Borrowing it
Nothing to install: this file belongs to mort-lab/excel-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/mort-lab/excel-mcp/main/.claude/commands/prp-commands/prp-story-create.mdgit clone --depth 1 https://github.com/mort-lab/excel-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/mort-lab/excel-mcp/prp-story-create)<a href="https://agentmods.dev/commands/mort-lab/excel-mcp/prp-story-create"><img src="https://agentmods.dev/badge/commands/mort-lab/excel-mcp/prp-story-create.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 | $0.00013 | $0.01107 |
| Opus 5 | $0.00006 | $0.00553 |
| Sonnet 5 | $0.00003 | $0.00221 |
| Haiku 4.5 | $0.00001 | $0.00111 |
Grade A, and why
prp-story-create 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.
How it starts
The opening of the file, as written. The whole thing — 143 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Create Story PRP from User Story/Task
Story/Task: $ARGUMENTS
Mission
Transform a user story or task into a tactical implementation PRP through systematic codebase analysis and task decomposition.
We do not write any code in this step, the goal is to create a detailed context engineered implementation plan for the implementation agent.
Key Principle: We must first gather the context about the story/task before proceeding with the analysis.
When we understand the story/task, we can proceed with the codebase analysis. We systematically dig deep into the codebase to gather intelligence and identify patterns and implementation points. We then use this information to create a PRP that can be executed by a coding agent.
The contents of the created PRP should encapsulate all the information the agent needs to complete the story/task in one pass.
Remember that subagents will only receive their details from you, the user has no way of interacting with the subagents. so you need to share all the relevant context to the subagent in the subagent prompt and in the TODO that is shared with the particular agent.
Create detailed todos and spawn parallel subagents to analyze (Use specialized subagents when apropriate):
Analysis Process
Phase 1: Story Decomposition
Analyze the story to determine:
- Story/Task Type: Feature/Bug/Enhancement/Refactor
- Complexity: Low, Medium, High
- Affected Systems: Which components/services need changes
Get a deep understanding about the story/task before proceeding so that you can effectively guide the rest of the process.
Phase 2: Codebase Intelligence Gathering
1. Project Structure Analysis
- Detect primary language(s) and frameworks
- Map directory structure and conventions to identify integration points for the story/task
- Identify service/component boundaries
- Find configuration files and environment setup
2. Pattern Recognition
- Search for similar implementations in codebase
- Identify coding conventions (naming, structure, error handling) start in CLAUDE.md AGENTS.md or relevant rules files such as .cursorrules
- Extract common patterns for the story's domain that should be added to the PRP as context for the implementation agent.
- Note anti-patterns to avoid
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 · 143 lines · 13 tokens per session scan A 859ce5beace8
prp-story-create is a command published in the GitHub repository mort-lab/excel-mcp (5 stars, last pushed 1mo ago), licensed MIT. It adds 13 tokens to every session and 1,107 once invoked, about $0.0001 per session on Opus 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 2026-08-31.
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