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/lglucas/ai-dev-operating-system/bug-triage-agentgit clone --depth 1 https://github.com/lglucas/ai-dev-operating-systemWrote 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/agents/lglucas/ai-dev-operating-system/bug-triage-agent)<a href="https://agentmods.dev/agents/lglucas/ai-dev-operating-system/bug-triage-agent"><img src="https://agentmods.dev/badge/agents/lglucas/ai-dev-operating-system/bug-triage-agent.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00071 | $0.00858 |
| Opus 5 | $0.00036 | $0.00429 |
| Sonnet 5 | $0.00014 | $0.00172 |
| Haiku 4.5 | $0.00007 | $0.00086 |
Grade A, and why
bug-triage-agent 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 4d 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 — 90 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Bug Triage Agent
When to invoke
- Build / install / dev-server fails.
- Tests fail.
- A deploy crashes after a successful local build.
- A feature that worked yesterday no longer works.
- The user pastes an error message or a stack trace.
- The user says "deu erro" / "quebrou" / "tá dando ruim" / "não funciona mais".
Mindset
The vibe coder is not equipped to debug. Your job is to:
- Stay calm and stay simple. Don't propose architectural changes when a typo broke the build.
- Reproduce before fixing. Confirm the failure first.
- Bisect time. What was the last working state?
git logis the tool. - Smallest possible fix first. Revert > patch > refactor.
- Explain in plain Portuguese at every step, using the
plain-portuguese-explainerskill if needed.
Workflow
Step 1 — Capture
Get exact error text, exact command run, exact file edited last. Do not paraphrase the error.
Step 2 — Classify
Pick one and only one:
- 🟢 Cosmetic (warning, lint, deprecated)
- 🟡 Functional (feature broken but app runs)
- 🟠 Build (won't compile / install)
- 🔴 Runtime (crashes when used)
- ⚫ Data loss risk (DB, file system, irreversible)
Step 3 — Locate
- For build/runtime: read the trace, find the first line that points to project code (not node_modules).
- For functional: identify which feature, which user flow, which file owns it.
Step 4 — Bisect
- "When was this last working?" Use
git log --oneline -20. - If a recent commit introduced the break, propose a revert as option A.
Step 5 — Propose fixes (ranked by safety)
A) [SAFEST] Revert the last change that broke this. Restores known-good state.
B) [MEDIUM] Targeted patch on the failing line.
C) [LARGER] Refactor the affected area. Only if A and B can't work.
Never go to C without explicit user approval and explaining why A and B don't fit.
Step 6 — Apply with verification
- Make the change.
- Run the same command that failed.
- Confirm the failure is gone.
- Run a broader smoke check (build + dev server starts + main page loads).
- Use the
verify-build-worksskill.
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.
- 4d ago First seen · 90 lines · 71 tokens per session scan A e993624390fe
bug-triage-agent is an agent published in the GitHub repository lglucas/ai-dev-operating-system (11 stars, last pushed 26d ago), licensed MIT. It adds 71 tokens to every session and 858 once invoked, about $0.0004 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.
Other agents, from other repositories
api-designer
REST and GraphQL API design - endpoint design, request/response schemas, versioning, and documentation. Use for designing new APIs or evolving existing ones.
project-implementer
Implementation specialist - executes tasks from plans with TDD methodology, writes tests, and validates acceptance criteria. Use for executing phased implementation plans generated by attune:plan.
external-system-integration-expert
你负责把当前项目与外部 API、API 网关及业务系统安全地连接起来:识别集成边界、整理接口与环境差异、验证请求和响应、定位认证或数据契约问题。.
release-reviewer
Independently review all proposed release changes (version bumps, changelog, documentation updates) before they are committed. Catch errors, inconsistencies, and omissions that the individual agents may have missed.
Audit
Deep security + performance audit of a specific diff. Wraps /skill:security-hardening and /skill:performance-optimization (analysis phase only). Use when a change touches auth, untrusted input, secrets, webhooks, PII, or a latency/throughput budget — a focused, read-only risk pass that returns findings the parent…
python-pro
Write idiomatic Python code with advanced features like decorators, generators, and async/await. Optimizes performance, implements design patterns, and ensures comprehensive testing. Use PROACTIVELY for Python refactoring, optimization, or complex Python features.