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 skills/lgrappag/workflows-agents/ai-debugging-toolsnpx skills add LgrappaG/Workflows-Agents --skill ai-debugging-toolsgit clone --depth 1 https://github.com/LgrappaG/Workflows-AgentsWhat 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 | $0.00014 | $0.00257 |
| Opus 5 | $0.00007 | $0.00129 |
| Sonnet 5 | $0.00003 | $0.00051 |
| Haiku 4.5 | $0.00001 | $0.00026 |
Grade A, and why
ai-debugging-tools 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
Ai Debugging Tools
Create debugging tools for AI behavior visualization and analysis
Risk Level
LOW
Core Rules
- Optimize debug rendering
- validate visualizations
- Test thoroughly before deploying
Response Pattern
When Using This Skill
- Implement debug tools
- Validate the implementation
- Test edge cases and error conditions
- Ensure performance meets requirements
Usage Contexts
- AI debugging
- behavior analysis
What NOT to Do
- Performance impact
- incorrect visualizations
- Deploy without testing
Key Requirements
- Understand the use cases before application
- Follow the documented response pattern
- Validate results in the target environment
- Monitor for performance impact
Further Learning
Review related skills and documentation for deeper understanding of related systems and best practices.
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 · 49 lines · 14 tokens per session scan A 6c910dc8d284
ai-debugging-tools is a skill published in the GitHub repository LgrappaG/Workflows-Agents (2 stars, last pushed 4mo ago), licensed MIT. It adds 14 tokens to every session and 257 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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overall-planning
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practice-cognition
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refactor
Expert code refactoring based on Martin Fowler's catalog — improve maintainability without changing behavior. Covers code smells, composing methods, moving features, organizing data, simplifying conditionals, method calls, and generalization. Triggers on: refactor, 重构, clean up, improve code, code smell, extract…
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