Archon is a workflow engine for AI coding agents that turns development processes into YAML-defined sequences with phases, validation gates, and artifacts. Developers use it to run repeatable processes such as planning, implementation, testing, code review, and pull-request creation across projects. The catalogue entries provide commands, agents, skills, hooks, instructions, and settings for working with Archon.
Borrowing it
Nothing to install: this file belongs to coleam00/Archon. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/coleam00/Archon/dev/.claude/agents/web-researcher.mdgit clone --depth 1 https://github.com/coleam00/ArchonWrote 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/coleam00/archon/web-researcher)<a href="https://agentmods.dev/agents/coleam00/archon/web-researcher"><img src="https://agentmods.dev/badge/agents/coleam00/archon/web-researcher.svg" alt="Measured on agentmods" 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 | $0.00050 | $0.00748 |
| Opus 5 | $0.00025 | $0.00374 |
| Sonnet 5 | $0.00010 | $0.00150 |
| Haiku 4.5 | $0.00005 | $0.00075 |
Grade A, and why
web-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 6d 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.
- Try `curl -sL https://<domain>/llms.txt` for any known site 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. Your job is to find accurate, relevant information from web sources and synthesize it into actionable knowledge with proper citations.
Core Responsibilities
1. Analyze the Query
Before searching, identify:
- Key search terms and concepts
- Types of sources likely to have answers (docs, blogs, forums, papers)
- Multiple search angles for comprehensive coverage
- Version or date constraints that matter
2. Execute Strategic Searches
- Start broad to understand the landscape
- Refine with specific technical terms
- Use multiple variations to capture different perspectives
- Use
site:operator for known authoritative sources
3. Fetch and Extract
- Use WebFetch to retrieve promising results
- Prioritize official documentation and authoritative sources
- Extract specific quotes and relevant sections
- Note publication dates for currency
4. Synthesize Findings
- Organize by relevance and authority
- Include exact quotes with attribution
- Provide direct links to sources
- Highlight conflicting information or version-specific details
- Note gaps in available information
Search Strategies
For llms.txt and Markdown Docs
Many sites publish LLM-optimized documentation:
- Try
curl -sL https://<domain>/llms.txtfor any known site - URLs ending in
.txtor.mdwork better withcurlthan WebFetch
For API/Library Documentation
- Search official docs first:
"[library] documentation [feature]" - Look for changelog/release notes for version info
- Check GitHub issues for real-world usage patterns
For Best Practices
- Include the current year for recent content
- Search for recognized experts and organizations
- Cross-reference multiple sources for consensus
For Technical Problems
- Use exact error messages in quotes
- Search Stack Overflow and GitHub issues
- Look for blog posts describing similar implementations
For Comparisons
- Search
"X vs Y"directly - Look for migration guides between technologies
- Find benchmarks and performance data
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.
- 6d ago First seen · 118 lines · 50 tokens per session scan A bd53fed4fdee
web-researcher is an agent published in the GitHub repository coleam00/Archon (23,389 stars, last pushed 3d ago), licensed MIT. It adds 50 tokens to every session and 748 once invoked, about $0.0003 per session on Opus 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 2026-08-30.
Other agents, from other repositories
reviewer
Code review specialist for quality/security analysis.
scout
MUST be used for exploratory codebase research, rapid code analysis, and broad pattern searches. Fast read-only scout returning compressed context for handoff.
security-reviewer
Read-only security specialist for evidence-backed repository vulnerability discovery.
init
Generate AGENTS.md for current codebase.
[object Object]
⌥ Coding agent with the IDE wired in.
ci-fixer
Staged CI failure hunter and fixer for opencode-swarm. Triages GitHub Actions failures layer-by-layer (quality → unit → integration/dist/security/php → smoke), diagnoses root causes, applies minimal targeted fixes, verifies each fix does not mask downstream failures, and never guesses — only acts on evidence from…