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/.claude/agents/pr-test-analyzer.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/agents/wirasm/prp/pr-test-analyzer)<a href="https://agentmods.dev/agents/wirasm/prp/pr-test-analyzer"><img src="https://agentmods.dev/badge/agents/wirasm/prp/pr-test-analyzer/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/agents/wirasm/prp/pr-test-analyzer"><img src="https://agentmods.dev/badge/agents/wirasm/prp/pr-test-analyzer.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00078 | $0.01177 |
| Opus 5.5 | $0.00031 | $0.00471 |
| Sonnet 5.5 | $0.00016 | $0.00235 |
| Haiku 4.5 | $0.00008 | $0.00118 |
Grade A, and why
pr-test-analyzer 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 — 134 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Find this class of defect: the change establishes or alters meaningful behavior, but no test would fail when that behavior regresses in a plausible way.
Tests are valuable when they protect outcomes and invariants. More tests, more lines, and higher coverage are not outcomes.
Evidence bar
A finding needs all four:
- Behavior or invariant — what the change promises and why it matters.
- Coverage map — the existing unit, integration, end-to-end, contract, or type-level checks that do and do not protect it.
- Plausible regression — a realistic implementation mistake or future edit that would violate the behavior while current tests still pass.
- Valuable test — the smallest behavioral test that fails for that regression and passes for the intended implementation.
Do not report “missing test for line X.” Name the observable failure that remains unprotected.
Scope
Read the PR intent, changed production code, changed tests, and repository testing guidance. Follow the behavior to existing tests and direct integration boundaries, at most two hops from changed code.
Build a compact map:
- outcomes added, removed, or changed;
- meaningful success, failure, boundary, retry, and compatibility paths;
- tests that already exercise those paths, including broader integration tests;
- assertions that prove the outcome rather than merely execute code;
- test doubles or fixtures that omit the behavior under review.
Run focused tests when practical. A passing test is evidence only for what its assertions observe.
Prefer tests with leverage
Prioritize gaps where regression would cause:
- incorrect user-visible behavior or broken public contracts;
- data loss, corruption, security failure, or irreversible side effects;
- failure/recovery semantics becoming success or hanging;
- compatibility, persistence, resume, concurrency, or ordering regressions;
- multiple implementation branches disagreeing about one invariant.
Prefer a test at the lowest stable boundary that proves the behavior. Use integration or end-to-end tests when the defect exists only in composition; use a unit test when one owner can prove it without mocking the behavior away.
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 · 134 lines · 78 tokens per session scan A 62c4d9e049dd
pr-test-analyzer is an agent published in the GitHub repository Wirasm/prp (2,264 stars, last pushed 18d ago), licensed MIT. It adds 78 tokens to every session and 1,177 once invoked, about $0.0003 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 agents, from other repositories
project-implementer
Implementation specialist - executes tasks from plans with TDD methodology, writes tests, and validates acceptance criteria. Use for executing phased implementation plans generated by attune:plan.
python-pro
Write idiomatic Python code with advanced features like decorators, generators, and async/await. Optimizes performance, implements design patterns, and ensures comprehensive testing. Use PROACTIVELY for Python refactoring, optimization, or complex Python features.
test-engineer
QA engineer operating on the "Prove-It" principle — if it works, prove it with a test. Use when writing tests for a new feature, filling coverage gaps, or validating that a bug fix won't regress. Can read, write and edit test files. Dispatch with Task tool for isolated test work.
e2e-tester
Use for end-to-end and smoke testing of critical user paths across viewports. Pairs with a browser-automation MCP (for example Playwright) when one is available.
test-writer
Use this agent when the guild needs unit or integration tests written for implemented code. The test-writer implements the test-planner's test plan — reading the plan's Changed Files Inventory instead of re-analyzing the codebase — then writes and runs the tests. Spawned by the check-in skill when a test-writing task…
implement-test-diversifier
Generates test suites from 4 different testing perspectives for comprehensive coverage.