Web Doc Resolver
Resolve queries or URLs into compact, LLM-ready markdown using a low-cost cascade. Prioritizes llms.txt for structured docs, uses web fetch/search tools for extraction. Use when you need to fetch documentation, resolve web URLs to markdown, search for technical content, or build context from web sources.
Install
$ agentstack add skill-d-o-hub-rust-self-learning-memory-web-doc-resolver ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
Verified badge
Passed review? Show it. Paste this badge into your README, it links to the public security report.
Links to your public security report.[](https://agentstack.voostack.com/security/report/skill-d-o-hub-rust-self-learning-memory-web-doc-resolver) Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
Web Documentation Resolver
Resolve query or URL inputs into compact, high-signal markdown using an intelligent cascade.
Platform Tool Mapping
| Platform | Fetch Tool | Search Tool | |----------|------------|-------------| | opencode | webfetch | websearch | | claude code | WebFetch (MCP) | WebSearch (MCP) | | blackbox | web_fetch | web_search | | Python | scripts/resolve.py (auto-detects) | scripts/resolve.py (auto-detects) |
Cascade Resolution Strategy
URL Inputs
- llms.txt: Probe
https://origin/llms.txtfirst (free, structured) - Fetch: Use platform fetch tool for markdown content
- Search fallback: Find cached/mirrored versions if fetch fails
Query Inputs
- Search first: Use platform search tool (fast, free)
- Fetch top results: Get markdown from promising results
Python Script
python scripts/resolve.py "https://docs.rust-lang.org/book/" # URL
python scripts/resolve.py "Rust async programming" # Query
python scripts/resolve.py "query" --json --max-chars 4000 # OptionsDirect Tool Usage
# opencode
webfetch https://example.com/llms.txt
webfetch --format markdown https://docs.rust-lang.org/book/
websearch "Rust book documentation"
# claude code (MCP)
WebFetch(url="https://example.com/llms.txt")
WebFetch(url="https://docs.rust-lang.org/book/")
WebSearch(query="Rust book documentation")
# blackbox
web_fetch(url="https://example.com/llms.txt", prompt="Extract all content")
web_search(query="Rust book documentation")Best Practices
- Check
/llms.txtfirst for structured documentation - Use specific queries: "tokio spawn vs spawn_blocking difference" > "tokio"
- Add year to queries for current info: "Rust async 2026"
- Prefer official docs; try mirrors if primary fails
Quality Indicators
Good: Code examples, API signatures, config samples, version info, clear structure Poor: Boilerplate, paywalls, login requirements, heavy ads
Error Handling
- Cascade fallback on provider failures
- Log errors for debugging
- Search fallback when direct fetch fails
Testing
cd .agents/skills/web-doc-resolver
python -m pytest tests/ -vFiles
scripts/resolve.py - Main implementation | tests/test_resolve.py - Unit tests | reference.md - Detailed reference
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: d-o-hub
- Source: d-o-hub/rust-self-learning-memory
- License: MIT
- Homepage: https://d-o-hub.github.io/rust-self-learning-memory/
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.