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/commands/validation/system-review.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/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/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/system-review"><img src="https://agentmods.dev/badge/commands/coleam00/archon/system-review.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 | $0.00007 | $0.01154 |
| Opus 5.5 | $0.00003 | $0.00462 |
| Sonnet 5.5 | $0.00001 | $0.00231 |
| Haiku 4.5 | $0.00001 | $0.00115 |
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 17d 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, 34 lines differ
How it starts
The opening of the file, as written. The whole thing — 193 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 (AGENTS.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.
- 17d ago Changed · +1 lines 826aff301596
- 1mo ago First seen · 192 lines · 7 tokens per session scan A 43dd829e6fb6
system-review is a command published in the GitHub repository coleam00/Archon (23,752 stars, last pushed today), licensed MIT. It adds 7 tokens to every session and 1,154 once invoked, about $0.0000 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.
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-workflow
Guided development lifecycle with 4 phases (Analyze, Plan, Implement, Review) and scale-adaptive Quick Mode for small tasks; --plan-file executes an approved plan document without re-deriving it.