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/mturac/everything-openai-codex/prp-prdgit clone --depth 1 https://github.com/mturac/everything-openai-codexWrote 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/mturac/everything-openai-codex/prp-prd)<a href="https://agentmods.dev/commands/mturac/everything-openai-codex/prp-prd"><img src="https://agentmods.dev/badge/commands/mturac/everything-openai-codex/prp-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 | $0.00018 | $0.03004 |
| Opus 5 | $0.00009 | $0.01502 |
| Sonnet 5 | $0.00004 | $0.00601 |
| Haiku 4.5 | $0.00002 | $0.00300 |
Grade A, and why
prp-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 3d 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:
- prp-prd — 92% identical, 16 lines differ
How it starts
The opening of the file, as written. The whole thing — 448 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Product Requirements Document Generator
Adapted from PRPs-agentic-eng by Wirasm. Part of the PRP workflow series.
Input: $ARGUMENTS
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
QUESTION SET 1 → GROUNDING → QUESTION SET 2 → RESEARCH → QUESTION SET 3 → GENERATE
Each question set builds on previous answers. Grounding phases validate assumptions.
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:
Research market context:
- Find similar products/features in the market
- Identify how competitors solve this problem
- Note common patterns and anti-patterns
- Check for recent trends or changes in this space
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.
- 3d ago First seen · 448 lines · 18 tokens per session scan A f7f56b855381
prp-prd is a command published in the GitHub repository mturac/everything-openai-codex (89 stars, last pushed 13d ago), licensed MIT. It adds 18 tokens to every session and 3,004 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 2026-09-03.
Other commands, from other repositories
goal
Set a goal and enter the RALPH loop — keep working autonomously until the goal is genuinely done or the user stops you. Use /goal "objective" to start, /goal status to check, /goal complete to finish, /goal clear to stop.
teach
Hand-record a lesson into Memory Fabric (learning tier, high confidence) so it is recalled later.
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.