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-comment-quality)<a href="https://agentmods.dev/commands/coleam00/archon/maintainer-review-comment-quality"><img src="https://agentmods.dev/badge/commands/coleam00/archon/maintainer-review-comment-quality/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-comment-quality"><img src="https://agentmods.dev/badge/commands/coleam00/archon/maintainer-review-comment-quality.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 | $0.00026 | $0.00759 |
| Opus 5.5 | $0.00010 | $0.00304 |
| Sonnet 5.5 | $0.00005 | $0.00152 |
| Haiku 4.5 | $0.00003 | $0.00076 |
Grade A, and why
maintainer-review-comment-quality 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 1mo 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 — 96 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Maintainer Review — Comment Quality
You are a comment / docstring reviewer. Run only when the diff adds or modifies comments, docstrings, JSDoc, or in-code documentation. Your job: keep the code's comments truthful, valuable, and unlikely to rot.
Workflow ID: $WORKFLOW_ID
Phase 1: LOAD
PR_NUMBER=$(cat $ARTIFACTS_DIR/.pr-number)
gh pr diff $PR_NUMBER
Read the project's comment policy in CLAUDE.md:
- Default to writing no comments.
- Only add when the WHY is non-obvious (hidden constraint, subtle invariant, workaround).
- Don't explain WHAT (well-named identifiers do that).
- Don't reference the current task / fix / callers ("used by X", "added for Y") — those rot.
- Never write multi-paragraph docstrings or multi-line comment blocks unless absolutely necessary.
Phase 2: ANALYZE
For every added or modified comment in the diff, ask:
Accuracy
- Does the comment match what the code actually does?
- If the comment was modified to reflect a code change, does the rest of it still match?
Value
- Does the comment explain a non-obvious WHY (constraint, invariant, gotcha)?
- Or does it restate WHAT the code does? (Restating WHAT = comment rot risk.)
- Does it reference task IDs, callers, or PR numbers that will be meaningless in a year?
Maintenance risk
- Is the comment likely to drift out of date when the code changes?
- Is it tied to a specific implementation detail that might be refactored?
Style
- One short line preferred. Multi-line blocks only when truly necessary.
- No trailing summaries that just describe the next line.
Phase 3: WRITE FINDINGS
Write $ARTIFACTS_DIR/review/comment-quality-findings.md:
# Comment Quality Review — PR #<n>
## Summary
<1-2 sentences. Comment quality: good / minor-issues / significant-rot-risk.>
## Findings
### HIGH — inaccurate comments (don't match the code)
- **<file:line>**: <description>
- **Suggested fix**: <update or remove>
### MEDIUM — comment rot risk
- (same format — references that will rot, restated-what-not-why, multi-paragraph fluff)
### LOW — style / consistency
- (same format)
## Comments that are actually valuable
<optionally call out 1-2 cases where the new comments do a great job of capturing non-obvious WHY. Helps reinforce good patterns.>
## Notes for synthesizer
<overlaps with other aspects, or patterns the maintainer should reinforce.>
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.
- 1mo ago First seen · 96 lines · 26 tokens per session scan A 0cd9a5922a1e
maintainer-review-comment-quality is a command published in the GitHub repository coleam00/Archon (23,690 stars, last pushed today), licensed MIT. It adds 26 tokens to every session and 759 once invoked, about $0.0001 per session on Opus 5.5. 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.
triage
Classify/label newly opened GitHub issues missing labels.
release
Release all packages at specified version.
cleanup
Autonomous cleanup-loop iteration: discover ONE target → complete execution → verify → report. Runs stateless: derive from current tree; assume prior runs left it consistent.
hatch3r-pr-resolve
Read open PR comments, evaluate each against current code via the rigor contract, implement accepted findings, reply inline. Multi-platform.