Que fait caveman-manage ?
Inspectez le cycle de vie des expériences Caveman Cloud et bloquez les exécutions non sécurisées. Utilisez-le lorsqu'on vous demande de démarrer, approuver, annuler, promouvoir ou annuler une expérience Caveman.
Inspectez le cycle de vie des expériences Caveman Cloud et bloquez les exécutions non sécurisées. Utilisez-le lorsqu'on vous demande de démarrer, approuver, annuler, promouvoir ou annuler une expérience Caveman.
Ce skill permet aux agents IA, tels que Claude Code, Cursor ou Codex, de gérer en toute sécurité le cycle de vie des expériences Caveman Cloud. Il offre la capacité d'inspecter l'état actuel des expériences, de bloquer les exécutions dangereuses ou non autorisées, et d'exécuter des actions comme le démarrage, l'approbation, l'annulation, la promotion ou le rollback d'une expérience. Idéal pour les workflows DevOps et MLOps nécessitant une supervision automatisée, ce skill s'intègre directement aux invites des agents pour garantir que seules les actions approuvées sont effectuées, réduisant ainsi les risques d'erreurs humaines ou de déploiements non conformes.
npx skills add https://github.com/JuliusBrussee/caveman/tree/main/skills/caveman-manageInspectez le cycle de vie des expériences Caveman Cloud et bloquez les exécutions non sécurisées. Utilisez-le lorsqu'on vous demande de démarrer, approuver, annuler, promouvoir ou annuler une expérience Caveman.
This repo contains 12 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.
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.