Loading...
Loading...
Found 1,834 Skills
Automated BRD generation pipeline from reference documents (docs/00_REF/ or REF/) or user prompts - analyzes sources, determines BRD type, generates content, validates readiness, creates BRD-00_index.md, and supports parallel execution
Receive and verify GitLab webhooks. Use when setting up GitLab webhook handlers, debugging token verification, or handling repository events like push, merge_request, issue, pipeline, or release.
Audits Python + BigQuery pipelines for cost safety, idempotency, and production readiness. Returns a structured report with exact patch locations.
Programmatic visual asset pipeline for proposal-context logos and images. Uses Recraft, OpenAI Image, and Nano Banana Pro together with phase-aware breadth vs convergence.
Advanced GitHub Actions workflow automation with AI swarm coordination, intelligent CI/CD pipelines, and comprehensive repository management
Schedule "research + content production" tasks in A/B/C levels. First define the audience, goal, carrier and perspective, then follow the Research→Synthesis→Content pipeline to output publishable content and evidence chains. It is suitable for writing tasks that require credible conclusions, stable structure and reusable material precipitation.
Cuantización de modelos ML a FP16/INT8 para reducir memoria y acelerar inferencia en el pipeline KYC
Guides teams through designing, implementing, and optimizing CI/CD pipelines, GitHub Actions workflows, deployment automation, and agentic workflow patterns. Provides production-ready templates, cost optimization strategies, quality gates, and multi-environment deployment planning for modern DevOps practices.
Rapidly scaffold and implement a playable game — no assets, design, audio, deploy, or monetize. Get something on screen fast. Use when the user says "quick game", "fast prototype", "just get something playable", or wants a game without the full pipeline. For the complete pipeline, use make-game instead. Do NOT use for production games (use make-game for the full pipeline).
Three-layer verification pipeline for AI output. Extracts verifiable claims, finds supporting or contradicting sources via web search, runs adversarial review for hallucination patterns, and produces a structured verification report with source links for human review.
Use this skill when the user wants to debug, diagnose, or systematically iterate on an experiment that already exists, or when they need a structured experiment log for tracking runs, hypotheses, failures, results, and next steps during active research. Apply it to underperforming methods, training that will not converge, regressions after a change, inconsistent results across datasets, aimless experimentation without progress, and questions like 'why doesn't this work?', 'no progress after many attempts', or 'how should I investigate this failure?'. Also use it for setting up practical experiment logging/record-keeping that supports debugging and iteration. Do not use it for designing a brand-new experiment pipeline or full experiment program (use experiment-pipeline), generating research ideas, fixing isolated coding/syntax errors, or writing retrospective summaries into research memory/notes/knowledge bases.
Use this skill when creating, editing, or optimizing video content for YouTube and other platforms. Triggers on script writing, video editing workflows, thumbnail design, YouTube SEO, content strategy, retention optimization, or channel growth. Covers the full production pipeline from ideation to publish - scriptwriting frameworks, editing pacing, thumbnail best practices, metadata optimization, and audience retention techniques.