菜单
Run sentence-level traceability verification on a blog, then generate viewer.html with interactive evidence panel. No LLM needed — pure Python.
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/QinghongLin/data2story-skill --skill inspector命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
Exhaustively profile a dataset and list ALL possible analyses — distributions, correlations, rankings, trends, group comparisons, anomalies. Reads detective.json for context. Runs after the Detective/Scout, before the Editor. Outputs analyst.json with ana_xx IDs and chart-ready data_tables.
Audit a generated Data2Story blog for build correctness across ALL modalities by ACTUALLY RENDERING it in a real headless browser (when available) — catching blank/0-width charts, broken/oversized media, desktop+mobile overflow, and dead interactions that source inspection cannot see — then having vision agents review the screenshots, playtesting every interactive, and walking the flagship capability contract. Plus VIEW every image for a wrong/AI-faked subject. Fixes layout in place; sends content-correctness problems back to the owning role. Use at Stage 6 after the Programmer assembles index.html and before the Critic; re-runs each revision round. Triggers: a built index.html exists, or you need to confirm charts/media/interactions actually render.
Design the MANDATORY cinematic scroll experience (every blog ships one): a full-bleed, scroll-driven background — primarily from the Scout's verified real imagery — plus the global motion choreography, so the narrative unfolds like a film as the reader scrolls. Owns the BACKGROUND layer + page-level motion (not the Designer's per-section visuals, not the Interaction centerpiece — it stages them inside the scroll). Outputs cinematographer.json (cin_xx scenes). There is no off path: a topic with no sourceable real imagery falls back to a generative-atmosphere or data-driven-spine background, never to a bare column.
基于 SOC 职业分类
正在显示 SKILL.md
| name | inspector |
| description | Run sentence-level traceability verification on a blog, then generate viewer.html with interactive evidence panel. No LLM needed — pure Python. |
| argument-hint | ["PROJECT_DIR"] |
| allowed-tools | Bash(*), Read, Write |
Your job is traceability verification. Parse the blog HTML, extract every visible sentence, link each back to its evidence in the role JSONs, and generate a self-contained viewer.html that lets readers inspect the evidence chain.
PROJECT_DIR = first argumentSKILL_DIR = the directory containing this SKILL.md (.../skills/data2story/inspector). Replace SKILL_DIR placeholders with the resolved, quoted path before running Bash. Do not hard-code machine-local paths.index.html, analyst.json, detective.json, designer.json, editor.jsonpython3 SKILL_DIR/scripts/verify.py PROJECT_DIR --log-errors
Produces PROJECT_DIR/inspector.json (sentence→evidence mapping). The output shape (format v3: stats, sentences, unused_ids) is in references/inspector_schema.json.
python3 SKILL_DIR/scripts/generate_viewer.py PROJECT_DIR
This reads index.html + inspector.json and produces viewer.html — a self-contained file that works on file:// (no server needed). You run the script; you do not reimplement it. How it works (tag → style → script ordering), its critical constraints (line-number search, lite JSON, ES5, no fetch), and the viewer UI behavior are documented in references/viewer_internals.json.
python3 SKILL_DIR/scripts/verify.py PROJECT_DIR --log-errors
python3 SKILL_DIR/scripts/generate_viewer.py PROJECT_DIR
PROJECT_DIR/inspector.json — full traceability data (with raw_evidence)PROJECT_DIR/viewer.html — self-contained interactive viewer (works on file://)Done when viewer.html opens directly in a browser (no server), shows the blog with a working 🔍 toggle, and every traced sentence has a visible ID linking to its evidence summary.
Running with --log-errors auto-updates known recurring-case metadata in skills/errors/ for patterns the script detects directly from HTML. Use manual logging only when you discover a new pattern the script does not know yet — and only for Critical/High-severity issues that represent patterns, not one-off mistakes. The full logging process, the error-case markdown template, and the common error types worth logging are in references/error_logging.json.