markdown-browser

name	markdown-browser
description	Wrapper skill for OpenClaw web_fetch results. Use when you need MECE post-processing on fetched pages: policy decision from Content-Signal, privacy redaction, optional markdown normalization fallback, and stable output schema without re-implementing network fetch.

Markdown Browser Skills

This skill is an orchestration layer, not a replacement fetcher. It always keeps official web_fetch as the fetch source of truth.

MECE Architecture

1. Fetch layer (official, exclusive)

• Use OpenClaw web_fetch to retrieve the page.
• Do not call direct HTTP fetch inside this skill for normal operation.

2. Policy layer (these skills)

• Parse Content-Signal and compute policy_action.
• Current action focuses on ai-input semantics: allow_input, block_input, needs_review.

3. Privacy layer (these skills)

• Redact path/fragment/query values in output URL fields.
• Keep URL shape useful for debugging without leaking sensitive values.

4. Normalization layer (these skills)

• If contentType=text/markdown, keep content as-is.
• If contentType=text/html, convert with turndown as fallback enhancement.
• For other content types, pass through text.

Execution Order

1. Call official web_fetch.
2. Pass the result JSON into this wrapper.
3. Optionally pass Content-Signal and x-markdown-tokens header values if available.
4. Use the returned normalized object for downstream agent logic.

Wrapper Tool

process_web_fetch_result({ web_fetch_result, content_signal_header, markdown_tokens_header })

Input:

• web_fetch_result (required): JSON payload returned by OpenClaw web_fetch.
• content_signal_header (optional): raw Content-Signal header string.
• markdown_tokens_header (optional): raw x-markdown-tokens header value.

Output:

• content
• format (markdown | html-fallback | text)
• token_estimate (number | null)
• content_signal
• policy_action
• source_url (redacted)
• status_code
• fallback_used

CLI Usage

# Install runtime dependency once inside the skill directory
npm install --omit=dev

# 1) Obtain a web_fetch payload first (from OpenClaw runtime)
# 2) Save it as /tmp/web_fetch.json
# 3) Run wrapper post-processing
node browser.js \
  --input /tmp/web_fetch.json \
  --content-signal "ai-input=yes, search=yes, ai-train=no" \
  --markdown-tokens "1820"