Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add d-o-hub/github-template-ai-agents --skill do-web-doc-resolvergit clone --depth 1 https://github.com/d-o-hub/github-template-ai-agentsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/d-o-hub/github-template-ai-agents/do-web-doc-resolver)<a href="https://agentmods.dev/skills/d-o-hub/github-template-ai-agents/do-web-doc-resolver"><img src="https://agentmods.dev/badge/skills/d-o-hub/github-template-ai-agents/do-web-doc-resolver/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/d-o-hub/github-template-ai-agents/do-web-doc-resolver"><img src="https://agentmods.dev/badge/skills/d-o-hub/github-template-ai-agents/do-web-doc-resolver.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00094 | $0.01715 |
| Opus 5.5 | $0.00038 | $0.00686 |
| Sonnet 5.5 | $0.00019 | $0.00343 |
| Haiku 4.5 | $0.00009 | $0.00171 |
Grade A, and why
do-web-doc-resolver scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 152 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Web Doc Resolver Skill
Resolve URLs and queries into compact, LLM-ready markdown using a progressive, free-first cascade with 2026 production patterns.
When to Use
- Fetch and parse documentation from a URL
- Search for technical information across the web
- Build context from web sources for AI agents
- Validate content against agent-docs-spec v0.3.0
- Trace resolution trajectories for debugging
Prerequisites
- Python 3.10+ in an isolated virtual environment.
- From the skill directory, run
python -m pip install .to install the base package and dependencies declared inpyproject.tomlbefore the quick start. - Install provider, ML, or database extras only for the features that need them; text resolution does not require the optional visual/ML stack.
Quick Start
python -m scripts.cli "https://docs.rust-lang.org/book/"
python -m scripts.cli "Rust async programming" --profile quality --json
Cascade Strategy
URL: Cache → llms.txt → Jina → Direct Fetch → Firecrawl → Mistral Browser → DuckDuckGo
Query: Cache → Exa MCP → Exa SDK → Tavily → Serper → DuckDuckGo → Mistral Web Search
Providers (12 total)
| Provider | Type | Free | Latency |
|---|---|---|---|
llms_txt |
URL | Yes | ~100ms |
jina |
URL | Yes | ~300-1500ms |
exa_mcp |
Query | Yes | Varies |
duckduckgo |
Query | Yes | ~2s |
direct_fetch |
URL | Yes | ~500ms |
exa, tavily, serper |
Query | No | ~1s |
firecrawl, mistral_* |
URL/Query | No | ~2-3s |
docling, ocr |
URL | No | ~3-5s |
Execution Profiles
| Profile | Attempts | Paid | Latency | Quality |
|---|---|---|---|---|
free |
3 | 0 | 6s | 0.70 |
fast |
2 | 1 | 4s | 0.60 |
balanced |
4-6 | 1-2 | 9-12s | 0.65 |
quality |
6-10 | 3-5 | 15-20s | 0.55 |
Key Features
Structured Tool Contracts: Every provider returns ProviderResult(ok, content, error, meta) with ProviderMeta(tool, duration_ms, cache_hit, quality_score, error_type).
What ships with it
60 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- __init__.py 197 B runs code
- __main__.py 68 B runs code
- .env.example 252 B
- .gitignore 131 B
- AGENTS.md 421 B
- evals/evals.json 1.9 KB
- pyproject.toml 3.9 KB
- references/CASCADE.md 6.6 KB
- references/CLI.md 5.1 KB
- references/CONFIG.md 4.9 KB
- references/PROVIDERS.md 5.2 KB
- references/RUST_CLI.md 7.1 KB
- references/TESTING.md 7.0 KB
- references/web-research-optimization.md 12 KB
- requirements.txt 275 B
- scripts/__init__.py 0 B runs code
- scripts/_cascade_async.py 8.1 KB runs code
- scripts/_cascade.py 8.1 KB runs code
- scripts/_query_resolve.py 4.5 KB runs code
- scripts/_routing_utils.py 364 B runs code
- scripts/_url_resolve_async.py 6.9 KB runs code
- scripts/_url_resolve.py 6.7 KB runs code
- scripts/cache_negative.py 2.1 KB runs code
- scripts/capture/capture-flow.sh 2.2 KB runs code
- scripts/capture/capture-release.sh 1.6 KB runs code
- scripts/capture/capture-responsive.sh 1.1 KB runs code
- scripts/circuit_breaker.py 2.4 KB runs code
- scripts/cli.py 3.9 KB runs code
- scripts/constants.py 4.2 KB runs code
- scripts/diagnose_providers.py 4.3 KB runs code
- scripts/docs_validation.py 7.9 KB runs code
- scripts/models.py 5.5 KB runs code
- scripts/provider_decorator.py 3.1 KB runs code
- scripts/providers_impl.py 1.3 KB runs code
- scripts/providers/__init__.py 2.0 KB runs code
- scripts/providers/docling.py 3.4 KB runs code
- scripts/providers/duckduckgo.py 2.6 KB runs code
- scripts/providers/exa.py 8.2 KB runs code
- scripts/providers/firecrawl.py 4.2 KB runs code
- scripts/providers/jina.py 3.8 KB runs code
- scripts/providers/mistral.py 12 KB runs code
- scripts/providers/serper.py 5.1 KB runs code
- scripts/providers/stealth.py 945 B runs code
- scripts/providers/tavily.py 3.8 KB runs code
- scripts/providers/visual_clip.py 3.7 KB runs code
- scripts/quality.py 5.6 KB runs code
- scripts/resolve.py 7.8 KB runs code
- scripts/routing_memory.py 7.0 KB runs code
- scripts/routing.py 7.4 KB runs code
- scripts/semantic_cache.py 16 KB runs code
- scripts/state.py 1.0 KB runs code
- scripts/synthesis.py 9.1 KB runs code
- scripts/utils/__init__.py 4.2 KB runs code
- scripts/utils/async_http.py 9.4 KB runs code
- scripts/utils/cache.py 3.4 KB runs code
- scripts/utils/content_clean.py 2.5 KB runs code
- scripts/utils/fetch.py 3.7 KB runs code
- scripts/utils/html.py 4.1 KB runs code
- scripts/utils/http.py 10 KB runs code
- scripts/utils/thread_pool.py 1.0 KB runs code
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 4d ago Changed · +5 lines 94244da4770f
- 13d ago Changed · +8 lines d9651fd75649
- 1mo ago First seen · 139 lines · 94 tokens per session scan A cc5a2a8edd3a
do-web-doc-resolver is a skill published in the GitHub repository d-o-hub/github-template-ai-agents (2 stars, last pushed yesterday), licensed MIT. It adds 94 tokens to every session and 1,715 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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