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/vmkteam/claude-plugins/investigatenpx skills add vmkteam/claude-plugins --skill investigategit clone --depth 1 https://github.com/vmkteam/claude-pluginsWrote 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/vmkteam/claude-plugins/investigate)<a href="https://agentmods.dev/skills/vmkteam/claude-plugins/investigate"><img src="https://agentmods.dev/badge/skills/vmkteam/claude-plugins/investigate.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 | $0.00061 | $0.01695 |
| Opus 5 | $0.00030 | $0.00847 |
| Sonnet 5 | $0.00012 | $0.00339 |
| Haiku 4.5 | $0.00006 | $0.00169 |
Grade A, and why
investigate 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 — 192 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/investigate — Расследование инцидента
Полное расследование инцидента. Задействует все доступные data source скиллы по стадии проекта.
Конкретные подключения из .claude/memory/project-index.md.
Триггер
- "API тормозит"
- "500 ошибки на /rpc/"
- "у пользователя не работает X"
Для полного workflow production-инцидента с HITL, mitigation и post-mortem — использовать
/incident.
- "что-то сломалось после деплоя"
Входные данные
- Описание проблемы (свободный текст)
- Временной диапазон (опционально, default: 1h)
- Проект/сервис (опционально, default: все)
Алгоритм
1. Определить scope
Из описания извлечь:
- Время: когда началось? default
statsPeriod=1h - Сервис: какой именно? Если неизвестно — все из project-index
- Endpoint/метод: конкретный RPC method или URL?
- Ключевые слова: для поиска в Sentry и логах
2. Проверить здоровье (скилл /api-health)
Первым делом — жив ли сервис?
pcurl @{api_prod_profile} https://{api_prod_host}/{rpc_endpoint} -s -L -X POST \
-H 'Content-Type: application/json' \
-d '{"jsonrpc":"2.0","method":"{known_method}","params":{},"id":1}' \
-w '\nHTTP %{http_code} | Total: %{time_total}s | TTFB: %{time_starttransfer}s\n'
Если не отвечает — сразу проверять Nomad (шаг 6).
3. Sentry: ошибки (скилл /sentry)
Параллельно:
- Unresolved issues за период, по частоте
- Новые issues (firstSeen в периоде)
- По ключевым словам из описания проблемы
Top-3 issues → получить latest event (stacktrace, breadcrumbs).
4. Prometheus: метрики (скилл /prometheus)
Параллельно:
- RPC error rate и HTTP 5xx
- Latency (avg по методу, top-10 медленных)
- Throughput (RPS) — сравнить с обычным уровнем
- Saturation: goroutines, memory, DB connections
5. Loki: приложенческие логи (скилл /loki)
- Ошибки по сервису (
level="ERROR") - По конкретному методу если известен
- Медленные запросы (
durationMS > 500) - Ошибки с текстом (
err!="<nil>")
6. Nomad: оркестрация (скилл /nomad)
Проверить — не инфраструктурная ли причина:
- OOM kills (
increase(nomad_client_allocs_oom_killed[{period}])) - Restarts (
increase(nomad_client_allocs_restart[{period}])) - Blocked allocations
- Node resources (CPU, memory)
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 · 192 lines · 61 tokens per session scan A b5565fe1996a
investigate is a skill published in the GitHub repository vmkteam/claude-plugins (7 stars, last pushed 4mo ago), licensed MIT. It adds 61 tokens to every session and 1,695 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.
Other skills, from other repositories
systematic-debugging
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brainstorming
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auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…