PRP is a collection of prompts and workflows for AI-assisted software development with Claude Code. Its Product Requirement Prompts combine product requirements, selected codebase context, and an execution runbook so a coding agent can implement a vertical slice of software. The catalogue entries are the project’s own skills, agents, instructions, hook, and plugin workflows.
Borrowing it
Nothing to install: this file belongs to Wirasm/prp. 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/Wirasm/prp/development/.agents/skills/prp-plan/SKILL.mdgit clone --depth 1 https://github.com/Wirasm/prpWrote 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/wirasm/prp/prp-plan)<a href="https://agentmods.dev/skills/wirasm/prp/prp-plan"><img src="https://agentmods.dev/badge/skills/wirasm/prp/prp-plan/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/wirasm/prp/prp-plan"><img src="https://agentmods.dev/badge/skills/wirasm/prp/prp-plan.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- 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 | $0.00122 | $0.03300 |
| Opus 5.5 | $0.00049 | $0.01320 |
| Sonnet 5.5 | $0.00024 | $0.00660 |
| Haiku 4.5 | $0.00012 | $0.00330 |
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 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.
How it starts
The opening of the file, as written. The whole thing — 214 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Arguments:
$ARGUMENTS(and$1,$2, ...) refer to the arguments given when this skill was invoked. Take them from the user's request; if absent, infer them from the conversation.
PRP Plan
Produce a plan a human can scan and an implementation agent can execute without rediscovering the design. Identify the invariant, find the existing primitives, and choose the smallest solution supported by evidence.
Plan only. Do not implement, commit, or open a PR. A spike is allowed only to settle an architectural hinge; its code remains throwaway under the prp-spike contract.
Input: $ARGUMENTS (if absent, use the conversation).
Mode
- A request to link two existing plans routes to
workflows/update-references.mdand stops. publish <existing .plan.md>publishes or refreshes that plan on its recorded source issue, updatesPlan Publication, verifies the shared comment, and stops. Do not redesign the plan unless the user asks to revise it.- A bug report, stack trace, regression, error, or unexplained current behavior adds root-cause analysis before solution design.
- Everything else creates an implementation plan.
1. Resolve the request
Accept a PRD path, issue reference or URL, another document, free-form text, or conversation context.
For a PRD:
- Read it and select the first pending phase whose dependencies are complete.
- Preserve its problem, user, hypothesis, scope, and success signal.
- Note other independently actionable phases, but plan only the selected phase.
- Tell the user which phase was selected.
For an issue from GitHub, Jira, Linear, or another tracker:
- Retrieve the issue through whatever access is already configured in the environment. The skill does not prescribe or configure a tracker client.
- Treat the body as the starting point, not the complete brief. Read the relevant comment history—including earlier published PRP plans and corrections after them—and follow linked issues, parent/child or blocking relationships, duplicates, PRs, specifications, and attachments that can change scope, intent, constraints, or current decisions.
- Reconcile that context: distinguish current decisions from superseded discussion, note unresolved disagreements, and stop following links once additional material no longer affects the plan. Curate; do not dump the tracker graph.
- Preserve the source issue in the plan while separating its required outcome from any suggested implementation.
- If the issue, comments, or decision-relevant links cannot be retrieved, state what context is missing and ask the user to provide it or configure access. Never infer missing tracker content.
What ships with it
7 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.
- 17d ago Changed · +3 lines b0e56867a8ab
- 19d ago Changed · +2 lines ef22ca432a1e
- 1mo ago First seen · 209 lines · 122 tokens per session scan A b0b90fef7718
prp-plan is a skill published in the GitHub repository Wirasm/prp (2,264 stars, last pushed 18d ago), licensed MIT. It adds 122 tokens to every session and 3,300 once invoked, about $0.0005 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
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…
next-cache-components-optimizer
Optimize the meaningful UI available immediately from a Next.js route on an initial load (hard navigation) or named client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route and entry point at a time; the shipped test then guards…