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/codebygarv/ai-skills/bug-hunternpx skills add codebygarv/Ai-skills --skill bug-huntergit clone --depth 1 https://github.com/codebygarv/Ai-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 | $0.00043 | $0.00470 |
| Opus 5 | $0.00022 | $0.00235 |
| Sonnet 5 | $0.00009 | $0.00094 |
| Haiku 4.5 | $0.00004 | $0.00047 |
Grade A, and why
bug-hunter 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
Purpose
Hunt specifically for defects — not style, not maintainability, not architecture. The single question driving this skill is: "under what real input or timing, does this code produce a wrong result or crash?"
When to Use
- The user wants bugs found, specifically — not a general quality review.
- Before shipping code that handles money, user data, or concurrency, where a missed bug is expensive.
- Debugging: the user suspects a bug exists somewhere in a file/module but hasn't found it yet.
What to Analyze
- Boundary conditions — empty collections, zero, negative numbers, max values, single-element cases.
- Null/undefined/missing data — every place data is accessed, ask what happens if it's absent.
- Type coercion and comparison bugs — loose equality, implicit conversions, unit mismatches (ms vs. seconds, cents vs. dollars).
- Concurrency/race conditions — shared mutable state, async operations that assume ordering that isn't guaranteed, double-submit/double-click scenarios.
- Off-by-one and loop errors — inclusive/exclusive bounds, iterator invalidation.
- Incorrect assumptions — code that assumes an invariant (sorted input, unique IDs, non-empty list) that isn't actually enforced anywhere.
Output Format
- Each bug as its own entry: file/line, trigger condition (the specific input/timing that causes it), actual vs. expected behavior, suggested fix.
- Ordered by severity — data corruption/crash first, cosmetic/rare-edge-case issues last.
- No entry without a concrete triggering scenario — "this looks risky" without a scenario isn't a finding, it's a hunch; say so separately if worth a mention.
Avoid
- Reporting style or readability issues — that's Code Reviewer's job, not this skill's.
- Vague findings without a reproducing scenario.
- Flagging theoretical issues that the surrounding code already guards against (check the full context first).
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 36 lines · 43 tokens per session scan A 93cb3f27a45c
bug-hunter is a skill published in the GitHub repository codebygarv/Ai-skills (24 stars, last pushed 14d ago), licensed MIT. It adds 43 tokens to every session and 470 once invoked, about $0.0002 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 2026-08-30.
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