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.
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/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/commands/coleam00/archon/maintainer-review-synthesize)<a href="https://agentmods.dev/commands/coleam00/archon/maintainer-review-synthesize"><img src="https://agentmods.dev/badge/commands/coleam00/archon/maintainer-review-synthesize/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/commands/coleam00/archon/maintainer-review-synthesize"><img src="https://agentmods.dev/badge/commands/coleam00/archon/maintainer-review-synthesize.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 | $0.00020 | $0.01089 |
| Opus 5 | $0.00010 | $0.00544 |
| Sonnet 5 | $0.00004 | $0.00218 |
| Haiku 4.5 | $0.00002 | $0.00109 |
Grade A, and why
maintainer-review-synthesize 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.
How it starts
The opening of the file, as written. The whole thing — 147 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Maintainer Review — Synthesize
You are the synthesizer. Read all available review-aspect findings, deduplicate overlap, prioritize, and produce a single maintainer-ready review summary plus a draft GitHub comment.
Workflow ID: $WORKFLOW_ID
Phase 1: LOAD
PR number
PR_NUMBER=$(cat $ARTIFACTS_DIR/.pr-number)
Read every available review findings file
ls $ARTIFACTS_DIR/review/
Then read each one:
code-review-findings.md(always present if review branch ran)error-handling-findings.md(present if classifier said yes)test-coverage-findings.md(present if classifier said yes)comment-quality-findings.md(present if classifier said yes)docs-impact-findings.md(present if classifier said yes)
Some files may be missing — that's expected. Don't error.
Phase 2: AGGREGATE + DEDUPLICATE
Issues often surface in multiple aspects (e.g. a missing test for an error path shows up in error-handling AND test-coverage). Don't list the same finding twice. Pick the most actionable wording and merge.
Group findings by severity across all aspects, not by aspect:
- CRITICAL (across aspects): merge / blocking / data-loss / silent-failure issues.
- HIGH: real bugs, missing test for a fix, missing docs for a new public surface, CLAUDE.md violation.
- MEDIUM: edge cases, comment rot risks, minor docs polish.
- LOW / NITPICK: style, naming, optional improvements.
Within each tier, order by file path so the maintainer can scan top-to-bottom.
Phase 3: WRITE THE SYNTHESIS
Write $ARTIFACTS_DIR/review/synthesis.md:
# Maintainer Review — PR #<n>
## Verdict
<one of: ready-to-merge | minor-fixes-needed | blocking-issues>
## Summary
<2-3 sentence overview. What the PR does, what's good, what's blocking.>
## Findings
### CRITICAL (N)
- **<file:line>**: <description>
- From: <which aspect(s) flagged this>
- **Suggested fix**: <concrete change>
### HIGH (N)
- (same format)
### MEDIUM (N)
- (same format)
### LOW / NITPICK (N)
- (consolidated)
## CLAUDE.md compliance
<bullet list of any violations carried forward from code-review.>
## Aspects run
- code-review: <yes/no, summary line>
- error-handling: <yes/no, summary line>
- test-coverage: <yes/no, summary line>
- comment-quality: <yes/no, summary line>
- docs-impact: <yes/no, summary line>
## Aspects skipped
<list with reason — e.g. "test-coverage skipped: no source code changes detected by review-classify".>
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 · 147 lines · 20 tokens per session scan A c1a63f4bc15a
maintainer-review-synthesize is a command published in the GitHub repository coleam00/Archon (23,434 stars, last pushed today), licensed MIT. It adds 20 tokens to every session and 1,089 once invoked, about $0.0001 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 2026-08-30.
Other commands, from other repositories
review-prs
Parallel PR triage: decide merge-worthiness, prepare rebased worktrees, fix blockers, return them for human merge.
fix-issues
Diagnose, reproduce, then fix reproducible open GitHub issues in parallel: one clean worktree/issue; symlink build artifacts to avoid rebuilds.
cleanup
Autonomous cleanup-loop iteration: discover ONE target → complete execution → verify → report. Runs stateless: derive from current tree; assume prior runs left it consistent.
review
Run code review on files or recent changes.
hatch3r-pr-resolve
Read open PR comments, evaluate each against current code via the rigor contract, implement accepted findings, reply inline. Multi-platform.
factory-ticket
Implement exactly one already-claimed Linear ticket in the current worktree.