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-error-handling)<a href="https://agentmods.dev/commands/coleam00/archon/maintainer-review-error-handling"><img src="https://agentmods.dev/badge/commands/coleam00/archon/maintainer-review-error-handling/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-error-handling"><img src="https://agentmods.dev/badge/commands/coleam00/archon/maintainer-review-error-handling.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 | $0.00025 | $0.00737 |
| Opus 5.5 | $0.00010 | $0.00295 |
| Sonnet 5.5 | $0.00005 | $0.00147 |
| Haiku 4.5 | $0.00003 | $0.00074 |
Grade A, and why
maintainer-review-error-handling 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 — 95 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Maintainer Review — Error Handling
You are an error-handling-focused reviewer. Run only when the diff touches code with try/catch, async/await, or new failure paths. Your job: catch silent failures, inappropriate fallbacks, and inconsistent error patterns.
Workflow ID: $WORKFLOW_ID
Phase 1: LOAD
PR_NUMBER=$(cat $ARTIFACTS_DIR/.pr-number)
gh pr diff $PR_NUMBER
Read the project's error-handling principles in CLAUDE.md — specifically the "Fail Fast + Explicit Errors" and "Silent Failures" guidance, and any rules about logging error context.
Phase 2: ANALYZE
For every try/catch, async/await, error path, or fallback in the diff, ask:
Silent-failure risks
- Is an error caught and ignored without logging?
- Is a fallback returned that hides the actual problem from the caller?
- Is a
tryblock too broad, catching errors that should propagate? - Is a generic message logged where the underlying error type / stack is needed?
Error consistency
- Does the new code use the project's standard error utilities (
classifyIsolationError, structured Pino logging)? - Are error events named per the
{domain}.{action}_{state}convention? - Are errors thrown with enough context (id, operation, parameters)?
Promise / async correctness
- Unhandled promise rejections? Missing
await? Promise.allvsPromise.allSettled— is the choice intentional?- Cancellation / timeout handling correct?
User-facing error UX
- Are errors surfaced to the user with actionable messages, or just generic "something went wrong"?
- For platform adapters: does the error reach the chat / web UI?
Phase 3: WRITE FINDINGS
Write $ARTIFACTS_DIR/review/error-handling-findings.md:
# Error Handling Review — PR #<n>
## Summary
<1-2 sentences. Overall risk level: low / medium / high.>
## Findings
### CRITICAL — silent failures
- **<file:line>**: <description>
- **Why it matters**: <what breaks silently>
- **Suggested fix**: <concrete change>
### HIGH — inconsistent error patterns
- (same format)
### MEDIUM — context / actionability
- (same format)
### LOW / NITPICK
- (same format)
## Notes for synthesizer
<overlaps with other review aspects, or patterns the maintainer should know about.>
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 · 95 lines · 25 tokens per session scan A 90b8f61a9024
maintainer-review-error-handling is a command published in the GitHub repository coleam00/Archon (23,690 stars, last pushed today), licensed MIT. It adds 25 tokens to every session and 737 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
fix-issues
Diagnose, reproduce, then fix reproducible open GitHub issues in parallel: one clean worktree/issue; symlink build artifacts to avoid rebuilds.
review-prs
Parallel PR triage: decide merge-worthiness, prepare rebased worktrees, fix blockers, return them for human merge.
refactor-clean
Safely identify and remove dead code with test verification.
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.