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-loop/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-loop)<a href="https://agentmods.dev/skills/wirasm/prp/prp-loop"><img src="https://agentmods.dev/badge/skills/wirasm/prp/prp-loop/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-loop"><img src="https://agentmods.dev/badge/skills/wirasm/prp/prp-loop.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 | $0.00095 | $0.00889 |
| Opus 5.5 | $0.00038 | $0.00356 |
| Sonnet 5.5 | $0.00019 | $0.00178 |
| Haiku 4.5 | $0.00010 | $0.00089 |
Grade A, and why
prp-loop 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 1mo 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 — 57 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Arguments:
$ARGUMENTS(and$1,$2, ...) refer to the arguments given when this skill was invoked. Take them from the user's request; if absent, infer them from the conversation.
PRP Loop — autonomous cyclic pipeline
Launch the orchestrator that drives plan → implement (commit + PR) → review and loops review → fix until the PR review is clean (or limits are hit). It runs headless codex exec once per stage and tracks progress in ~/.prp/<key>/state/prp-loop.state.json.
Run it
Start a new loop with the user's request as the feature argument:
uv run .agents/skills/prp-loop/scripts/prp_loop.py "$ARGUMENTS" --cli codex
Resume a halted or in-progress loop:
uv run .agents/skills/prp-loop/scripts/prp_loop.py --resume --cli codex
Defaults: --max-cycles 3, --max-implement-iterations 10, base branch auto-detected. Pass --validate "<cmd>" to give the loop an authoritative green check (exit 0 = pass).
Stop after a stage (--until)
Pass --until <stage> (plan | implement | pr | review | fix) to halt once that stage completes:
uv run .agents/skills/prp-loop/scripts/prp_loop.py "$ARGUMENTS" --cli codex --until implement
--until implement runs plan → implement and stops once validations are green and the implementation skill has committed and opened its PR — no review.
UX note: the retired Ralph loop was single-session and interactive (a Stop-hook fed the prompt back in the same session). prp-loop --until implement is headless instead — it drives fresh codex exec sessions per iteration and you resume/inspect via the state file rather than watching it live.
What it does
- plan —
prp-planwrites the plan under the project's PRP store at$PRP_DIR/plans/<feature>.plan.md. - implement —
prp-implementexecutes and validates the plan, commits the work, and opens the PR (bounded by--max-implement-iterations). - pr compatibility — if an older implementation run did not open a PR,
prp-prdoes so once. - review —
prp-reviewruns its current default review, writes the canonical report, and publishes that complete report to GitHub. - cycle — if the verdict needs fixes, the complete report, plan, and live PR feed into a fresh
prp-implementcorrection pass → push → re-review, up to--max-cycles. Ready to merge → done; review incomplete → halt.
What ships with it
1 file 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.
- 1mo ago First seen · 57 lines · 95 tokens per session scan A 818a91408bf6
prp-loop is a skill published in the GitHub repository Wirasm/prp (2,264 stars, last pushed 18d ago), licensed MIT. It adds 95 tokens to every session and 889 once invoked, about $0.0004 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
release-announcement
Write a release announcement — changelog, blog post, in-app note, or social post — that leads with user impact, names the audience, and includes upgrade/migration steps without filler.
release
Fully automate a release with version-bump and childhood.
release-notes
Generate user-facing release notes from tickets, PRDs, or changelogs. Creates clear, engaging summaries organized by category (new features, improvements, fixes). Use when writing release notes, creating changelogs, announcing product updates, or summarizing what shipped.
multi-agent-release-manager
Cleans up the workspace, formats code, runs presubmit checks, and uploads CLs to Gerrit.
pack-submit
Package one of this agent's own skills as a standalone community pack and submit it to the aeon registry as a PR.
release-helper
Ensures configuration and code changes are released correctly. Use PROACTIVELY whenever you edit config.json, change any setting, or fix a configuration bug in this project, so the change reaches production.