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/skills/archon-cli/SKILL.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/skills/coleam00/archon/archon-cli)<a href="https://agentmods.dev/skills/coleam00/archon/archon-cli"><img src="https://agentmods.dev/badge/skills/coleam00/archon/archon-cli/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/skills/coleam00/archon/archon-cli"><img src="https://agentmods.dev/badge/skills/coleam00/archon/archon-cli.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Snyk pass
- NVIDIA SkillSpector pass
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 | $0.00194 | $0.01571 |
| Opus 5.5 | $0.00078 | $0.00628 |
| Sonnet 5.5 | $0.00039 | $0.00314 |
| Haiku 4.5 | $0.00019 | $0.00157 |
Grade A, and why
archon-cli 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 yesterday.
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 — 113 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Archon CLI
Archon runs multi-step AI workflows through the archon CLI. Git projects run in
isolated worktrees by default; registered folder projects run in place. You sit on
top of the runs: you write the brief, launch, watch one stream, answer the decisions
that reach you, and read what each run left behind.
Route by intent:
| The user wants to... | Read |
|---|---|
| Run a workflow on real work | operate/running.md |
| Inspect, answer, cancel or resume runs | operate/managing-runs.md |
| Drive the SDLC pack's queues | operate/queues.md |
| Find out why a run failed or hangs | operate/troubleshooting.md |
| Set up Archon or change config | setup/setup-and-config.md |
| Author or change a workflow | authoring/authoring-workflows.md (then node-reference.md and variables.md in the same folder) |
| Improve a node prompt or a run message | authoring/prompting-mistakes.md |
- When the intent is clear ("build me a workflow for X", "run archon-ship on issue #42"), go straight to the reference. Do not ask.
- When it is ambiguous, ask which of these the user wants.
- On a machine where
archon doctorfails, start withsetup/setup-and-config.md.
The operating loop
-
Discover.
archon workflow list --jsongives compact descriptions. For each plausible candidate, read the full description witharchon workflow list <name> --full. Choose from full descriptions, never from names you remember. -
Check the brief. The message, issue body or document the run reads is the contract the run is measured against. It must state the problem, why it is worth solving, why now, the desired outcome, the invariants and the acceptance. If any is missing, say which, propose wording, and get the user's agreement before launching.
operate/running.mdhas the full check. -
Launch detached. Pass the work as a message, the workflow's declared inputs with
--input, or both:archon workflow run <workflow> --branch <branch-name> "<the work>" --detach archon workflow run <workflow> --input <name>=<value> --input <name>=<value> --detach
What ships with it
9 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- yesterday Changed · +8 lines c8cec1a86700
- 3d ago Changed · +7 lines · +29 tokens per session ee011c2a3986
- 8d ago Changed · -3 lines 63d3c64336da
- 1mo ago Changed · +21 lines c32f936846fb
- 1mo ago First seen · 80 lines · 165 tokens per session scan A d16c1aca31cb
archon-cli is a skill published in the GitHub repository coleam00/Archon (23,752 stars, last pushed today), licensed MIT. It adds 194 tokens to every session and 1,571 once invoked, about $0.0008 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 skills, from other repositories
system-prompts
Write system prompts, tool docs, and agent definitions. Project tag conventions + RFC 2119 keywords + dense compression. Use when authoring or editing any prompt the model reads.
tool-prompt-optimization
Optimize the description prompts an AI agent reads to learn its built-in tools (the .md files under prompts/tools/). Two halves: (1) measure how much of a prompt is already inferable from the tool's JSON parameter schema + name, to prune redundancy with evidence; (2) house authoring rules for what belongs in a tool…
semantic-compression
Re-encode verbose prose into a dense telegraphic register — punctuation as connectives, label frames, verbless assertions — without losing normativity or precision. Use when compressing system prompts, tool/function descriptions, skill bodies, or agent instructions; reducing token count or context bloat; making…
delegation
Delegates independent work to worker agents through the Task tool, choosing between the explore scout and the ml, biology, physics, chemistry and data specialists, writing a self-contained brief for a worker that cannot see the conversation, setting boundaries on files and compute, using two independent annotators…
hatch3r-cli-toolbox
Category-indexed reference for 34 specialist CLI tools beyond the always-on five (ripgrep, jq, gh, fd, fzf). Use to pick the right tool for HTTP clients, ai-chat, structural-search, sed-style edits, data ops, browser automation, container ops, and more.
hatch3r-cleanup
Repo-state cleanup pass — inventories accumulated hatch3r working state (plan files, workspace dirs, review telemetry, findings ledgers, learnings, handoffs, board staleness), asks once with per-category counts and estimated reclaim, dispatches each owned surface to its owning skill or CLI path, and prunes only the…