Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Wrapper skill for OpenClaw web_fetch results. Use when you need MECE post-processing on fetched pages: policy decision from Content-Signal, privacy redaction...
Wrapper skill for OpenClaw web_fetch results. Use when you need MECE post-processing on fetched pages: policy decision from Content-Signal, privacy redaction...
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Then review README.md for any prerequisites, environment setup, or post-install checks. Tell me what you changed and call out any manual steps you could not complete.
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Then review README.md for any prerequisites, environment setup, or post-install checks. Summarize what changed and any follow-up checks I should run.
This skill is an orchestration layer, not a replacement fetcher. It always keeps official web_fetch as the fetch source of truth.
Fetch layer (official, exclusive) Use OpenClaw web_fetch to retrieve the page. Do not call direct HTTP fetch inside this skill for normal operation. Policy layer (these skills) Parse Content-Signal and compute policy_action. Current action focuses on ai-input semantics: allow_input, block_input, needs_review. Privacy layer (these skills) Redact path/fragment/query values in output URL fields. Keep URL shape useful for debugging without leaking sensitive values. 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.
Call official web_fetch. Pass the result JSON into this wrapper. Optionally pass Content-Signal and x-markdown-tokens header values if available. Use the returned normalized object for downstream agent logic.
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
# 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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