#!/usr/bin/env python3 import html import math SCRIPT_INTERFACE = "internal-module" SCRIPT_INTERFACE_REASON = "Imported by render_skill_overview.py to render inline SVG report charts." BRAND = "#1B365D" BORDER = "#e8e6dc" SOFT = "#faf9f5" TEXT = "#141413" MUTED = "#504e49" def esc(value) -> str: return html.escape(str(value)) def figure(name: str, svg: str, caption: str) -> str: return ( f'
' f"{svg}" f"
{esc(caption)}
" "
" ) def render_radar(scorecard: dict) -> str: keys = ["completeness_score", "trigger_score", "evidence_score", "maintainability_score", "portability_score"] labels = [scorecard[key]["label"] for key in keys if key in scorecard] scores = [scorecard[key]["score"] for key in keys if key in scorecard] center = 150 radius = 92 rings = [] for pct in (0.25, 0.5, 0.75, 1.0): points = [] for i in range(len(scores)): angle = -math.pi / 2 + 2 * math.pi * i / len(scores) points.append(f"{center + radius * pct * math.cos(angle):.1f},{center + radius * pct * math.sin(angle):.1f}") rings.append(f'') data_points = [] label_nodes = [] for i, score in enumerate(scores): angle = -math.pi / 2 + 2 * math.pi * i / len(scores) data_radius = radius * score / 100 data_points.append(f"{center + data_radius * math.cos(angle):.1f},{center + data_radius * math.sin(angle):.1f}") lx = center + (radius + 32) * math.cos(angle) ly = center + (radius + 32) * math.sin(angle) label_nodes.append( f'{esc(labels[i])}' ) svg = ( '' f'评分雷达' + "".join(rings) + f'' + "".join(label_nodes) + "" ) return figure("radar", svg, "评分雷达展示结构完整度、触发边界、证据、维护和迁移的相对强弱。") def render_flow(summary: dict) -> str: labels = summary.get("flow", ["输入材料", "Skill 包体", "可复用能力"]) nodes = [] for index, label in enumerate(labels[:3]): x = 38 + index * 210 nodes.append( f'' f'{esc(label)}' ) svg = ( '' '交付流程' '' + "".join(nodes) + "" ) return figure("flow", svg, "交付流程把用户输入、生成的包体和可复用能力放在一条线上。") def render_matrix(profile: dict) -> str: matrix = profile.get("matrix", {}) x = 70 + matrix.get("execution_certainty", 60) * 3.8 y = 430 - matrix.get("knowledge_density", 60) * 3.2 svg = ( '' '能力矩阵' f'' f'' f'' '执行确定性' '知识密度' f'' f'{esc(profile.get("task_family", "Skill workflow"))}' "" ) return figure("matrix", svg, "能力矩阵说明这个 Skill 更偏知识密集还是执行确定。") def render_layers(principle: dict) -> str: layers = principle.get("layers", ["入口层", "参考层", "脚本层", "评估层", "报告层"]) blocks = [] for index, layer in enumerate(layers[:5]): y = 55 + index * 48 blocks.append( f'' f'{esc(layer)}' ) svg = ( '' '分层结构' + "".join(blocks) + "" ) return figure("layers", svg, "分层结构展示入口、参考、脚本、评估和报告如何各司其职。") def render_risk_heatmap(risk: dict) -> str: risks = risk.get("risks", []) cells = [] for impact in range(1, 4): for probability in range(1, 4): count = sum(1 for item in risks if item.get("impact") == impact and item.get("probability") == probability) color = ["#faf9f5", "#EEF2F7", "#D0DCE9", BRAND][min(3, count)] x = 80 + (probability - 1) * 86 y = 58 + (3 - impact) * 66 cells.append( f'' f'{count}' ) svg = ( '' '风险热力' + "".join(cells) + '发生概率' + '影响程度' + "" ) return figure("risk_heatmap", svg, "风险热力图用影响程度和发生概率标出当前治理重点。") def render_asset_donut(assets: dict) -> str: distribution = assets.get("distribution", [])[:6] total = sum(item.get("value", 1) for item in distribution) or 1 colors = [BRAND, "#2D5A8A", "#D0DCE9", "#E4ECF5", "#e8e6dc", "#504e49"] offset = 0 circles = [] labels = [] for index, item in enumerate(distribution): value = item.get("value", 1) dash = value / total * 100 circles.append( f'' ) offset += dash labels.append(f'{esc(item.get("label", "asset"))}') svg = ( '' '资产分布' + "".join(circles) + f'{assets.get("file_count", 0)}项' + "".join(labels) + "" ) return figure("asset_donut", svg, "资产分布图展示当前包体的文件和目录重心。") def render_timeline(roadmap: dict) -> str: items = roadmap.get("items", [])[:3] blocks = [] for index, item in enumerate(items): x = 60 + index * 190 title = str(item.get("title", "升级")) if len(title) > 18: title = title[:17] + "…" blocks.append( f'' f'下一步 {index + 1}' f'{esc(title)}' ) svg = ( '' '迭代时间' f'' + "".join(blocks) + "" ) return figure("timeline", svg, "迭代时间线把下一步升级收束成少数可执行动作。") def render_chart_set(model: dict) -> dict: return { "radar": render_radar(model.get("scorecard", {})), "flow": render_flow(model.get("skill_summary", {})), "matrix": render_matrix(model.get("capability_profile", {})), "layers": render_layers(model.get("principle_model", {})), "risk_heatmap": render_risk_heatmap(model.get("risk_governance", {})), "asset_donut": render_asset_donut(model.get("package_assets", {})), "timeline": render_timeline(model.get("iteration_roadmap", {})), }