status": "pending",
        "summary_zh": "真实外部客户端 metadata-only 事件仍未导入。",
        "summary_en": "Real external-client metadata-only events have not been imported yet.",
        "blocked_checks": [
          "External events",
          "Adoption sample"
        ]
      }
    ]
  },
  "package_assets": {
    "entries": [
      {
        "path": "SKILL.md",
        "label": "Skill entrypoint",
        "kind": "file",
        "file_count": 1
      },
      {
        "path": "README.md",
        "label": "Human-readable usage guide",
        "kind": "file",
        "file_count": 1
      },
      {
        "path": "agents/interface.yaml",
        "label": "Neutral interface metadata",
        "kind": "file",
        "file_count": 1
      },
      {
        "path": "manifest.json",
        "label": "Lifecycle and portability metadata",
        "kind": "file",
        "file_count": 1
      },
      {
        "path": "references",
        "label": "Extended guidance and reusable notes",
        "kind": "folder",
        "file_count": 35
      },
      {
        "path": "scripts",
        "label": "Deterministic helpers or local tooling",
        "kind": "folder",
        "file_count": 140
      },
      {
        "path": "evals",
        "label": "Trigger and quality checks",
        "kind": "folder",
        "file_count": 29
      },
      {
        "path": "reports",
        "label": "Generated evidence and overview artifacts",
        "kind": "folder",
        "file_count": 235
      }
    ],
    "file_count": 443,
    "folder_count": 4,
    "distribution": [
      {
        "label": "SKILL.md",
        "value": 1
      },
      {
        "label": "README.md",
        "value": 1
      },
      {
        "label": "agents/interface.yaml",
        "value": 1
      },
      {
        "label": "manifest.json",
        "value": 1
      },
      {
        "label": "references",
        "value": 35
      },
      {
        "label": "scripts",
        "value": 140
      },
      {
        "label": "evals",
        "value": 29
      },
      {
        "label": "reports",
        "value": 235
      }
    ]
  },
  "iteration_roadmap": {
    "items": [
      {
        "title": "补齐世界证据",
        "why": "世界级证据仍有 4 项待补；公开完成态 claim 必须继续保持阻塞。",
        "actions": [
          "补齐提供商留出证据：缺少真实 provider 模型运行和 token metadata。",
          "补齐人工盲评证据：盲评 pair 仍待真实 reviewer 决策。",
          "按 ledger 总量继续补齐 4 项待补证据（外部 3 项、人工 1 项；未展开 2 项），并保持 claim guard 为 pending 状态。"
        ],
        "unlocks": "全部外部/人工证据被 ledger 接受后，才能进入公开 world-class claim 复核。",
        "source": "world_class_evidence_ledger"
      },
      {
        "title": "Borrow one proven pattern on purpose",
        "why": "You already have public benchmark objects. The next gain is to choose one pattern intentionally instead of absorbing everything loosely.",
        "actions": [
          "Read the strongest pattern from obra/superpowers.",
          "Decide whether to borrow method, structure, execution, or portability, but only one of them first.",
          "Record what you will not borrow so the package stays light."
        ],
        "unlocks": "A cleaner package shape with less accidental over-design."
      },
      {
        "title": "Harden portability semantics",
        "why": "The skill already signals reuse across environments, so contract clarity matters early.",
        "actions": [
          "Confirm activation mode, execution context, and trust assumptions.",
          "Add or review degradation strategy for non-native targets.",
          "Package the skill once to verify adapter expectations."
        ],
        "unlocks": "Safer cross-environment reuse with less target drift."
      }
    ]
  },
  "report_contract": {
    "schema_version": "2.0",
    "html_report": "reports/skill-interpretation.html",
    "language": "zh-CN",
    "default_language": "zh-CN",
    "languages": [
      "zh-CN",
      "en"
    ],
    "layout": "kami-white-audit-v2",
    "nav_labels": [
      "技能概述",
      "总览指标",
      "能力画像",
      "原理结构",
      "契约边界",
      "质量评估",
      "风险治理",
      "包体资产",
      "迭代路线"
    ],
    "nav_labels_en": [
      "Overview",
      "Metrics",
      "Profile",
      "Principle",
      "Contract",
      "Quality",
      "Risk",
      "Assets",
      "Roadmap"
    ],
    "report_kind": "skill-interpretation",
    "canonical_overview_report": "reports/skill-overview.html",
    "json_report": "reports/skill-interpretation.json",
    "purpose": "Explain the generated skill's role, principles, usage scenarios, trigger contract, inputs, outputs, quality evidence, risks, assets, highlights, and next upgrade directions."
  },
  "name": "yao-meta-skill",
  "title": "Yao Meta Skill",
  "display_name": "Yao Meta Skill",
  "description": "Create, refactor, evaluate, and package agent skills from workflows, prompts, transcripts, docs, or notes. Use when asked to create a skill, turn a repeated process into a reusable skill, improve an existing skill, add evals, or package a skill for team reuse.",
  "logic_steps": [
    "Decide whether the request should become a skill and choose the lightest fit.",
    "Capture job, output, exclusions, constraints, and standards.",
    "Run reference scan: external benchmarks first, user references second, local fit third; surface only uncertainty or conflict.",
    "Write the `description` early and test route quality before expanding the package.",
    "Add output-risk, artifact-design, prompt-quality, and system-model reports only when they matter."
  ],
  "usage_steps": [
    "Use $yao-meta-skill to turn my workflow or notes into a reusable skill with lean structure, clear triggering, and the right evals.",
    "Use this skill when the request matches: Create, refactor, evaluate, and package agent skills from workflows, prompts, transcripts, docs, or notes. Use when asked to create a skill, turn a repeated process into a reusable skill, improve an existing skill, add evals, or package a skill for team reuse."
  ],
  "package_map": [
    {
      "path": "SKILL.md",
      "label": "Skill entrypoint",
      "kind": "file",
      "file_count": 1
    },
    {
      "path": "README.md",
      "label": "Human-readable usage guide",
      "kind": "file",
      "file_count": 1
    },
    {
      "path": "agents/interface.yaml",
      "label": "Neutral interface metadata",
      "kind": "file",
      "file_count": 1
    },
    {
      "path": "manifest.json",
      "label": "Lifecycle and portability metadata",
      "kind": "file",
      "file_count": 1
    },
    {
      "path": "references",
      "label": "Extended guidance and reusable notes",
      "kind": "folder",
      "file_count": 35
    },
    {
      "path": "scripts",
      "label": "Deterministic helpers or local tooling",
      "kind": "folder",
      "file_count": 140
    },
    {
      "path": "evals",
      "label": "Trigger and quality checks",
      "kind": "folder",
      "file_count": 29
    },
    {
      "path": "reports",
      "label": "Generated evidence and overview artifacts",
      "kind": "folder",
      "file_count": 235
    }
  ],
  "strengths": [
    "触发面保持精简，并锚定在 frontmatter description。",
    "已生成 Skill IR，核心语义可先于平台打包被审查和迁移。",
    "已生成目标编译报告，可审查 IR 到 OpenAI、Claude、generic 等目标契约的映射。",
    "已生成 Output Eval Lab scorecard，可比较 with-skill 与 baseline 输出质量。",
    "已生成 Output Execution Runs，可区分记录样本、命令执行和模型执行证据。",
    "已生成 Output Review Adjudication，可记录盲评决策、一致率和待评审项。"
  ],
  "scenarios": [
    "把重复流程沉淀为可复用的 agent skill。",
    "把分散提示词、对话记录或操作规范整理为稳定能力。",
    "团队复用前，需要明确触发边界、质量证据和维护责任。",
    "用户说出类似需求时：Use $yao-meta-skill to turn my workflow or notes into a reusable skill with lean structure, clear triggering, and the right evals."
  ],
  "trigger_contract": {
    "description": "Create, refactor, evaluate, and package agent skills from workflows, prompts, transcripts, docs, or notes. Use when asked to create a skill, turn a repeated process into a reusable skill, improve an existing skill, add evals, or package a skill for team reuse.",
    "activation": "manual",
    "execution": "inline",
    "shell": "bash"
  },
  "io_contract": {
    "inputs": [
      "rough workflow notes, SOPs, runbooks, prompts, transcripts, documents, or repeated task descriptions",
      "an existing skill directory that needs refactor, evaluation, packaging, or governance hardening",
      "target platform requirements such as OpenAI, Claude, generic Agent Skills, or team distribution",
      "benchmark references, local constraints, desired maturity tier, and review standards"
    ],
    "outputs": [
      "A working skill package with lean SKILL.md, aligned agents/interface.yaml, justified references, scripts only when useful, eval evidence, reports, packaging metadata, and clear next iteration recommendations.",
      "结构化 Skill 目录，共 8 类关键资产。"
    ]
  },
  "principles": [
    {
      "title": "意图澄清",
      "body": "Turn repeated workflows, prompts, transcripts, runbooks, documents, or existing skill packages into routeable, evaluable, packageable, and governable agent skills for personal, team, library, or governed reuse."
    },
    {
      "title": "边界路由",
      "body": "用 frontmatter description 决定是否触发，并写明相邻非目标。"
    },
    {
      "title": "资产分层",
      "body": "把入口、参考、脚本、评估和报告拆到各自目录，避免 SKILL.md 膨胀。"
    },
    {
      "title": "证据回路",
      "body": "Ask only the highest-leverage clarification before adding package weight."
    },
    {
      "title": "漂移观察",
      "body": "Add near-neighbor exclusions and route evals before expanding workflow steps."
    },
    {
      "title": "杠杆升级",
      "body": "Name the recurring job, expected input, output, and strongest non-goal in compact language."
    }
  ],
  "roadmap": [
    {
      "title": "补齐世界证据",
      "why": "世界级证据仍有 4 项待补；公开完成态 claim 必须继续保持阻塞。",
      "actions": [
        "补齐提供商留出证据：缺少真实 provider 模型运行和 token metadata。",
        "补齐人工盲评证据：盲评 pair 仍待真实 reviewer 决策。",
        "按 ledger 总量继续补齐 4 项待补证据（外部 3 项、人工 1 项；未展开 2 项），并保持 claim guard 为 pending 状态。"
      ],
      "unlocks": "全部外部/人工证据被 ledger 接受后，才能进入公开 world-class claim 复核。",
      "source": "world_class_evidence_ledger"
    },
    {
      "title": "Borrow one proven pattern on purpose",
      "why": "You already have public benchmark objects. The next gain is to choose one pattern intentionally instead of absorbing everything loosely.",
      "actions": [
        "Read the strongest pattern from obra/superpowers.",
        "Decide whether to borrow method, structure, execution, or portability, but only one of them first.",
        "Record what you will not borrow so the package stays light."
      ],
      "unlocks": "A cleaner package shape with less accidental over-design."
    },
    {
      "title": "Harden portability semantics",
      "why": "The skill already signals reuse across environments, so contract clarity matters early.",
      "actions": [
        "Confirm activation mode, execution context, and trust assumptions.",
        "Add or review degradation strategy for non-native targets.",
        "Package the skill once to verify adapter expectations."
      ],
      "unlocks": "Safer cross-environment reuse with less target drift."
    }
  ],
  "cards": [],
  "introduction": [
    "这份报告用于快速理解新生成 Skill 的定位、原理、触发边界和交付内容。",
    "先确认重复任务、真实输入形态和可交付输出，再决定是否继续加 references、scripts 或 evals。",
    "如果需求仍然模糊，优先回到 intent dialogue 收紧边界，再扩展包体结构。"
  ],
  "benchmark_highlights": [],
  "skill_ir": {
    "schema_version": "2.0.0",
    "source_path": "skill-ir/examples/yao-meta-skill.json",
    "target_count": 5,
    "trigger_samples": 8,
    "output_eval_cases": 3
  },
  "compiled_targets": {
    "ok": true,
    "schema_version": "1.0",
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      "pass_count": 5,
      "warn_count": 0,
      "block_count": 0,
      "failure_count": 0,
      "warning_count": 0
    },
    "targets": [
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        "degradation_strategy": "metadata-adapter",
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        "generated_files": [
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        ],
        "unsupported_features": [
          "client-native script permission prompts are represented as permission contract metadata"
        ],
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      },
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        "target": "claude",
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        "generated_files": [
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        "native_surface": "VS Code/Copilot Agent Skills project or user scope",
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        ],
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  "output_execution": {
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  "upgrade_check": {
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          "to": "pass",
          "direction": "improved"
        }
      ],
      "metadata_changes": [
        {
          "field": "description",
          "from": "Create, refactor, evaluate, and package agent skills from workflows, prompts, transcripts, docs, or notes.",
          "to": "Create, refactor, evaluate, and package agent skills from workflows, prompts, transcripts, docs, or notes. Use when asked to create a skill, turn a repeated process into a reusable skill, improve an existing skill, add evals, or package a skill for team reuse."
        }
      ],
      "checksum_changes": [
        {
          "field": "archive_sha256",
          "from": "",
          "to": "5802e5f522558cf77666ae59390a28845cf3affd22fa1267b6629891ddeba76b"
        },
        {
          "field": "package_sha256",
          "from": "0000000000000000000000000000000000000000000000000000000000000000",
          "to": "9cb9a19e774540e721b143a240baf41b41c32b9f10df532eb4a7d1cf7a2fe67b"
        }
      ]
    },
    "release_notes": [
      "Recommended version bump: minor.",
      "Added targets: agent-skills-compatible, vscode.",
      "Package or archive checksum changed; reviewers should verify package artifacts before release.",
      "Package verification evidence: reports/package_verification.md."
    ],
    "failures": [],
    "warnings": []
  },
  "adoption_drift": {
    "ok": true,
    "summary": {
      "event_count": 1,
      "adoption_sample_count": 0,
      "activation_count": 0,
      "accepted_count": 0,
      "edited_count": 0,
      "rejected_count": 0,
      "missed_count": 0,
      "failed_count": 0,
      "adoption_rate": 0,
      "missed_trigger_count": 0,
      "wrong_trigger_count": 0,
      "bad_output_count": 0,
      "script_error_count": 0,
      "missing_resource_count": 0,
      "review_overdue_count": 0,
      "risk_band": "low",
      "event_types": {
        "review_event": 1
      },
      "failure_types": {},
      "source_types": {
        "manual": 1
      },
      "command_counts": {}
    },
    "next_iteration_candidates": [],
    "privacy_contract": {
      "storage": "local-first",
      "event_scope": "metadata-only",
      "raw_content_allowed": false,
      "raw_event_log_packaged": false,
      "blocked_fields": [
        "content",
        "input",
        "inputs",
        "message",
        "messages",
        "note",
        "output",
        "outputs",
        "prompt",
        "raw",
        "text",
        "transcript"
      ]
    },
    "failures": []
  },
  "review_waivers": {
    "ok": true,
    "summary": {
      "waiver_count": 0,
      "active_count": 0,
      "expired_count": 0,
      "invalid_count": 0,
      "covered_gate_count": 0,
      "covered_gate_keys": [],
      "waiver_candidate_count": 2,
      "waiverable_open_count": 1,
      "non_waivable_count": 1
    },
    "policy": {
      "blocker_waivers_allowed": false,
      "minimum_reason_chars": 20,
      "expires_required": true,
      "review_studio_gate_keys": [
        "architecture-maintainability",
        "context-budget",
        "intent-canvas",
        "operations-loop",
        "output-lab",
        "permission-gates",
        "permission-runtime",
        "python-compat",
        "registry-audit",
        "release-notes",
        "review-waivers",
        "runtime-matrix",
        "skill-atlas",
        "trigger-lab",
        "trust-report",
        "world-class-evidence"
      ],
      "known_gate_keys": [
        "architecture-maintainability",
        "context-budget",
        "intent-canvas",
        "operations-loop",
        "output-lab",
        "permission-gates",
        "permission-runtime",
        "python-compat",
        "registry-audit",
        "release-notes",
        "runtime-matrix",
        "skill-atlas",
        "trigger-lab",
        "trust-report"
      ],
      "waiverable_gate_keys": [
        "architecture-maintainability",
        "context-budget",
        "intent-canvas",
        "operations-loop",
        "output-lab",
        "permission-gates",
        "permission-runtime",
        "python-compat",
        "registry-audit",
        "release-notes",
        "runtime-matrix",
        "skill-atlas",
        "trigger-lab",
        "trust-report"
      ],
      "non_waivable_gate_keys": [
        "review-waivers",
        "world-class-evidence"
      ]
    },
    "failures": [],
    "warnings": []
  },
  "review_annotations": {
    "ok": true,
    "summary": {
      "annotation_count": 0,
      "open_count": 0,
      "resolved_count": 0,
      "deferred_count": 0,
      "open_blocker_count": 0,
      "open_warning_count": 0,
      "linked_gate_count": 0,
      "target_missing_count": 0,
      "failure_count": 0
    },
    "annotations": [],
    "failures": []
  },
  "world_class_evidence_plan": {
    "ok": true,
    "summary": {
      "audit_decision": "continue-iteration",
      "world_class_ready": false,
      "audit_world_class_ready": false,
      "ready_to_claim_world_class": false,
      "ledger_completion_required": true,
      "evidence_requirement_count": 4,
      "task_count": 4,
      "human_task_count": 1,
      "external_task_count": 3,
      "review_task_count": 0,
      "decision": "collect-external-evidence"
    },
    "tasks": [
      {
        "key": "provider-holdout",
        "label": "Provider Holdout",
        "status": "external_required",
        "category": "external",
        "owner": "operator with provider credentials",
        "current": "model-executed 0; token-observed 0",
        "objective": "Collect at least one provider-backed output-eval holdout run with model, timing, and token metadata.",
        "runbook": [
          "Set OPENAI_API_KEY in the operator shell before running provider evidence; never commit or print the value.",
          "export YAO_OUTPUT_EVAL_MODEL=${YAO_OUTPUT_EVAL_MODEL:-gpt-4.1-mini}",
          "python3 scripts/yao.py output-exec --provider-runner openai --timeout-seconds 60",
          "python3 scripts/yao.py skill-os2-audit . --generated-at <YYYY-MM-DD>",
          "Copy evidence/world_class/templates/provider-holdout.intake.json to evidence/world_class/submissions/provider-holdout.json and fill only real evidence fields.",
          "python3 scripts/yao.py world-class-intake . --submissions-dir evidence/world_class/submissions"
        ],
        "success_checks": [
          "reports/output_execution_runs.json summary.model_executed_count > 0",
          "reports/output_execution_runs.json summary.timing_observed_count > 0",
          "reports/output_execution_runs.json summary.token_observed_count > 0",
          "reports/skill_os2_audit.json item provider-holdout status becomes pass"
        ],
        "evidence_artifacts": [
          "reports/output_execution_runs.json",
          "reports/output_execution_runs.md",
          "reports/skill_os2_audit.json",
          "evidence/world_class/intake.schema.json",
          "evidence/world_class/templates/provider-holdout.intake.json",
          "reports/world_class_evidence_intake.json",
          "reports/world_class_evidence_intake.md"
        ],
        "privacy_contract": [
          "Do not commit provider credentials or environment dumps.",
          "The output execution report records output hashes and aggregate run metadata, not raw provider prompts."
        ],
        "audit_next_action": "Run provider-backed holdout cases with real credentials and commit only aggregate evidence."
      },
      {
        "key": "human-adjudication",
        "label": "Human Adjudication",
        "status": "human_required",
        "category": "human",
        "owner": "human reviewer",
        "current": "0/5 decisions; pending 5",
        "objective": "Record real blind A/B reviewer decisions before claiming human output review completion.",
        "runbook": [
          "python3 scripts/yao.py output-review-kit --write-template",
          "Open reports/output_review_kit.md and choose A or B for each pair without opening the answer key.",
          "python3 scripts/adjudicate_output_review.py --write-template",
          "Record reviewer choices in a separate JSON, JSONL, or CSV decision source with case_id, winner_variant, confidence, and reason only.",
          "python3 scripts/yao.py output-review-import --input <reviewer-decisions.json> --run-adjudication",
          "python3 scripts/yao.py output-review",
          "python3 scripts/yao.py skill-os2-audit . --generated-at <YYYY-MM-DD>",
          "Copy evidence/world_class/templates/human-adjudication.intake.json to evidence/world_class/submissions/human-adjudication.json and fill only real evidence fields.",
          "python3 scripts/yao.py world-class-intake . --submissions-dir evidence/world_class/submissions"
        ],
        "success_checks": [
          "reports/output_review_adjudication.json summary.pending_count == 0",
          "reports/output_review_adjudication.json summary.judgment_count == summary.pair_count",
          "reports/output_review_adjudication.json summary.invalid_decision_count == 0",
          "reports/skill_os2_audit.json item human-adjudication status becomes pass"
        ],
        "evidence_artifacts": [
          "reports/output_blind_review_pack.md",
          "reports/output_review_kit.md",
          "reports/output_review_decisions.json",
          "reports/output_review_adjudication.json",
          "reports/output_review_adjudication.md",
          "scripts/import_output_review_decisions.py",
          "evidence/world_class/intake.schema.json",
          "evidence/world_class/templates/human-adjudication.intake.json",
          "reports/world_class_evidence_intake.json",
          "reports/world_class_evidence_intake.md"
        ],
        "privacy_contract": [
          "Reviewer decisions should not include raw user data or private customer detail.",
          "The decision importer rejects raw prompt, output, transcript, message, and answer-key fields.",
          "Keep the answer key separate until after decisions are recorded."
        ],
        "audit_next_action": "Record real A/B choices in the decision template, then regenerate adjudication."
      },
      {
        "key": "native-permission-enforcement",
        "label": "Native Permission Enforcement",
        "status": "external_required",
        "category": "external",
        "owner": "target client or installer integrator",
        "current": "native-enforced targets 0; installer-enforced targets 4",
        "objective": "Prove at least one real target client or external installer runtime guard enforces approved high-permission capabilities.",
        "runbook": [
          "Implement or connect a real target client or external installer runtime guard that blocks undeclared network, file_write, or subprocess capabilities.",
          "Update the generated target adapter only when the guard is actually enforced by that target.",
          "python3 scripts/yao.py package . --platform openai --platform claude --platform generic --platform vscode --output-dir dist --zip",
          "python3 scripts/yao.py install-simulate . --package-dir dist --install-root dist/install-simulation",
          "python3 scripts/yao.py runtime-permissions . --package-dir dist",
          "python3 scripts/yao.py skill-os2-audit . --generated-at <YYYY-MM-DD>",
          "Copy evidence/world_class/templates/native-permission-enforcement.intake.json to evidence/world_class/submissions/native-permission-enforcement.json and fill only real evidence fields.",
          "python3 scripts/yao.py world-class-intake . --submissions-dir evidence/world_class/submissions"
        ],
        "success_checks": [
          "reports/runtime_permission_probes.json summary.native_enforcement_count > 0",
          "reports/runtime_permission_probes.json summary.failure_count == 0",
          "reports/runtime_permission_probes.json summary.installer_enforcement_pass_count records local installer enforcement but does not replace native evidence",
          "reports/skill_os2_audit.json item native-permission-enforcement status becomes pass"
        ],
        "evidence_artifacts": [
          "dist/targets/*/adapter.json",
          "reports/runtime_permission_probes.json",
          "reports/runtime_permission_probes.md",
          "reports/install_simulation.json",
          "reports/install_simulation.md",
          "security/permission_policy.json",
          "evidence/world_class/intake.schema.json",
          "evidence/world_class/templates/native-permission-enforcement.intake.json",
          "reports/world_class_evidence_intake.json",
          "reports/world_class_evidence_intake.md"
        ],
        "privacy_contract": [
          "Do not mark native_enforcement true for metadata-only fallbacks.",
          "Keep residual risks visible for targets that still rely on operator enforcement."
        ],
        "audit_next_action": "Integrate a real target-client or external installer runtime guard before claiming native permission enforcement."
      },
      {
        "key": "native-client-telemetry",
        "label": "Native Client Telemetry",
        "status": "external_required",
        "category": "external",
        "owner": "Browser/Chrome/IDE/provider client integrator",
        "current": "external source events 0; adoption samples 0",
        "objective": "Import production metadata-only events from a real external client into the local drift loop.",
        "runbook": [
          "python3 scripts/telemetry_native_host.py . --write-launcher /tmp/yao-telemetry-host.sh --write-manifest /tmp/yao-telemetry-host.json --allowed-origin chrome-extension://<extension-id>/",
          "Install the generated native messaging manifest for the real client and send at least one accepted skill_activation or skill_output event.",
          "python3 scripts/yao.py telemetry-import . --input-jsonl .yao/telemetry_spool/external_events.jsonl",
          "python3 scripts/yao.py skill-atlas --workspace-root .",
          "python3 scripts/yao.py skill-os2-audit . --generated-at <YYYY-MM-DD>",
          "Copy evidence/world_class/templates/native-client-telemetry.intake.json to evidence/world_class/submissions/native-client-telemetry.json and fill only real evidence fields.",
          "python3 scripts/yao.py world-class-intake . --submissions-dir evidence/world_class/submissions"
        ],
        "success_checks": [
          "reports/adoption_drift_report.json summary.source_types.external > 0",
          "reports/adoption_drift_report.json summary.adoption_sample_count > 0",
          "reports/skill_os2_audit.json item native-client-telemetry status becomes pass"
        ],
        "evidence_artifacts": [
          "reports/adoption_drift_report.json",
          "reports/adoption_drift_report.md",
          "reports/telemetry_hook_recipes.json",
          "scripts/telemetry_native_host.py",
          "evidence/world_class/intake.schema.json",
          "evidence/world_class/templates/native-client-telemetry.intake.json",
          "reports/world_class_evidence_intake.json",
          "reports/world_class_evidence_intake.md"
        ],
        "privacy_contract": [
          "Telemetry must remain metadata-only and local-first.",
          "Do not package reports/telemetry_events.jsonl or any raw prompt, output, transcript, note, or message field."
        ],
        "audit_next_action": "Install a real client against the native host and import production metadata-only events."
      }
    ],
    "source_audit": {
      "json": "reports/skill_os2_audit.json",
      "markdown": "reports/skill_os2_audit.md",
      "open_gap_count": 4
    }
  },
  "world_class_evidence_ledger": {
    "ok": true,
    "summary": {
      "ledger_entry_count": 4,
      "source_accepted_count": 0,
      "accepted_count": 0,
      "pending_count": 4,
      "human_pending_count": 1,
      "external_pending_count": 3,
      "submitted_entry_count": 0,
      "reviewer_approved_submission_count": 0,
      "missing_submission_count": 4,
      "invalid_submission_count": 0,
      "source_check_count": 13,
      "source_pass_count": 6,
      "source_blocked_count": 7,
      "submitted_but_pending_count": 0,
      "source_accepted_without_valid_submission_count": 0,
      "overclaim_guard_active": true,
      "ready_to_claim_world_class": false,
      "decision": "evidence-pending"
    },
    "entries": [
      {
        "key": "provider-holdout",
        "label": "Provider Holdout",
        "category": "external",
        "owner": "operator with provider credentials",
        "status": "pending",
        "source_status": "external_required",
        "source_accepted": false,
        "current": "model-executed 0; token-observed 0",
        "objective": "Collect at least one provider-backed output-eval holdout run with model, timing, and token metadata.",
        "runbook": [
          "Set OPENAI_API_KEY in the operator shell before running provider evidence; never commit or print the value.",
          "export YAO_OUTPUT_EVAL_MODEL=${YAO_OUTPUT_EVAL_MODEL:-gpt-4.1-mini}",
          "python3 scripts/yao.py output-exec --provider-runner openai --timeout-seconds 60",
          "python3 scripts/yao.py skill-os2-audit . --generated-at <YYYY-MM-DD>",
          "Copy evidence/world_class/templates/provider-holdout.intake.json to evidence/world_class/submissions/provider-holdout.json and fill only real evidence fields.",
          "python3 scripts/yao.py world-class-intake . --submissions-dir evidence/world_class/submissions"
        ],
        "provenance_requirements": [
          "provider-backed model run",
          "observed timing",
          "observed token metadata"
        ],
        "success_checks": [
          "reports/output_execution_runs.json summary.model_executed_count > 0",
          "reports/output_execution_runs.json summary.timing_observed_count > 0",
          "reports/output_execution_runs.json summary.token_observed_count > 0",
          "reports/skill_os2_audit.json item provider-holdout status becomes pass"
        ],
        "evidence_artifacts": [
          "reports/output_execution_runs.json",
          "reports/output_execution_runs.md",
          "reports/skill_os2_audit.json",
          "evidence/world_class/intake.schema.json",
          "evidence/world_class/templates/provider-holdout.intake.json",
          "reports/world_class_evidence_intake.json",
          "reports/world_class_evidence_intake.md"
        ],
        "privacy_contract": [
          "Do not commit provider credentials or environment dumps.",
          "The output execution report records output hashes and aggregate run metadata, not raw provider prompts."
        ],
        "observed_state": {
          "model_executed_count": 0,
          "timing_observed_count": 10,
          "token_observed_count": 0,
          "accepted": false
        },
        "source_checklist": [
          {
            "evidence_key": "provider-holdout",
            "label": "Provider model run",
            "field": "model_executed_count",
            "expected": ">0",
            "actual": 0,
            "status": "blocked",
            "source_accepted": false,
            "next_action": "Run provider-backed output-exec with real credentials."
          },
          {
            "evidence_key": "provider-holdout",
            "label": "Timing observed",
            "field": "timing_observed_count",
            "expected": ">0",
            "actual": 10,
            "status": "pass",
            "source_accepted": false,
            "next_action": "Provider execution should record timing metadata."
          },
          {
            "evidence_key": "provider-holdout",
            "label": "Token usage observed",
            "field": "token_observed_count",
            "expected": ">0",
            "actual": 0,
            "status": "blocked",
            "source_accepted": false,
            "next_action": "Provider execution should return non-estimated token usage."
          }
        ],
        "source_check_count": 3,
        "source_pass_count": 1,
        "source_blocked_count": 2,
        "submission_state": {
          "status": "missing",
          "path": "evidence/world_class/submissions/provider-holdout.json",
          "artifact_ref_count": 0,
          "attested_real_evidence": false,
          "privacy_contract_satisfied": false,
          "ledger_reviewer_approved": false,
          "ledger_reviewer": "",
          "ledger_reviewed_at": "",
          "ledger_counts_as_completion": false
        },
        "anti_overclaim": {
          "planned_work_counts_as_evidence": false,
          "metadata_fallback_counts_as_native_enforcement": false,
          "pending_review_counts_as_human_decision": false,
          "local_command_runner_counts_as_provider_model": false
        },
        "next_action": "Run provider-backed holdout cases with real credentials and commit only aggregate evidence."
      },
      {
        "key": "human-adjudication",
        "label": "Human Adjudication",
        "category": "human",
        "owner": "human reviewer",
        "status": "pending",
        "source_status": "human_required",
        "source_accepted": false,
        "current": "0/5 decisions; pending 5",
        "objective": "Record real blind A/B reviewer decisions before claiming human output review completion.",
        "runbook": [
          "python3 scripts/yao.py output-review-kit --write-template",
          "Open reports/output_review_kit.md and choose A or B for each pair without opening the answer key.",
          "python3 scripts/adjudicate_output_review.py --write-template",
          "Record reviewer choices in a separate JSON, JSONL, or CSV decision source with case_id, winner_variant, confidence, and reason only.",
          "python3 scripts/yao.py output-review-import --input <reviewer-decisions.json> --run-adjudication",
          "python3 scripts/yao.py output-review",
          "python3 scripts/yao.py skill-os2-audit . --generated-at <YYYY-MM-DD>",
          "Copy evidence/world_class/templates/human-adjudication.intake.json to evidence/world_class/submissions/human-adjudication.json and fill only real evidence fields.",
          "python3 scripts/yao.py world-class-intake . --submissions-dir evidence/world_class/submissions"
        ],
        "provenance_requirements": [
          "real reviewer identity",
          "blind A/B decisions",
          "answer key unopened until decisions exist"
        ],
        "success_checks": [
          "reports/output_review_adjudication.json summary.pending_count == 0",
          "reports/output_review_adjudication.json summary.judgment_count == summary.pair_count",
          "reports/output_review_adjudication.json summary.invalid_decision_count == 0",
          "reports/skill_os2_audit.json item human-adjudication status becomes pass"
        ],
        "evidence_artifacts": [
          "reports/output_blind_review_pack.md",
          "reports/output_review_kit.md",
          "reports/output_review_decisions.json",
          "reports/output_review_adjudication.json",
          "reports/output_review_adjudication.md",
          "scripts/import_output_review_decisions.py",
          "evidence/world_class/intake.schema.json",
          "evidence/world_class/templates/human-adjudication.intake.json",
          "reports/world_class_evidence_intake.json",
          "reports/world_class_evidence_intake.md"
        ],
        "privacy_contract": [
          "Reviewer decisions should not include raw user data or private customer detail.",
          "The decision importer rejects raw prompt, output, transcript, message, and answer-key fields.",
          "Keep the answer key separate until after decisions are recorded."
        ],
        "observed_state": {
          "pair_count": 5,
          "judgment_count": 0,
          "pending_count": 5,
          "invalid_decision_count": 0,
          "answer_revealed_count": 0,
          "accepted": false
        },
        "source_checklist": [
          {
            "evidence_key": "human-adjudication",
            "label": "Review pairs exist",
            "field": "pair_count",
            "expected": ">0",
            "actual": 5,
            "status": "pass",
            "source_accepted": false,
            "next_action": "Generate the blind A/B review pack."
          },
          {
            "evidence_key": "human-adjudication",
            "label": "No pending decisions",
            "field": "pending_count",
            "expected": "==0",
            "actual": 5,
            "status": "blocked",
            "source_accepted": false,
            "next_action": "Record a reviewer choice for every pair."
          },
          {
            "evidence_key": "human-adjudication",
            "label": "Judgments complete",
            "field": "judgment_count",
            "expected": "==pair_count",
            "actual": 0,
            "status": "blocked",
            "source_accepted": false,
            "next_action": "Every pair needs one valid human judgment."
          },
          {
            "evidence_key": "human-adjudication",
            "label": "No invalid decisions",
            "field": "invalid_decision_count",
            "expected": "==0",
            "actual": 0,
            "status": "pass",
            "source_accepted": false,
            "next_action": "Fix malformed winner/confidence entries."
          }
        ],
        "source_check_count": 4,
        "source_pass_count": 2,
        "source_blocked_count": 2,
        "submission_state": {
          "status": "missing",
          "path": "evidence/world_class/submissions/human-adjudication.json",
          "artifact_ref_count": 0,
          "attested_real_evidence": false,
          "privacy_contract_satisfied": false,
          "ledger_reviewer_approved": false,
          "ledger_reviewer": "",
          "ledger_reviewed_at": "",
          "ledger_counts_as_completion": false
        },
        "anti_overclaim": {
          "planned_work_counts_as_evidence": false,
          "metadata_fallback_counts_as_native_enforcement": false,
          "pending_review_counts_as_human_decision": false,
          "local_command_runner_counts_as_provider_model": false
        },
        "next_action": "Record real A/B choices in the decision template, then regenerate adjudication."
      },
      {
        "key": "native-permission-enforcement",
        "label": "Native Permission Enforcement",
        "category": "external",
        "owner": "target client or installer integrator",
        "status": "pending",
        "source_status": "external_required",
        "source_accepted": false,
        "current": "native-enforced targets 0; installer-enforced targets 4",
        "objective": "Prove at least one real target client or external installer runtime guard enforces approved high-permission capabilities.",
        "runbook": [
          "Implement or connect a real target client or external installer runtime guard that blocks undeclared network, file_write, or subprocess capabilities.",
          "Update the generated target adapter only when the guard is actually enforced by that target.",
          "python3 scripts/yao.py package . --platform openai --platform claude --platform generic --platform vscode --output-dir dist --zip",
          "python3 scripts/yao.py install-simulate . --package-dir dist --install-root dist/install-simulation",
          "python3 scripts/yao.py runtime-permissions . --package-dir dist",
          "python3 scripts/yao.py skill-os2-audit . --generated-at <YYYY-MM-DD>",
          "Copy evidence/world_class/templates/native-permission-enforcement.intake.json to evidence/world_class/submissions/native-permission-enforcement.json and fill only real evidence fields.",
          "python3 scripts/yao.py world-class-intake . --submissions-dir evidence/world_class/submissions"
        ],
        "provenance_requirements": [
          "real target client or external installer runtime guard",
          "native enforcement flag or externally accepted guard proof",
          "residual risk retained for fallback targets"
        ],
        "success_checks": [
          "reports/runtime_permission_probes.json summary.native_enforcement_count > 0",
          "reports/runtime_permission_probes.json summary.failure_count == 0",
          "reports/runtime_permission_probes.json summary.installer_enforcement_pass_count records local installer enforcement but does not replace native evidence",
          "reports/skill_os2_audit.json item native-permission-enforcement status becomes pass"
        ],
        "evidence_artifacts": [
          "dist/targets/*/adapter.json",
          "reports/runtime_permission_probes.json",
          "reports/runtime_permission_probes.md",
          "reports/install_simulation.json",
          "reports/install_simulation.md",
          "security/permission_policy.json",
          "evidence/world_class/intake.schema.json",
          "evidence/world_class/templates/native-permission-enforcement.intake.json",
          "reports/world_class_evidence_intake.json",
          "reports/world_class_evidence_intake.md"
        ],
        "privacy_contract": [
          "Do not mark native_enforcement true for metadata-only fallbacks.",
          "Keep residual risks visible for targets that still rely on operator enforcement."
        ],
        "observed_state": {
          "native_enforcement_count": 0,
          "metadata_fallback_count": 4,
          "installer_enforcement_pass_count": 4,
          "installer_permission_failure_count": 0,
          "installer_enforcement_ready": true,
          "residual_risk_count": 4,
          "failure_count": 0,
          "accepted": false
        },
        "source_checklist": [
          {
            "evidence_key": "native-permission-enforcement",
            "label": "Native enforcement",
            "field": "native_enforcement_count",
            "expected": ">0",
            "actual": 0,
            "status": "blocked",
            "source_accepted": false,
            "next_action": "Collect real target-client or external runtime guard proof."
          },
          {
            "evidence_key": "native-permission-enforcement",
            "label": "Probe failures",
            "field": "failure_count",
            "expected": "==0",
            "actual": 0,
            "status": "pass",
            "source_accepted": false,
            "next_action": "Runtime permission probes must stay clean."
          },
          {
            "evidence_key": "native-permission-enforcement",
            "label": "Installer support",
            "field": "installer_enforcement_ready",
            "expected": "true",
            "actual": true,
            "status": "pass",
            "source_accepted": false,
            "next_action": "Installer enforcement is supporting evidence, not native proof."
          }
        ],
        "source_check_count": 3,
        "source_pass_count": 2,
        "source_blocked_count": 1,
        "submission_state": {
          "status": "missing",
          "path": "evidence/world_class/submissions/native-permission-enforcement.json",
          "artifact_ref_count": 0,
          "attested_real_evidence": false,
          "privacy_contract_satisfied": false,
          "ledger_reviewer_approved": false,
          "ledger_reviewer": "",
          "ledger_reviewed_at": "",
          "ledger_counts_as_completion": false
        },
        "anti_overclaim": {
          "planned_work_counts_as_evidence": false,
          "metadata_fallback_counts_as_native_enforcement": false,
          "pending_review_counts_as_human_decision": false,
          "local_command_runner_counts_as_provider_model": false
        },
        "next_action": "Integrate a real target-client or external installer runtime guard before claiming native permission enforcement."
      },
      {
        "key": "native-client-telemetry",
        "label": "Native Client Telemetry",
        "category": "external",
        "owner": "Browser/Chrome/IDE/provider client integrator",
        "status": "pending",
        "source_status": "external_required",
        "source_accepted": false,
        "current": "external source events 0; adoption samples 0",
        "objective": "Import production metadata-only events from a real external client into the local drift loop.",
        "runbook": [
          "python3 scripts/telemetry_native_host.py . --write-launcher /tmp/yao-telemetry-host.sh --write-manifest /tmp/yao-telemetry-host.json --allowed-origin chrome-extension://<extension-id>/",
          "Install the generated native messaging manifest for the real client and send at least one accepted skill_activation or skill_output event.",
          "python3 scripts/yao.py telemetry-import . --input-jsonl .yao/telemetry_spool/external_events.jsonl",
          "python3 scripts/yao.py skill-atlas --workspace-root .",
          "python3 scripts/yao.py skill-os2-audit . --generated-at <YYYY-MM-DD>",
          "Copy evidence/world_class/templates/native-client-telemetry.intake.json to evidence/world_class/submissions/native-client-telemetry.json and fill only real evidence fields.",
          "python3 scripts/yao.py world-class-intake . --submissions-dir evidence/world_class/submissions"
        ],
        "provenance_requirements": [
          "real external client source",
          "metadata-only event",
          "local-first import path"
        ],
        "success_checks": [
          "reports/adoption_drift_report.json summary.source_types.external > 0",
          "reports/adoption_drift_report.json summary.adoption_sample_count > 0",
          "reports/skill_os2_audit.json item native-client-telemetry status becomes pass"
        ],
        "evidence_artifacts": [
          "reports/adoption_drift_report.json",
          "reports/adoption_drift_report.md",
          "reports/telemetry_hook_recipes.json",
          "scripts/telemetry_native_host.py",
          "evidence/world_class/intake.schema.json",
          "evidence/world_class/templates/native-client-telemetry.intake.json",
          "reports/world_class_evidence_intake.json",
          "reports/world_class_evidence_intake.md"
        ],
        "privacy_contract": [
          "Telemetry must remain metadata-only and local-first.",
          "Do not package reports/telemetry_events.jsonl or any raw prompt, output, transcript, note, or message field."
        ],
        "observed_state": {
          "external_source_events": 0,
          "adoption_sample_count": 0,
          "raw_content_allowed": false,
          "risk_band": "low",
          "accepted": false
        },
        "source_checklist": [
          {
            "evidence_key": "native-client-telemetry",
            "label": "External events",
            "field": "external_source_events",
            "expected": ">0",
            "actual": 0,
            "status": "blocked",
            "source_accepted": false,
            "next_action": "Import at least one metadata-only event from a real client."
          },
          {
            "evidence_key": "native-client-telemetry",
            "label": "Adoption sample",
            "field": "adoption_sample_count",
            "expected": ">0",
            "actual": 0,
            "status": "blocked",
            "source_accepted": false,
            "next_action": "Telemetry must include adoption outcome evidence."
          },
          {
            "evidence_key": "native-client-telemetry",
            "label": "Raw content blocked",
            "field": "raw_content_allowed",
            "expected": "false",
            "actual": false,
            "status": "pass",
            "source_accepted": false,
            "next_action": "Telemetry must stay metadata-only."
          }
        ],
        "source_check_count": 3,
        "source_pass_count": 1,
        "source_blocked_count": 2,
        "submission_state": {
          "status": "missing",
          "path": "evidence/world_class/submissions/native-client-telemetry.json",
          "artifact_ref_count": 0,
          "attested_real_evidence": false,
          "privacy_contract_satisfied": false,
          "ledger_reviewer_approved": false,
          "ledger_reviewer": "",
          "ledger_reviewed_at": "",
          "ledger_counts_as_completion": false
        },
        "anti_overclaim": {
          "planned_work_counts_as_evidence": false,
          "metadata_fallback_counts_as_native_enforcement": false,
          "pending_review_counts_as_human_decision": false,
          "local_command_runner_counts_as_provider_model": false
        },
        "next_action": "Install a real client against the native host and import production metadata-only events."
      }
    ],
    "source_plan": {
      "json": "reports/world_class_evidence_plan.json",
      "markdown": "reports/world_class_evidence_plan.md",
      "task_count": 4,
      "evidence_requirement_count": 4
    }
  },
  "synthesis_highlights": [
    "Borrow progressive disclosure: keep the entrypoint lean and move depth into references or scripts.",
    "Borrow a review checkpoint wherever trust matters more than raw speed.",
    "Borrow the discipline of defining what the skill should not own before growing the package."
  ],
  "artifact_design": {
    "design_system": "metric editorial",
    "highlights": [
      "Execution-focused technical artifact with environment assumptions, copyable commands, expected outputs, and side effects made explicit.",
      "Name the working directory and required inputs for commands.",
      "Mark destructive, networked, or external side-effect operations.",
      "Prefer the smallest runnable snippet over broad framework scaffolding."
    ]
  },
  "prompt_quality": {
    "overall_quality_score": 89.0,
    "highlights": [
      "Primary prompt task family: Execution operation.",
      "Complexity: expert — multiple task families plus governance, evaluation, or expert-level constraints",
      "Completeness: 100/100.",
      "Clarity: 85/100."
    ]
  },
  "system_model": {
    "stability": {
      "score": 100,
      "band": "system-ready"
    },
    "highlights": [
      "Stability: system-ready (100/100).",
      "Owned job: Turn repeated workflows, prompts, transcripts, runbooks, documents, or existing skill packages into routeable, evaluable, packageable, and governable agent skills for personal, team, library, or governed reuse.",
      "Leverage: Tune the frontmatter description — Name the recurring job, expected input, output, and strongest non-goal in compact language.",
      "Leverage: Install output self-repair checks — Add only the checks that prevent recurring output mistakes."
    ]
  },
  "metadata": {
    "canonical_format": "agent-skills",
    "targets": [
      "openai",
      "claude",
      "generic",
      "vscode"
    ],
    "maturity_tier": "governed",
    "skill_archetype": "governed",
    "updated_at": "2026-03-31"
  },
  "interpretation_contract": {
    "schema_version": "2.0",
    "source_model": "skill-overview-v2",
    "source_model_reused": true,
    "overview_report": "reports/skill-overview.html",
    "html_report": "reports/skill-interpretation.html",
    "json_report": "reports/skill-interpretation.json",
    "default_language": "zh-CN",
    "languages": [
      "zh-CN",
      "en"
    ],
    "includes": [
      "skill role",
      "principles",
      "usage scenarios",
      "trigger contract",
      "inputs and outputs",
      "quality evidence",
      "risk governance",
      "package assets",
      "highlights",
      "upgrade roadmap"
    ]
  }
}