av/harbor is a command-line tool and companion app that uses Docker Compose to start a connected local stack of language-model backends, user interfaces, and supporting AI services. People use it to run services such as Ollama, llama.cpp, vLLM, Open WebUI, search, voice, and image-generation tools without configuring their connections manually, while the catalogue provides agent workflows for operating Harbor.
Borrowing it
Nothing to install: this file belongs to av/harbor. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/av/harbor/main/.agents/skills/bughunt/SKILL.mdgit clone --depth 1 https://github.com/av/harborWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/av/harbor/bughunt)<a href="https://agentmods.dev/skills/av/harbor/bughunt"><img src="https://agentmods.dev/badge/skills/av/harbor/bughunt/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/av/harbor/bughunt"><img src="https://agentmods.dev/badge/skills/av/harbor/bughunt.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 10 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
What 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 5.1 | $0.00052 | $0.02743 |
| Opus 5.5 | $0.00021 | $0.01097 |
| Sonnet 5.5 | $0.00010 | $0.00549 |
| Haiku 4.5 | $0.00005 | $0.00274 |
Grade A, and why
bughunt 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 2mo 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- bughunt — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 209 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Bughunt
Fully autonomous pipeline: find, triage, fix, and audit bugs in a scoped area of a codebase. Five phases, each using subagents to parallelize work and provide independent assessments.
This skill runs end-to-end without asking the user any questions. Do not pause for confirmation, approval, or input between phases. Make autonomous decisions at every step — section splits, severity judgments, fix strategies, conflict resolution. Log progress to the report so the user can review the final output, but never block on user input.
Parameters
| Parameter | Default | Example override |
|---|---|---|
| Scope | (required) | the install script, src/auth/, the CLI argument parser |
| Sections | auto-split from scope | split by platform, one per module |
| Platforms | inferred from repo | Linux, macOS, WSL |
| Output | /tmp/bughunt-output/<project>/ |
Output directory: ./qa/ |
<project> is the basename of the repo root (e.g. /tmp/bughunt-output/harbor/). The output directory — and therefore prior-run memory — is always per-project; never read or write another project's reports.
Workflow
1. Orient Read the target code, identify natural section boundaries
2. Discover Parallel bugbash subagents, one per section
3. Triage Independent subagent per finding — confirm, dispute, or adjust severity
4. Fix Subagent per confirmed issue — investigate, implement, self-review
5. Audit Verify all fixes against repo constraints and target platforms
Phase 1 — Orient
Read the target files yourself. Identify section boundaries for parallel discovery. Do not delegate this phase — you need the full picture to write good subagent prompts in later phases.
Good splits follow the code's own structure:
- Per platform/backend — if the code branches by OS, distro, or provider
- Per layer — argument parsing, core logic, error handling, output formatting
- Per module — one subagent per file or logical unit
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.
- 2mo ago First seen · 209 lines · 52 tokens per session scan A e96a32c6b6bd
bughunt is a skill published in the GitHub repository av/harbor (3,263 stars, last pushed 6d ago), licensed Apache-2.0. It adds 52 tokens to every session and 2,743 once invoked, about $0.0002 per session on Opus 5.5. 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.
bughunt compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| bughuntav/harbor | Skill | 3.3k | 6d ago | Apache-2.0 | A | 52 | Claude Code, Codex |
| perfepicsagas/epic-harness | Skill | 20 | yesterday | Apache-2.0 | A | 32 | Claude Code |
| impactnikolay-e/diffctx | Skill | 6 | today | Apache-2.0 | A | 53 | Claude Code |
| sephera-change-safetyReim-developer/Sephera | Skill | 77 | 7d ago | GPL-3.0 | A | 80 | Claude Code, Codex |
| docker-managementfurkangonel/cowrangler | Skill | 1 | 1mo ago | MIT | C | 16 | Claude Code, Codex |
| printing-press-output-reviewmvanhorn/cli-printing-press | Skill | 4.9k | yesterday | MIT | A | 102 | Claude Code |
| smiles-validationsynthetic-sciences/openscience | Skill | 4.3k | 2d ago | Apache-2.0 | A | 24 | Claude Code, Codex |