Borrowing it
Nothing to install: this file belongs to s-stefanov/actual-mcp. 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/s-stefanov/actual-mcp/main/.claude/commands/generate-prp.mdgit clone --depth 1 https://github.com/s-stefanov/actual-mcpWrote 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/s-stefanov/actual-mcp/generate-prp)<a href="https://agentmods.dev/commands/s-stefanov/actual-mcp/generate-prp"><img src="https://agentmods.dev/badge/commands/s-stefanov/actual-mcp/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/s-stefanov/actual-mcp/generate-prp"><img src="https://agentmods.dev/badge/commands/s-stefanov/actual-mcp/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 | $0.00585 |
| Opus 5 | $0.00000 | $0.00293 |
| Sonnet 5 | $0.00000 | $0.00117 |
| Haiku 4.5 | $0.00000 | $0.00059 |
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 10d 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.
This is a copy
100% identical to generate-prp — 10 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 75 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Create PRP
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
-
User Clarification (if needed)
- Specific patterns to mirror and where to find them?
- Integration requirements and where to find them?
PRP Generation
Using PRPs/templates/prp_base.md as template:
Critical Context to Include and pass to the AI agent as part of the PRP
- Documentation: URLs with specific sections
- Code Examples: Real snippets from codebase
- Gotchas: Library quirks, version issues
- Patterns: Existing approaches to follow
Implementation Blueprint
- Start with pseudocode showing approach
- Reference real files for patterns
- Include error handling strategy
- list tasks to be completed to fullfill the PRP in the order they should be completed
Validation Gates (Must be Executable) eg for python
# Syntax/Style
ruff check --fix && mypy .
# Unit Tests
uv run pytest tests/ -v
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.
- 10d ago First seen · 75 lines · 0 tokens per session scan A a02adb2ab4ae
generate-prp is a command published in the GitHub repository s-stefanov/actual-mcp (225 stars, last pushed 2d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 585 tokens. A static security scan graded it A with 0 findings. It is 100% identical to generate-prp, differing in 10 lines, and is treated as a copy.
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.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
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.