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 agentmods add commands/mort-lab/excel-mcp/prp-core-creategit 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-core-create)<a href="https://agentmods.dev/commands/mort-lab/excel-mcp/prp-core-create"><img src="https://agentmods.dev/badge/commands/mort-lab/excel-mcp/prp-core-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 | $0.00012 | $0.02539 |
| Opus 5 | $0.00006 | $0.01269 |
| Sonnet 5 | $0.00002 | $0.00508 |
| Haiku 4.5 | $0.00001 | $0.00254 |
Grade A, and why
prp-core-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 4d 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 — 409 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Create Feature PRP
Feature: $ARGUMENTS
Mission
Transform a feature request into a comprehensive implementation PRP through systematic codebase analysis, external research, and strategic planning.
Core Principle: We do NOT write code in this phase. Our goal is to create a battle-tested, context-rich implementation plan that enables one-pass implementation success.
Key Philosophy: Context is King. The PRP must contain ALL information needed for implementation - patterns, gotchas, documentation, validation commands - so the execution agent succeeds on the first attempt.
Planning Process
Phase 1: Feature Understanding
Deep Feature Analysis:
- Extract the core problem being solved
- Identify user value and business impact
- Determine feature type: New Capability/Enhancement/Refactor/Bug Fix
- Assess complexity: Low/Medium/High
- Map affected systems and components
Create User Story Format:
As a <type of user>
I want to <action/goal>
So that <benefit/value>
Phase 2: Codebase Intelligence Gathering
Use specialized agents and parallel analysis:
1. Project Structure Analysis
- Detect primary language(s), frameworks, and runtime versions
- Map directory structure and architectural patterns
- Identify service/component boundaries and integration points
- Locate configuration files (pyproject.toml, package.json, etc.)
- Find environment setup and build processes
2. Pattern Recognition (Use specialized subagents when beneficial)
- Search for similar implementations in codebase
- Identify coding conventions:
- Naming patterns (CamelCase, snake_case, kebab-case)
- File organization and module structure
- Error handling approaches
- Logging patterns and standards
- Extract common patterns for the feature's domain
- Document anti-patterns to avoid
- Check CLAUDE.md for project-specific rules and conventions
3. Dependency Analysis
- Catalog external libraries relevant to feature
- Understand how libraries are integrated (check imports, configs)
- Find relevant documentation in PRPs/ai_docs/ if available
- Note library versions and compatibility requirements
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.
- 4d ago First seen · 409 lines · 12 tokens per session scan A 5ae07fca0c04
prp-core-create is a command published in the GitHub repository mort-lab/excel-mcp (5 stars, last pushed 27d ago), licensed MIT. It adds 12 tokens to every session and 2,539 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.
Other commands, from other repositories
review-epo-claims
Analyze patent claims for EPO Art. 84 EPC compliance - clarity, conciseness, support by description.
instructions
Use the instructions command to print the CLI playbook and current server inventory for AI agents using a running 1MCP serve instance.
ingest-dev
Ingest a URL, directory, or file into your knowledge base.
quarry
Manage your quarry knowledge base.
remember-dev
Remember inline text content in your knowledge base.
resume
Auto-detect and resume any interrupted Plan Cascade task. Detects mega-plan, hybrid-worktree, or hybrid-auto context and routes to the appropriate resume command.