# System Model

Skill: `geo-content-brief-skill`

- Stability score: `77/100`
- Stability band: `stable-first-pass`
- Doctrine: Structure drives behavior: improve the boundary, feedback loops, drift watch, and leverage points before adding weight.

## System Boundary Map

- Owned job: 将 GEO 内容访谈、关键词笔记和渠道约束整理成可执行的中文内容简报 Skill，适用于团队复用、触发边界审查和后续质量评估。
- Output boundary: a reusable skill output
- Maturity assumption: `scaffold`
- Input boundary:
  - user-provided workflow notes, prompts, docs, or examples
- Non-goals:
  - one-off adjacent requests that do not match the recurring job
  - private local material that was not intentionally included
- 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.

## Feedback Loops

### Intent boundary loop

- Signal: Intent confidence score is 10/100.
- Response: Ask only the highest-leverage clarification before adding package weight.
- Evidence: reports/intent-confidence.md and reports/intent-dialogue.md

### Reference synthesis loop

- Signal: Benchmark patterns are useful only after they are abstracted into borrow and avoid guidance.
- Response: Borrow one pattern at a time and keep the rest as reviewer-visible evidence.
- Evidence: reports/reference-synthesis.md
- Current patterns:
  - Borrow progressive disclosure: keep the entrypoint lean and move depth into references or scripts.
  - Borrow a small hypothesis-test-learn loop so the first revision is evidence-backed.
  - Borrow the discipline of defining what the skill should not own before growing the package.
  - Do not let packaging or platform concerns swallow the core job boundary.
  - Do not create experimental overhead that exceeds the skill's real risk tier.

### Output quality loop

- Signal: Generated output may fail in recurring domain-specific ways.
- Response: Apply predicted output-risk families as self-repair checks before final output.
- Evidence: reports/output-risk-profile.md
- Current risk families:
  - Markdown readability
  - Screenshot and visual capture
  - Citation and footnote clutter
  - Tone and specificity
  - Tutorial quality

### Reviewer feedback loop

- Signal: Human review catches drift that static checks miss.
- Response: Capture lightweight feedback and turn repeated findings into gates or references.
- Evidence: reports/review-viewer.html and feedback records

### Lifecycle loop

- Signal: As reuse grows, the skill needs stronger gates, ownership, and regression evidence.
- Response: Promote only when the next gate improves reliability more than context cost.
- Evidence: manifest.json, reports/iteration-directions.md, and governance checks

## Delay And Drift Watch

### Trigger drift

- Watch signal: Users start invoking the skill for adjacent one-off or explanation-only requests.
- Countermeasure: Add near-neighbor exclusions and route evals before expanding workflow steps.
- Cadence: per trigger or description change

### Output drift

- Watch signal: Outputs remain valid but become generic, cluttered, or weakly aligned with the user's domain.
- Countermeasure: Refresh output-risk and artifact-design profiles, then add one self-repair check.
- Cadence: after the first 3-5 real uses
- Risk families:
  - Markdown readability
  - Screenshot and visual capture
  - Citation and footnote clutter
  - Tone and specificity
  - Tutorial quality

### Reference drift

- Watch signal: Borrowed benchmark patterns no longer fit the local job or add ceremony without payoff.
- Countermeasure: Re-run reference synthesis and keep only patterns that improve the current boundary.
- Cadence: per material benchmark or product assumption change

### Governance drift

- Watch signal: Skill usage becomes team-critical while ownership, review cadence, or rollback evidence stays informal.
- Countermeasure: Promote maturity tier and add reviewer-visible lifecycle evidence.
- Cadence: when reuse becomes real

## Failure Pattern Map

### Boundary failure

- Symptom: The skill handles nearby requests that were never part of the recurring job.
- Repair: Narrow the description and add explicit non-goals before adding more execution steps.

### Feedback gap

- Symptom: The skill has rules but no signal telling authors which rule should change after use.
- Repair: Turn repeated reviewer feedback into one eval, one reference note, or one self-repair check.

### Output degradation

- Symptom: The result is structurally correct but generic, cluttered, or weakly matched to the user's domain.
- Repair: Use output-risk families as pre-final checks.
- Current Risk Families:
  - Markdown readability
  - Screenshot and visual capture
  - Citation and footnote clutter
  - Tone and specificity
  - Tutorial quality

### Prompt-behavior mismatch

- Symptom: The role, task, and format are copied from a prompt instead of becoming stable skill behavior.
- Repair: Convert reusable role/task/format assumptions into workflow, reports, or references.
- Watch Axes:
  - Specificity

## Highest Leverage Moves

### 1. Clarify the real job boundary

- Why: Intent uncertainty creates downstream trigger, output, and governance errors.
- Move: Ask one focused question and update intent context before adding assets.

### 2. Tune the frontmatter description

- Why: The description is the highest-leverage routing surface.
- Move: Name the recurring job, expected input, output, and strongest non-goal in compact language.

### 3. Install output self-repair checks

- Why: The likely failure families are: Markdown readability, Screenshot and visual capture, Citation and footnote clutter.
- Move: Add only the checks that prevent recurring output mistakes.

### 4. Borrow one pattern, not a whole product

- Why: External references improve quality when reduced to structure, not copied as surface style.
- Move: Start from: Borrow progressive disclosure: keep the entrypoint lean and move depth into references or scripts.

## Reviewer Use

- Reviewer should ask whether the skill's structure will keep producing the desired behavior after repeated real use.
- Prefer changing the system boundary, feedback loop, or leverage point before adding more prose.
- If a problem repeats, convert it into a named failure pattern and one regression check.
