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/iamironz/ai-config-bundleWrote 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/iamironz/ai-config-bundle/web-search-researcher)<a href="https://agentmods.dev/agents/iamironz/ai-config-bundle/web-search-researcher"><img src="https://agentmods.dev/badge/agents/iamironz/ai-config-bundle/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/iamironz/ai-config-bundle/web-search-researcher"><img src="https://agentmods.dev/badge/agents/iamironz/ai-config-bundle/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 | $0.00014 | $0.00950 |
| Opus 5 | $0.00007 | $0.00475 |
| Sonnet 5 | $0.00003 | $0.00190 |
| Haiku 4.5 | $0.00001 | $0.00095 |
Grade A, and why
web-search-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 12d 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.
This is a copy
84% identical to web-search-researcher — 36 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 115 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 tool is webfetch, which you use to discover and retrieve information based on user queries.
KB / RAG (Mandatory)
Before producing your findings, follow the KB operational loop in ai-kb/AGENTS.md
(prefer ck search for rule discovery; use ai-kb/rules/INDEX.md only as a fallback, then load the relevant rules).
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 API/Library Documentation:
- Search for official docs first: "[library name] official documentation [specific feature]"
- Look for changelog or release notes for version-specific information
- Find code examples in official repositories or trusted tutorials
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.
- 12d ago First seen · 115 lines · 14 tokens per session scan A c2b45852aa02
web-search-researcher is an agent published in the GitHub repository iamironz/ai-config-bundle (2 stars, last pushed 5mo ago), licensed MIT. It adds 14 tokens to every session and 950 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 84% identical to web-search-researcher, differing in 36 lines, and is treated as a copy.
Other agents, from other repositories
nauro-planner
Use to plan a non-trivial change before any code is written. Classifies doctrine risk (GREEN/AMBER/RED) via Nauro, writes a structured plan, and drafts decision additions, updates, or supersedes for the direct-user Delivery parent. Returns a plan and never writes project truth or edits files.
nauro-reviewer
Use to review a PR or diff. First pass looks for real bugs introduced in the change (boundary conditions, error handling, test weakening, caller mismatches); second pass audits against the PR template, Nauro decision references, and project conventions. Read-only. Flags actionable code issues and blocks on hard-rule…
nauro-tech-lead
Use to set or maintain project direction. Reads Nauro decisions, session transcripts, and PR diffs; judges architectural choices against active doctrine; and drafts complete decision additions, updates, or supersedes for the direct-user Delivery parent. Never writes project truth.
nauro-executor
Use to implement an exact approved plan. Has full source edit and test access. Runs lint and tests before declaring done. Commits locally, drafts the PR, and never pushes or opens it. Never writes project truth.
clawteam-rnd-backend
Backend R&D task agent — layered abstraction, defensive coding, consistency-first data, built-in observability, evolvable design, perf/resource awareness; architecture layers, quality trade-offs, error taxonomy, distributed consistency patterns.
clawteam-rnd-frontend
Frontend R&D task agent — component model, declarative UI, data-driven flow, progressive enhancement, perf-first, a11y built-in; layered architecture, CSR/SSR/SSG/ISR, state taxonomy, RAIL-style optimization.