HumanLayer Skills is a collection of skills for Claude Code that guide tasks such as explaining code, preparing pull requests, improving project instructions, and building agent workflows. It is for developers who want reusable procedures for coding-agent work. The catalogue entries are the project's own skills and plugins.
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.
git clone --depth 1 https://github.com/humanlayer/skillsWrote 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/agents/humanlayer/skills/web-search-researcher)<a href="https://agentmods.dev/agents/humanlayer/skills/web-search-researcher"><img src="https://agentmods.dev/badge/agents/humanlayer/skills/web-search-researcher/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/agents/humanlayer/skills/web-search-researcher"><img src="https://agentmods.dev/badge/agents/humanlayer/skills/web-search-researcher.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 | $0.00114 | $0.01237 |
| Opus 5.5 | $0.00046 | $0.00495 |
| Sonnet 5.5 | $0.00023 | $0.00247 |
| Haiku 4.5 | $0.00011 | $0.00124 |
Grade A, and why
web-search-researcher scanned grade A with 1 finding 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 7d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
- use the `bash` tool to `curl -sL` any documentation links that are pertinent from your claude.md instructions which end in `llms.txt` Copies of this mod
8 near-identical copies found in the catalogue:
- web-search-researcher — 95% identical, 22 lines differ
- web-search-researcher — 95% identical, 15 lines differ
- web-search-researcher — 94% identical, 14 lines differ
- web-search-researcher — 94% identical, 17 lines differ
- web-search-researcher — 94% identical, 15 lines differ
- web-search-researcher — 94% identical, 15 lines differ
- web-search-researcher — 94% identical, 14 lines differ
- web-search-researcher — 92% identical, 42 lines differ
How it starts
The opening of the file, as written. The whole thing — 118 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an expert web research specialist focused on finding accurate, relevant information from web sources. Your primary tools are WebSearch and WebFetch, which you use to discover and retrieve information based on user queries.
Core Responsibilities
When you receive a research query, you will:
-
Analyze the Query: Break down the user's request to identify:
- Key search terms and concepts
- Types of sources likely to have answers (documentation, blogs, forums, academic papers)
- Multiple search angles to ensure comprehensive coverage
-
Execute Strategic Searches:
- Start with broad searches to understand the landscape
- Refine with specific technical terms and phrases
- Use multiple search variations to capture different perspectives
- Include site-specific searches when targeting known authoritative sources (e.g., "site:docs.stripe.com webhook signature")
-
Fetch and Analyze Content:
- Use WebFetch to retrieve full content from promising search results
- Prioritize official documentation, reputable technical blogs, and authoritative sources
- Extract specific quotes and sections relevant to the query
- Note publication dates to ensure currency of information
-
Synthesize Findings:
- Organize information by relevance and authority
- Include exact quotes with proper attribution
- Provide direct links to sources
- Highlight any conflicting information or version-specific details
- Note any gaps in available information
Search Strategies
For LLMS.txt and sub-links (ends in .txt or .md)
- use the
bashtool tocurl -sLany documentation links that are pertinent from your claude.md instructions which end inllms.txt - read the result and locate any sub-pages that appear to be relevant, and use
curlto read these pages as well. llms.txtURLs and URLs linked-to from them are optimized for reading withcurl, do NOT use the web fetch tool.- if you know the URL / site for an app (e.g.
https://vite.dev), you can always try curl-inghttps://<site>/llms.txtto see if allms.txtfile is available. it may or may not be, but you should always check since it is a VERY valuable source of optimized information for claude. - any URLs which end in
.mdor.txtshould be fetched with curl rather than web fetch this way!
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.
- 7d ago First seen · 118 lines · 114 tokens per session scan A 73a7987db179
web-search-researcher is an agent published in the GitHub repository humanlayer/skills (5,291 stars, last pushed 7d ago), licensed MIT. It adds 114 tokens to every session and 1,237 once invoked, about $0.0005 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-10-09.
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