Borrowing it
Nothing to install: this file belongs to BACH-AI-Tools/pdf-reader-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/BACH-AI-Tools/pdf-reader-mcp/main/.claude/commands/mep.mdgit clone --depth 1 https://github.com/BACH-AI-Tools/pdf-reader-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/bach-ai-tools/pdf-reader-mcp/mep)<a href="https://agentmods.dev/commands/bach-ai-tools/pdf-reader-mcp/mep"><img src="https://agentmods.dev/badge/commands/bach-ai-tools/pdf-reader-mcp/mep/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/bach-ai-tools/pdf-reader-mcp/mep"><img src="https://agentmods.dev/badge/commands/bach-ai-tools/pdf-reader-mcp/mep.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 | $0.00011 | $0.00450 |
| Opus 5.5 | $0.00004 | $0.00180 |
| Sonnet 5.5 | $0.00002 | $0.00090 |
| Haiku 4.5 | $0.00001 | $0.00045 |
Grade A, and why
mep 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 2mo 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
MEP - Minimal Effective Prompt
Context
User's original prompt:
$ARGUMENTS
Your Task
Analyze the user's prompt above and refactor it into a Minimal Effective Prompt (MEP) that:
Remove Unnecessary Context
❌ Remove information that AI already knows:
- Current date/time (AI has access via hooks)
- System information (platform, CPU, memory - provided automatically)
- Project structure (AI can search codebase)
- Tech stack (AI can detect from package.json and code)
- File locations (AI can search)
- Existing code patterns (AI can search codebase)
Keep Essential Information
✅ Keep only what AI cannot infer:
- Specific business requirements
- User preferences or constraints
- Domain-specific knowledge
- Desired outcome or behavior
- Acceptance criteria
Apply MEP Principles
-
Be Specific About What, Not How
- ❌ "Create a React component with useState hook, useEffect for data fetching, proper error handling..."
- ✅ "Add user profile page with real-time data"
-
Trust AI's Knowledge
- ❌ "Using TypeScript with proper types, following our code style..."
- ✅ "Add user authentication" (AI will use TypeScript, follow existing patterns)
-
Focus on Intent
- ❌ "I need a function that takes an array and returns unique values using Set..."
- ✅ "Remove duplicate items from the list"
-
Remove Redundancy
- ❌ "Add comprehensive error handling with try-catch blocks and proper error messages..."
- ✅ "Add error handling" (comprehensive is default)
Output Format
Provide the refactored MEP prompt as a single, concise statement (1-3 sentences max) that captures the essence of the user's intent.
Original: [quote the original]
MEP Version: [your refactored minimal prompt]
Removed Context: [list what was removed and why - explain that AI already has this info]
Preserved Intent: [confirm the core requirement is maintained]
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.
- 2mo ago First seen · 72 lines · 11 tokens per session scan A e2ddf8364142
mep is a command published in the GitHub repository BACH-AI-Tools/pdf-reader-mcp (0 stars, last pushed 8mo ago), licensed MIT. It adds 11 tokens to every session and 450 once invoked, about $0.0000 per session on Opus 5.5. 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 2026-09-01.
Other commands, from other repositories
boot
Start a new PromptKit prompt assembly session (alias for /bootstrap).
prompt
System instructions for writing effective prompts. Apply when generating commands, skills, agents, or any LLM instructions.
music-suno-prompt
Grounded Suno prompt synthesis from local knowledge corpus + persona canon + label canon. No vibes-prompting.
prompt-show
Display full details of a saved prompt by ID.
ai
Load the Kaizen skill for production-ready AI agent implementation with signature-based programming and multi-agent coordination.
develop-image-prompt.eval
Generates a detailed image generation prompt from a document or content description. Good output: a prompt that is specific, visual, non-abstract, includes style/composition/lighting guidance, and is calibrated to the specified dimensions and style options.