markdown-browser
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.
Best use case
markdown-browser is best used when you need a repeatable AI agent workflow instead of a one-off prompt. It is especially useful for teams working in multi. 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.
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.
Users should expect a more consistent workflow output, faster repeated execution, and less time spent rewriting prompts from scratch.
Practical example
Example input
Use the "markdown-browser" skill to help with this workflow task. Context: 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.
Example output
A structured workflow result with clearer steps, more consistent formatting, and an output that is easier to reuse in the next run.
When to use this skill
- Use this skill when you want a reusable workflow rather than writing the same prompt again and again.
When not to use this skill
- Do not use this when you only need a one-off answer and do not need a reusable workflow.
- Do not use it if you cannot install or maintain the related files, repository context, or supporting tools.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/markdown-browser/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How markdown-browser Compares
| Feature / Agent | markdown-browser | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
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.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# 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
```bash
# 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"
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