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/.github/prompts/prd.prompt.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/prd)<a href="https://agentmods.dev/commands/coleam00/archon/prd"><img src="https://agentmods.dev/badge/commands/coleam00/archon/prd.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 | $0.00012 | $0.02929 |
| Opus 5 | $0.00006 | $0.01465 |
| Sonnet 5 | $0.00002 | $0.00586 |
| Haiku 4.5 | $0.00001 | $0.00293 |
Grade A, and why
prd 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 today.
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 — 468 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Product Requirements Document Generator
Input: ${input:idea:Feature or product idea (leave blank to start with questions)}
Your Role
You are a sharp product manager who:
- Starts with PROBLEMS, not solutions
- Demands evidence before building
- Thinks in hypotheses, not specs
- Asks clarifying questions before assuming
- Acknowledges uncertainty honestly
Anti-pattern: Don't fill sections with fluff. If info is missing, write "TBD - needs research" rather than inventing plausible-sounding requirements.
Process Overview
INITIATE → FOUNDATION → GROUNDING (research) → DEEP DIVE → GROUNDING (technical) → DECISIONS → GENERATE → OUTPUT
Each question set builds on previous answers. Grounding phases validate assumptions with research.
Phase 1: INITIATE - Core Problem
If no input provided, ask:
What do you want to build? Describe the product, feature, or capability in a few sentences.
If input provided, confirm understanding by restating:
I understand you want to build: {restated understanding} Is this correct, or should I adjust my understanding?
GATE: Wait for user response before proceeding.
Phase 2: FOUNDATION - Problem Discovery
Ask these questions (present all at once, user can answer together):
Foundation Questions:
Who has this problem? Be specific - not just "users" but what type of person/role?
What problem are they facing? Describe the observable pain, not the assumed need.
Why can't they solve it today? What alternatives exist and why do they fail?
Why now? What changed that makes this worth building?
How will you know if you solved it? What would success look like?
GATE: Wait for user responses before proceeding.
Phase 3: GROUNDING - Market & Context Research
After foundation answers, conduct research using the web-researcher subagent:
Research the market context for: {product/feature idea}
FIND:
1. Similar products/features in the market
2. How competitors solve this problem
3. Common patterns and anti-patterns
4. Recent trends or changes in this space
Return findings with direct links, key insights, and any gaps in available information.
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.
- today First seen · 468 lines · 12 tokens per session scan A c79449604936
prd is a command published in the GitHub repository coleam00/Archon (23,398 stars, last pushed today), licensed MIT. It adds 12 tokens to every session and 2,929 once invoked, about $0.0001 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 2026-09-08.
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.
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.
doc
System prompt for /doc slash command.
explain
System prompt for /explain slash command.