website-to-skill-folder（网站到技能文件夹）

Website-to-Skill Pipeline

Runs scripts/pipeline.py to crawl a website and produce an installable skill folder. The script handles everything — no need to read it.

1. Locate the Skill

SKILL_DIR="$HOME/.agents/skills/website-to-skill-folder"
[ -d "$SKILL_DIR" ] || SKILL_DIR=$(find "$HOME/.agents/skills" "$HOME/.claude/skills" \
  -maxdepth 2 -type d -name "website-to-skill-folder" 2>/dev/null | head -1)
echo "Skill dir: $SKILL_DIR"

2. Pre-flight Checks

Run these in order before the pipeline. Fix anything missing before proceeding — do not skip.

Python (required: 3.8+)

python --version 2>&1 || python3 --version 2>&1

• If python is Python 2 or not found, use python3 for all commands below.
• If neither is found: tell the user to install Python 3.8+ and stop.

Python packages (one-time)

python -c "import requests, pydantic, tenacity; print('OK')" 2>&1

If ModuleNotFoundError: install and retry before proceeding.

pip install requests pydantic tenacity

Firecrawl API key

python -c "import os; print('set' if os.environ.get('FIRECRAWL_API_KEY') else 'missing')"

If missing: stop and ask the user for their Firecrawl API key AND the target website in one question.

Tell them: "What website do you want to convert, and what's your Firecrawl API key? Get a free key at https://firecrawl.dev — no credit card required."

Once they provide both, use them in steps 3 and 4 below.

3. Show Cost Estimate and Get Approval

How the pipeline handles URLs: The pipeline extracts the domain from any URL the user gives. A path like /blog or /docs/api is ignored — the full domain is always crawled. Do NOT suggest filtering by URL path, grep the map file, or try to limit to a subdirectory. If the user provides https://example.com/blog, treat it as crawling example.com. (Subdomains like blog.example.com ARE different domains and are treated as such.)

Always run --dry-run first to discover the actual page count (costs 1 map credit). Include --max-pages N in the dry-run if the user specified a limit.

FIRECRAWL_API_KEY="fc-key" python "$SKILL_DIR/scripts/pipeline.py" https://example.com --dry-run
# or with a user-specified limit:
FIRECRAWL_API_KEY="fc-key" python "$SKILL_DIR/scripts/pipeline.py" https://example.com --dry-run --max-pages 50

The dry-run output shows Total URLs (actual pages found) and the estimated cost. Read those numbers and present them to the user. Examples:

• No limit given: "The site has 782 pages. Estimated cost: ~3,911 credits. Shall I proceed, or would you like to set a page limit?"
• User said 50 pages, site has 782: "The site has 782 pages. With your limit of 50, I'll scrape the first 50. Cost: ~251 credits. Proceed?"
• User said 200 pages, site has 30: "The site only has 30 pages (fewer than your 200 limit). Cost: ~151 credits. Proceed?"

Only run the pipeline (Step 4) after the user approves.

4. Run the Pipeline

Inline both the skill path and the API key so the command is self-contained:

FIRECRAWL_API_KEY="fc-their_key_here" python "$HOME/.agents/skills/website-to-skill-folder/scripts/pipeline.py" https://example.com --yes

If the skill was installed to a different location (check Step 1 output), substitute that path:

FIRECRAWL_API_KEY="fc-their_key_here" python "$SKILL_DIR/scripts/pipeline.py" https://example.com --yes

IMPORTANT:

• Always include --yes when running from an AI agent. Without it, the pipeline prompts for interactive confirmation which will time out and cancel the run.
• Set a 10-minute timeout on the Bash call. The pipeline can take several minutes for large sites (scraping + API polling). Example: timeout: 600000 in tool params.

Options:

Troubleshooting & Recovery

Pipeline crashed or timed out mid-scrape? Just rerun the same command. The pipeline saves progress after each batch to _workspace/{domain}/state.json. Completed batches are skipped automatically — you only pay for the remaining pages.

Want to rebuild the skill folder without re-scraping? Use --skip-scrape. This reassembles from cached data with zero API calls — useful if you want to tweak --description or the template changed.

Pages seem stale or site was redesigned? Use --force-refresh to ignore all cached data and re-scrape everything from scratch.

Firecrawl rate limit errors (HTTP 429)? The pipeline retries automatically with exponential backoff (up to 5 attempts per request). If it still fails, wait a few minutes and rerun — cached batches won't be repeated.

"API key missing" or authentication errors? Double-check the key starts with fc- and is set correctly. Get a free key at https://firecrawl.dev.

5. Install the Output Skill

When the pipeline finishes, it prints the exact install command with the real absolute path to the skill folder it just built. It looks like this (path will differ on your machine):

  Install / update skill in agents:

    Claude Code:
    npx skills add "/Users/yourname/path/to/output/example-com-website-search-skill" -g -y -a claude-code

    All agents:
    npx skills add "/Users/yourname/path/to/output/example-com-website-search-skill" -g -y

Before running: Show the user the command printed by the pipeline and ask:

"The skill folder is ready. Shall I install it now so your agents can search [domain] offline?"

Only run the install command after the user confirms. Use the exact path from the pipeline output — do not use the example path shown above.

If npx fails

npx skills requires Node.js. Check:

node --version 2>&1

If not found: tell the user "Node.js is required to install the skill. Download it from https://nodejs.org (LTS version) — it includes npx." Once they install it, re-run the install command.

After installing, the user's agents can answer questions about the website offline. Re-run the pipeline and re-run the install command any time to pick up new pages.

Cost

1 Firecrawl credit (map) + ~5 credits per page scraped. Example: 100-page site ≈ 501 credits. Incremental re-runs only pay for new or changed pages.