Setup & Installation
Or with the ClawHub CLI, for registry-managed skill folders outside a full OpenClaw workspace:
What This Skill Does
Post-processing wrapper for OpenClaw web_fetch results. Applies content policy decisions from Content-Signal headers, redacts sensitive path and query values from output URLs, and normalizes fetched content to markdown, HTML-fallback, or plain text. Outputs a stable schema for downstream agent logic without re-implementing network fetch.
Centralizes content policy, privacy redaction, and format normalization so individual agents don't each re-implement the same post-fetch logic.
When to use it
- Checking AI-input policy before feeding a fetched page into an LLM
- Stripping sensitive path and query values from URLs in agent logs
- Converting HTML responses to markdown for text-based agent pipelines
- Estimating token counts from fetched web content before processing
- Standardizing web fetch output schema across multiple agent tools
Example Workflow
Here's how your AI assistant might use this skill in practice.
User asks: fetch this documentation page and extract key points, respecting content policies
- 1Call OpenClaw web_fetch to retrieve the target page
- 2Pass the result JSON and Content-Signal header to process_web_fetch_result
- 3Check policy_action: proceed if 'allow_input', halt if 'block_input'
- 4Use the normalized content field (markdown or text) for downstream summarization
- 5Log source_url with sensitive values already redacted
Structured object with content, format, token_estimate, policy_action, and redacted source_url ready for agent consumption