L’interface est localisée ; les présentations et documents des Skills sont traduits automatiquement par IA dans votre langue. Le code, les commandes, chemins et termes techniques restent en version originale, et le document de l’auteur fait foi.
Explorer les Skills
Productivité · SKILL.md

inspector

inspector

Run sentence-level traceability verification on a blog, then generate viewer.html with interactive evidence panel. No LLM needed — pure Python.

Independent GitHub snapshot · Automated classification · Original documentation governs

Documentation de la Skill

GitHub

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

Inspector

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.

Setup

  • PROJECT_DIR = first argument
  • Resolve SKILL_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.
  • Required files in PROJECT_DIR: index.html, analyst.json, detective.json, designer.json, editor.json

Step 1: Run verify.py

python3 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.

Step 2: Generate viewer.html

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.

Running both steps

python3 SKILL_DIR/scripts/verify.py PROJECT_DIR --log-errors
python3 SKILL_DIR/scripts/generate_viewer.py PROJECT_DIR

Output

  • 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.

Step 3: Log Recurring Errors (Optional)

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.

Skills enregistrées