Borrowing it
Nothing to install: this file belongs to tomdwipo/claude-soul. 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/tomdwipo/claude-soul/main/.claude/commands/mini-prd.mdgit clone --depth 1 https://github.com/tomdwipo/claude-soulWrote 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/tomdwipo/claude-soul/mini-prd)<a href="https://agentmods.dev/commands/tomdwipo/claude-soul/mini-prd"><img src="https://agentmods.dev/badge/commands/tomdwipo/claude-soul/mini-prd.svg" alt="Measured on agentmods" 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 | $0.00271 |
| Opus 5 | $0.00000 | $0.00135 |
| Sonnet 5 | $0.00000 | $0.00054 |
| Haiku 4.5 | $0.00000 | $0.00027 |
Grade A, and why
mini-prd 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 8d 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.
What it actually says
mini-prd Command
Create a mini PRD for $ARGUMENTS that includes goal, requirements, and acceptance criteria. create file at .docs/year(YYYY)/month(MM)/day(DD)/{number-in-DD-folder(01)}-{Feature Spec}/{Feature Spec}-spec.md. check real current date first. only 1 file. must follow the format specified in the example bellow. do not add any additional information. max 8000 characters if the result exceeds 8000 characters, just it is.
example: User Authentication Feature Spec
User Authentication Feature Spec
Goal
Implement secure user authentication with JWT tokens
Requirements
- Email/password login
- Password hashing with bcrypt
- JWT token generation
- Protected route middleware
- Input validation and sanitization
Acceptance Criteria
- User can register with email/password
- User can login and receive JWT token
- Protected routes verify JWT tokens
- Passwords are properly hashed
- Input validation prevents injection attacksCopy
Implementation Approach
end to end system flow
use ascii diagram to show the flow of the feature from start to finish.
Files to Modify
Technical Constraints
Success Metrics
Risk Mitigation
Documentation Updates Required
Summary of Changes
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.
- 8d ago First seen · 47 lines · 0 tokens per session scan A 884dce4d6283
mini-prd is a command published in the GitHub repository tomdwipo/claude-soul (24 stars, last pushed yesterday), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 271 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 2026-08-30.
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.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.