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.
npx agentmods add commands/coleam00/archon/system-reviewgit 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/system-review)<a href="https://agentmods.dev/commands/coleam00/archon/system-review"><img src="https://agentmods.dev/badge/commands/coleam00/archon/system-review.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 | $0.00007 | $0.01158 |
| Opus 5 | $0.00003 | $0.00579 |
| Sonnet 5 | $0.00001 | $0.00232 |
| Haiku 4.5 | $0.00001 | $0.00116 |
Grade A, and why
system-review 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 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.
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.
Copies of this mod
1 near-identical copy found in the catalogue:
- system-review — 88% identical, 25 lines differ
How it starts
The opening of the file, as written. The whole thing — 192 lines — stays where its author put it; the contents beside it link to each section on GitHub.
System Review
Perform a meta-level analysis of how well the implementation followed the plan and identify process improvements.
Purpose
System review is NOT code review. You're not looking for bugs in the code - you're looking for bugs in the process.
Your job:
- Analyze plan adherence and divergence patterns
- Identify which divergences were justified vs problematic
- Surface process improvements that prevent future issues
- Suggest updates to Layer 1 assets (CLAUDE.md, plan templates, commands)
Philosophy:
- Good divergence reveals plan limitations → improve planning
- Bad divergence reveals unclear requirements → improve communication
- Repeated issues reveal missing automation → create commands
Context & Inputs
You will analyze four key artifacts:
Plan Command:
Read this to understand the planning process:
.claude/commands/plan-feature.md
Generated Plan: Read this to understand what the agent was SUPPOSED to do: Plan file: $1
Execute Command:
Read this to understand the execution process:
.claude/commands/execute.md
Execution Report: Read this to understand what the agent ACTUALLY did and why: Execution report: $2
Analysis Workflow
Step 1: Understand the Planned Approach
Read the generated plan ($1) and extract:
- What features were planned?
- What architecture was specified?
- What validation steps were defined?
- What patterns were referenced?
Step 2: Understand the Actual Implementation
Read the execution report ($2) and extract:
- What was implemented?
- What diverged from the plan?
- What challenges were encountered?
- What was skipped and why?
Step 3: Classify Each Divergence
For each divergence identified in the execution report, classify as:
Good Divergence ✅ (Justified):
- Plan assumed something that didn't exist in the codebase
- Better pattern discovered during implementation
- Performance optimization needed
- Security issue discovered that required different approach
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 · 192 lines · 7 tokens per session scan A 43dd829e6fb6
system-review is a command published in the GitHub repository coleam00/Archon (23,374 stars, last pushed 2d ago), licensed MIT. It adds 7 tokens to every session and 1,158 once invoked, about $0.0000 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
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.
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.
hello
Say hello.