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/bach-ai-tools/pdf-reader-mcp/mepgit 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.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 | $0.00011 | $0.00450 |
| Opus 5 | $0.00005 | $0.00225 |
| Sonnet 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 3d 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.
- 3d 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 6mo ago), licensed MIT. It adds 11 tokens to every session and 450 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 2026-09-01.
Other commands, from other repositories
prompt
System instructions for writing effective prompts. Apply when generating commands, skills, agents, or any LLM instructions.
prompt-show
Display full details of a saved prompt by ID.
prompt-optimize
Present the efficiency-versus-effectiveness frontier as labelled variants and let the user pick. TRIGGER WHEN: the user wants to review or optimize a prompt, system message, or agent instructions for clarity/tokens/reliability.
audit-prompt
Evaluate an existing prompt for clarity, effectiveness, and edge cases.
prompt-create
Create a new prompt following ground rules.
prompt-engineer
Interactive Prompt Engineer - collaborative refinement with best practices and clipboard copy.