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 skills add matteocervelli/llms --skill prp-generatorgit clone --depth 1 https://github.com/matteocervelli/llmsWrote 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/matteocervelli/llms/prp-generator)<a href="https://agentmods.dev/skills/matteocervelli/llms/prp-generator"><img src="https://agentmods.dev/badge/skills/matteocervelli/llms/prp-generator/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/matteocervelli/llms/prp-generator"><img src="https://agentmods.dev/badge/skills/matteocervelli/llms/prp-generator.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 | $0.00031 | $0.06857 |
| Opus 5.5 | $0.00012 | $0.02743 |
| Sonnet 5.5 | $0.00006 | $0.01371 |
| Haiku 4.5 | $0.00003 | $0.00686 |
Grade A, and why
prp-generator 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 2mo 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 — 1,026 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Purpose
The prp-generator skill provides structured templates and methods for creating comprehensive Product Requirements Prompt (PRP) documents. PRPs serve as the primary input for Phase 3 (Implementation), containing all design information, library documentation, dependencies, implementation plans, testing strategies, and success criteria needed to implement features.
When to Use
This skill auto-activates when you:
- Generate PRP documents from synthesized design
- Create implementation guidance from architectural design
- Structure design information for implementation phase
- Define implementation plans with step-by-step guidance
- Specify testing strategies and success criteria
- Document architecture, libraries, and dependencies for implementers
Provided Capabilities
1. Structured PRP Generation
- Standard Template: Consistent PRP structure across all features
- Section Templates: Pre-defined templates for each PRP section
- Completeness Checking: Ensure all required sections present
- Format Validation: Validate markdown structure and linking
2. Implementation Planning
- Phased Approach: Break implementation into logical phases
- Step-by-Step Guidance: Detailed steps within each phase
- Dependency Ordering: Ensure dependencies installed before use
- Validation Checkpoints: Define validation at each phase
3. Testing Strategy Documentation
- Test Types: Unit, integration, end-to-end testing approaches
- Test Coverage: Specify coverage targets per component
- Mock Strategies: Document how to mock library dependencies
- Test Data: Provide test data and fixture guidance
4. Success Criteria Definition
- Acceptance Criteria: Map from requirements analysis
- Implementation Criteria: Code-level success criteria
- Testing Criteria: Test coverage and passing requirements
- Documentation Criteria: Required documentation artifacts
5. Architecture Documentation
- Component Diagrams: Visual architecture representations
- Data Models: Pydantic schemas with validations
- API Contracts: Endpoint specifications with examples
- Data Flow: How data moves through system
What ships with it
2 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.
- 2mo ago First seen · 1,026 lines · 31 tokens per session scan A fdf5ef8eac61
prp-generator is a skill published in the GitHub repository matteocervelli/llms (25 stars, last pushed 5mo ago), licensed MIT. It adds 31 tokens to every session and 6,857 once invoked, about $0.0001 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 2026-09-01.
prp-generator compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| prp-generatormatteocervelli/llms | Skill | 25 | 5mo ago | MIT | A | 31 | Claude Code |
| imagegenopenai/codex | Skill | 133k | yesterday | Apache-2.0 | A | 113 | Codex |
| playwright-climicrosoft/playwright | Skill | 99k | yesterday | Apache-2.0 | A | 19 | Claude Code |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | 4d ago | MIT | A | 200 | Claude Code, Codex |
| systematic-debuggingobra/superpowers | Skill | 313k | 3d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 95 | Claude Code |
| next-partial-prefetching-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 103 | Claude Code |