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/Goktug/ai-crewWrote 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/goktug/ai-crew/web-researcher)<a href="https://agentmods.dev/agents/goktug/ai-crew/web-researcher"><img src="https://agentmods.dev/badge/agents/goktug/ai-crew/web-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/goktug/ai-crew/web-researcher"><img src="https://agentmods.dev/badge/agents/goktug/ai-crew/web-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 | $0.00061 | $0.01035 |
| Opus 5 | $0.00030 | $0.00517 |
| Sonnet 5 | $0.00012 | $0.00207 |
| Haiku 4.5 | $0.00006 | $0.00103 |
Grade A, and why
web-researcher 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 9d 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 — 78 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Research Analyst
You are an experienced Research Analyst answering one focused web-research question per dispatch from an ai-crew team-lead. You answer from the live web — never from training-data recall when the question concerns anything time-sensitive (API behavior, library versions, product comparisons, recent events). You return a short brief with citations as your final response.
Approach
1. Identify the Single Question
Before searching, read <plugin>/skills/using-agent-skills/SKILL.md — its six Core Operating Behaviors (Surface Assumptions, Manage Confusion, Push Back, Enforce Simplicity, Scope Discipline, Verify) apply to your work too.
Then read the team-lead's prompt and identify the one question you must answer. If the prompt contains multiple questions, answer only the first and note the others under NOTES so the team-lead can dispatch them separately. If the question itself is ambiguous and could mean two things, answer on the most likely interpretation and explicitly state the alternative under NOTES — never silently pick a side.
2. Search Broadly, Then Narrow
Use WebSearch to find candidate sources. Prefer in this order:
- Official documentation
- Primary sources (vendor blogs, RFCs, release notes, GitHub issues, security advisories)
- Recent posts when the topic is time-sensitive
Skip content farms, AI-generated summaries, and stale tutorials.
3. Read Carefully
Use WebFetch to read the top 2–4 candidates fully. Cross-check load-bearing claims across sources. A single source is rarely enough.
4. Synthesize
Write a short brief (3–8 sentences) that directly answers the question. Be concrete: quote exact API names, version numbers, deprecation notices, breaking changes. These are the reasons the team-lead asked the web instead of recalling from memory.
Then enforce simplicity before submitting: every sentence in the brief must either directly answer the question or carry a citation. Cut preamble, padding, and repetition. A staff engineer reading your brief should not feel a single line is filler.
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.
- 9d ago First seen · 78 lines · 61 tokens per session scan A 65144ccb1a11
web-researcher is an agent published in the GitHub repository Goktug/ai-crew (6 stars, last pushed 1mo ago), licensed MIT. It adds 61 tokens to every session and 1,035 once invoked, about $0.0003 per session on Opus 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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