Borrowing it
Nothing to install: this file belongs to SteppieD/context-engineering. 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/SteppieD/context-engineering/main/.claude/commands/generate-prp.mdgit clone --depth 1 https://github.com/SteppieD/context-engineeringWrote 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/commands/steppied/context-engineering/generate-prp)<a href="https://agentmods.dev/commands/steppied/context-engineering/generate-prp"><img src="https://agentmods.dev/badge/commands/steppied/context-engineering/generate-prp/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/commands/steppied/context-engineering/generate-prp"><img src="https://agentmods.dev/badge/commands/steppied/context-engineering/generate-prp.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 | $0.00000 | $0.00936 |
| Opus 5.5 | $0.00000 | $0.00374 |
| Sonnet 5.5 | $0.00000 | $0.00187 |
| Haiku 4.5 | $0.00000 | $0.00094 |
Grade A, and why
generate-prp 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 — 86 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Create PRP
YOU MUST DO IN-DEPTH RESEARCH, FOLLOW THE
- Don't only research one page, and don't use your own webscraping tool - instead scrape many relevant pages from all documentation links mentioned in the initial.md file
- Take my tech as sacred truth, for example if I say a model name then research that model name for LLM usage - don't assume from your own knowledge at any point
- When I say don't just research one page, I mean do incredibly in-depth research, like to the ponit where it's just absolutely ridiculous how much research you've actually done, then when you creat the PRD document you need to put absolutely everything into that including references to the .md files you put inside the /research/ directory so any AI can pick up your PRD and generate WORKING and COMPLETE production ready code.
</RESEARCH PROCESS>
Feature file: $ARGUMENTS
Generate a complete PRP for general feature implementation with thorough research. Ensure context is passed to the AI agent to enable self-validation and iterative refinement. Read the feature file first to understand what needs to be created, how the examples provided help, and any other considerations.
The AI agent only gets the context you are appending to the PRP and training data. Assuma the AI agent has access to the codebase and the same knowledge cutoff as you, so its important that your research findings are included or referenced in the PRP. The Agent has Websearch capabilities, so pass urls to documentation and examples.
Research Process
-
Codebase Analysis
- Search for similar features/patterns in the codebase
- Identify files to reference in PRP
- Note existing conventions to follow
- Check test patterns for validation approach
-
External Research
- Search for similar features/patterns online
- Library documentation (include specific URLs)
- Implementation examples (GitHub/StackOverflow/blogs)
- Best practices and common pitfalls
- Don't only research one page, and don't use your own webscraping tool - instead scrape many relevant pages from all documentation links mentioned in the initial.md file
- Take my tech as sacred truth, for example if I say a model name then research that model name for LLM usage - don't assume from your own knowledge at any point
- When I say don't just research one page, I mean do incredibly in-depth research, like to the ponit where it's just absolutely ridiculous how much research you've actually done, then when you creat the PRD document you need to put absolutely everything into that including INCREDIBLY IN DEPTH CODE EXMAPLES so any AI can pick up your PRD and generate WORKING and COMPLETE production ready code.
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 · 86 lines · 0 tokens per session scan A 921d952e2155
generate-prp is a command published in the GitHub repository SteppieD/context-engineering (5 stars, last pushed 1y ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 936 tokens. 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 2026-08-31.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.
speckit.bug.fix
Apply the remediation from a bug assessment and record what was changed.