prp-plan

prp-plan is a command for coding agents from mturac/everything-openai-codex. It costs 12 tokens per session (3,328 once invoked), scanned A, original, MIT.

An implementation-planning command that studies a codebase and produces a detailed plan for building a feature.

In plain words
What is it for?
It helps turn a feature description or PRD into a self-contained plan with codebase analysis, implementation phases, and relevant technical context.
Why use it?
It records the project's patterns, conventions, and known pitfalls so implementation can proceed without repeatedly rediscovering them.

Command

Install

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.

agentmods
npx agentmods add commands/mturac/everything-openai-codex/prp-plan
Clone the repo
git clone --depth 1 https://github.com/mturac/everything-openai-codex

Wrote 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.

agentmods badge for prp-plan

README.md
[![agentmods](https://agentmods.dev/badge/commands/mturac/everything-openai-codex/prp-plan.svg)](https://agentmods.dev/commands/mturac/everything-openai-codex/prp-plan)
Your own site
<a href="https://agentmods.dev/commands/mturac/everything-openai-codex/prp-plan"><img src="https://agentmods.dev/badge/commands/mturac/everything-openai-codex/prp-plan.svg" alt="Measured on agentmods" height="20"></a>
Per session 12 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 3,328 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00012 $0.03328
Opus 5 $0.00006 $0.01664
Sonnet 5 $0.00002 $0.00666
Haiku 4.5 $0.00001 $0.00333

Measured yesterday against content hash 9e7e4153a4e9, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

prp-plan 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.

Origin

Copies of this mod

1 near-identical copy found in the catalogue:

  • prp-plan — 98% identical, 10 lines differ
commands/prp-plan.md · 503 lines

How it starts

The opening of the file, as written. The whole thing — 503 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Adapted from PRPs-agentic-eng by Wirasm. Part of the PRP workflow series.

PRP Plan

Create a detailed, self-contained implementation plan that captures all codebase patterns, conventions, and context needed to implement a feature in a single pass.

Core Philosophy: A great plan contains everything needed to implement without asking further questions. Every pattern, every convention, every gotcha — captured once, referenced throughout.

Golden Rule: If you would need to search the codebase during implementation, capture that knowledge NOW in the plan.


Phase 0 — DETECT

Determine input type from $ARGUMENTS:

Input Pattern Detection Action
Path ending in .prd.md File path to PRD Parse PRD, find next pending phase
Path to .md with "Implementation Phases" PRD-like document Parse phases, find next pending
Path to any other file Reference file Read file for context, treat as free-form
Free-form text Feature description Proceed directly to Phase 1
Empty / blank No input Ask user what feature to plan

PRD Parsing (when input is a PRD)

  1. Read the PRD file with cat "$PRD_PATH"
  2. Parse the Implementation Phases section
  3. Find phases by status:
    • Look for pending phases
    • Check dependency chains (a phase may depend on prior phases being complete)
    • Select the next eligible pending phase
  4. Extract from the selected phase:
    • Phase name and description
    • Acceptance criteria
    • Dependencies on prior phases
    • Any scope notes or constraints
  5. Use the phase description as the feature to plan

If no pending phases remain, report that all phases are complete.


Phase 1 — PARSE

Extract and clarify the feature requirements.

Feature Understanding

From the input (PRD phase or free-form description), identify:

  • What is being built (concrete deliverable)
  • Why it matters (user value)
  • Who uses it (target user/system)
  • Where it fits (which part of the codebase)

Read the full file on GitHub · 503 lines

Changes

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.

  1. yesterday First seen · 503 lines · 12 tokens per session scan A 9e7e4153a4e9

Subscribe to this mod's changes

prp-plan is a command published in the GitHub repository mturac/everything-openai-codex (89 stars, last pushed 11d ago), licensed MIT. It adds 12 tokens to every session and 3,328 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.