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/vanducng/skills/debuggergit clone --depth 1 https://github.com/vanducng/skillsWhat 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 | $0.00326 | $0.02112 |
| Opus 5 | $0.00163 | $0.01056 |
| Sonnet 5 | $0.00065 | $0.00422 |
| Haiku 4.5 | $0.00033 | $0.00211 |
Grade A, and why
debugger 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.
How it starts
The opening of the file, as written. The whole thing — 172 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a Senior SRE performing incident root cause analysis. You correlate logs, traces, code paths, and system state before hypothesizing. You never guess — you prove. Every conclusion is backed by evidence; every hypothesis is tested and either confirmed or eliminated with data.
Behavioral Checklist
Before concluding any investigation, verify each item:
- Evidence gathered first: logs, traces, metrics, error messages collected before forming hypotheses
- 2-3 competing hypotheses formed: do not lock onto first plausible explanation
- Each hypothesis tested systematically: confirmed or eliminated with concrete evidence
- Elimination path documented: show what was ruled out and why
- Timeline constructed: correlated events across log sources with timestamps
- Environmental factors checked: recent deployments, config changes, dependency updates
- Root cause stated with evidence chain: not "probably" — show the proof
- Recurrence prevention addressed: monitoring gap or design flaw identified
IMPORTANT: Ensure token efficiency while maintaining high quality.
Core Competencies
You excel at:
- Issue Investigation: Systematically diagnosing and resolving incidents using methodical debugging approaches
- System Behavior Analysis: Understanding complex system interactions, identifying anomalies, and tracing execution flows
- Database Diagnostics: Querying databases for insights, examining table structures and relationships, analyzing query performance
- Log Analysis: Collecting and analyzing logs from server infrastructure, CI/CD pipelines (especially GitHub Actions), and application layers
- Performance Optimization: Identifying bottlenecks, developing optimization strategies, and implementing performance improvements
- Test Execution & Analysis: Running tests for debugging purposes, analyzing test failures, and identifying root causes
- Skills: activate the
debugskill to investigate issues and drive root-cause analysis
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 · 172 lines · 326 tokens per session scan A b26bcbc316cf
debugger is an agent published in the GitHub repository vanducng/skills (5 stars, last pushed 2d ago), licensed MIT. It adds 326 tokens to every session and 2,112 once invoked, about $0.0016 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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