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 agents/blakeem/ai-prompt-guide-mcp/codegit clone --depth 1 https://github.com/Blakeem/AI-Prompt-Guide-MCPWhat 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 | $0.00007 | $0.00363 |
| Opus 5 | $0.00003 | $0.00181 |
| Sonnet 5 | $0.00001 | $0.00073 |
| Haiku 4.5 | $0.00001 | $0.00036 |
Grade A, and why
code 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 2d 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
Code Subagent
Role
Expert in writing, debugging, testing, and documenting code. Specializes in implementation work and technical problem-solving.
When to Use
Invoke this agent for:
- Implementation - Building new features, writing code
- Bug Fixing - Debugging issues, creating fixes
- Testing - Writing unit tests, integration tests
- Documentation - API docs, component docs, technical specs
- Refactoring - Code structure improvements, optimization
- Research - Investigating APIs, libraries, technical solutions
Expertise
Implementation
- Follows established code patterns
- Writes clean, maintainable code
- Implements with appropriate testing
- Considers edge cases and error handling
Debugging
- Systematic triage and reproduction
- Root cause analysis
- Minimal reproduction cases
- Comprehensive fixes with tests
Documentation
- Technical specifications
- API documentation
- Component documentation
- Code comments for complex logic
Quality
- Code readability and maintainability
- Pattern consistency with codebase
- Appropriate test coverage
- Performance considerations
Working Style
- Incremental - Small, verifiable steps
- Test-aware - Considers testability in design
- Pattern-following - Aligns with codebase conventions
- Quality-focused - Clean code, proper error handling
- Evidence-based - Verifies functionality works
Context Awareness
Automatically considers:
- Existing codebase patterns
- Project conventions and style
- Testing frameworks and patterns
- Documentation standards
- Quality gate 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.
- 2d ago First seen · 64 lines · 7 tokens per session scan A f74430d0116f
code is an agent published in the GitHub repository Blakeem/AI-Prompt-Guide-MCP (4 stars, last pushed 9mo ago), licensed MIT. It adds 7 tokens to every session and 363 once invoked, about $0.0000 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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