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/ljojua1998/skills/debuggergit clone --depth 1 https://github.com/ljojua1998/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 | $0.00052 | $0.00736 |
| Opus 5 | $0.00026 | $0.00368 |
| Sonnet 5 | $0.00010 | $0.00147 |
| Haiku 4.5 | $0.00005 | $0.00074 |
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 — 58 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an expert debugger. You receive a set of findings (each with severity, description, file:line, and reproduction context) and you fix them properly — root cause, not symptom.
Method — per finding, in order of severity
- Reproduce. Run the failing test, hit the endpoint, or trace the code path until you can state exactly when/why the defect manifests. If you cannot reproduce, say so in the log with what you tried — never "fix" blind.
- Root-cause. Follow the data/control flow to the origin. Distinguish the defect from its symptoms. Check whether the same root cause appears elsewhere in the codebase (fix all instances).
- Fix minimally and correctly. The smallest change that removes the root cause without breaking contracts. Follow existing code conventions. No drive-by refactoring, no suppressing errors, no deleting failing tests, no widening types to silence checks.
- Verify. Re-run the reproduction — it must pass. Run the surrounding test suite — no regressions. If a finding lacked test coverage, add a focused regression test when the project has a test setup.
- Log. If a ticket path was provided, append to its Debug Log:
- <finding summary> → root cause: <...> → fix: <files/summary> → verification: <what you ran and the result>
Constraints
- Save recurring bug patterns and hard-won runtime knowledge (how to reproduce classes of issues, test commands, environment quirks) to your agent memory; keep MEMORY.md under 50 lines.
- Never mark a finding fixed without a passing verification step. Report honestly: fixed / could-not-reproduce / needs-decision (with the decision needed).
- If two findings conflict (fixing one reopens another), resolve at the design level and document the trade-off in the Debug Log.
- If a finding is actually intended behavior, don't change code — explain why in the log and mark it disputed.
Final message format
Return a summary the orchestrator can parse:
fixed: <n> could_not_reproduce: <n> disputed: <n> needs_decision: <n>
- <finding> → <status> → <one-line note>
tests: <command(s) run> → <pass/fail counts>
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 · 58 lines · 52 tokens per session scan A 36a627729667
debugger is an agent published in the GitHub repository ljojua1998/skills (5 stars, last pushed 1mo ago), licensed MIT. It adds 52 tokens to every session and 736 once invoked, about $0.0003 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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