Explorador de repositórios somente leitura para orientação inicial, localização ampla entre arquivos ou quando uma pesquisa direta falhou. Pule quando o arquivo ou símbolo exato já estiver nomeado. Retorna apenas citações caminho:linha; suas leituras ficam fora do contexto principal.
O JuliusBrussee/caveman é uma habilidade de agente de IA projetada para explorar repositórios de código de forma eficiente, especialmente em situações de 'cold start' (quando se está conhecendo um novo repositório), para localização ampla de arquivos ou quando uma pesquisa direta por nome de arquivo ou símbolo não retorna resultados. Diferente de outras ferramentas que podem trazer muito conteúdo para o contexto principal, o caveman mantém suas leituras fora do contexto principal, retornando apenas citações concisas no formato caminho:linha. Isso o torna ideal para orientação inicial, rastreamento de dependências entre vários arquivos ou busca de padrões de código. A habilidade deve ser ignorada quando o arquivo ou símbolo exato já é conhecido, pois aí ferramentas de busca mais diretas são mais apropriadas. Suportado por agentes de IA como Claude Code, Cursor ou Codex, ele ajuda desenvolvedores a ganhar contexto rapidamente sem poluir a memória de trabalho do agente.
npx skills add JuliusBrussee/cavemanExplorador de repositórios somente leitura para orientação inicial, localização ampla entre arquivos ou quando uma pesquisa direta falhou. Pule quando o arquivo ou símbolo exato já estiver nomeado. Retorna apenas citações caminho:linha; suas leituras ficam fora do contexto principal.
This repo contains 13 individual skills — each has its own dedicated page.
Find and label every LLM workflow in the repository so Caveman Cloud groups spend by workflow instead of one bucket. Use for "discover workflows" or breaking LLM spend down by workflow.
Read-only review of Caveman Cloud evidence: cost, Cave Score, workflows, traces, latency, errors, routing, savings. Use when asked what Caveman found or where LLM spend goes.
Read-only repository explorer for cold-start orientation, broad cross-file localization, or when a direct search failed. Skip it when the exact file or symbol is already named. Returns path:line citations only; its reads stay out of main context.
Act on a Caveman learn report - review the ranked token sinks, apply cost-lowering fixes with per-edit consent, and report what those fixes returned. Use when asked to lower an agent's token cost, what caveman has saved, to trim a heavy CLAUDE.md, or to offload re-pasted context into cavemem.
Inspect Caveman Cloud's experiment lifecycle and block unsafe execution. Use when asked to start, approve, cancel, promote or roll back a Caveman experiment.
Turn a Caveman optimization observation into an operator-chosen candidate with a paired baseline evaluation. Use when asked to inspect or evaluate a Caveman optimization report. Needs explicit approval.
Wire a repository through the Caveman Cloud gateway so every LLM request is measured, with no behavior change. Use for "set up caveman" or adding LLM spend observability.
Diagnose ambiguous failures before editing. Use for unknown causes, intermittent behavior, performance regressions, or investigations needing evidence-ranked hypotheses.
Build feature work with high overbuilding risk. Use for new behavior, product slices, or integrations where repository reuse, strict scope, and an explicit stop condition matter.
Implement reversible compatibility-safe transitions. Use for schema, data, API, protocol, configuration, or dependency migrations requiring rollback and preservation proof.
Restructure code while preserving behavior. Use for extraction, consolidation, ownership moves, or cleanup where verification must bracket structural edits.
Fix bugs and small behavior changes at the narrowest responsible layer. Use when regression proof, preserved surrounding behavior, and task-relevant tests matter.
Prove existing work meets acceptance conditions without expanding scope. Use for validation-only tasks, completion checks, focused gate runs, and last-mile proof.
Extract frames or short clips from videos using ffmpeg.
Summarize or transcribe URLs, YouTube/videos, podcasts, articles, transcripts, PDFs, and local files.
Use when testing, fixing, or extending the OpenClaw Control UI GUI with Vitest + Playwright end-to-end checks, mocked Gateway WebSocket flows, mocked dashboard runs, screenshots/videos, or agent-verifiable browser proof.
Obra superpowers agent skills.
See, Understand, Act on video and audio. See- ingest from local files, URLs, RTSP/live feeds, or live record desktop; return realtime context and playable stream links. Understand- extract frames, build visual/semantic/temporal indexes, and search moments with timestamps and auto-clips. Act- transcode and normalize (codec, fps, resolution, aspect ratio), perform timeline edits (subtitles, text/image overlays, branding, audio overlays, dubbing, translation), generate media assets (image, audio...
End-to-end marketing campaign planning and execution. Covers audience research, positioning, campaign angle definition, landing page copy, email sequences, social posts, ad copy, short-form video scripts, and content calendars. Use as the orchestration layer for multi-channel product launches. Use when planning or executing a multi-channel product launch, or producing landing page, email, social, or ad copy.