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Found 1,668 Skills
Thin orchestrator for the end-to-end video localization pipeline. Routes to the four focused sub-skills — /wjs-transcribing-audio, /wjs-translating-subtitles, /wjs-dubbing-video, /wjs-burning-subtitles. Use when the user asks for full localization in one go ("帮我把这个西班牙语视频做成中文字幕+配音", "translate and dub this video", "做完整的本地化"). For any individual step (just transcribe, just translate, just dub, just burn), invoke the sub-skill directly — it's faster and the boundary is cleaner.
Track multiple live deals with milestones, deadlines, action items, and status updates. Maintains a deal pipeline view and surfaces upcoming deadlines and overdue items. Use when managing a book of business, tracking process milestones, or preparing for weekly deal reviews. Triggers on "deal tracker", "deal status", "where are we on", "process update", "deal pipeline", or "weekly deal review".
Technology-agnostic guidance for modular systems: bounded contexts, clear boundaries, composability, state isolation, explicit contracts, failure containment, scaffolding workflows, split/merge criteria, sub-units inside a context, and compliance review signals. Use when designing or reviewing module structure, service boundaries, package layout, cross-cutting dependencies, "how should we split this?", modularity assessments, coupling between domains, greenfield context design, or architecture discussions without assuming a specific framework, language, or repository layout. Do NOT use for executing the full Patterns 1–5 repo decomposition pipeline or per-pattern inventories (use modular-decomposition), phased extraction roadmaps as the main deliverable (use decomposition-planning-roadmap), or end-to-end legacy migration strategy (use legacy-migration-planner).
Build ETL pipelines and analytics dashboards for Harvard Art Museums API data using Python, SQL, and Streamlit
ETL pipeline and analytics application for Harvard Art Museums API with SQL storage and Streamlit visualization
End-to-end data engineering and analytics application using Harvard Art Museums API with ETL pipelines, SQL analytics, and Streamlit visualization
Daily briefings, pipeline snapshots, and win/loss analysis from the terminal — closing-this-week, open pipeline by stage/owner, and closed-won vs closed-lost over a period.
Eight-axis judgment code review for the current diff — Correctness, Simplification, Tests, Documentation, Style, Intent, Design/API, Performance (+ Coherence on metadata changes). Five-phase pipeline scope → deterministic tool battery (npx/uvx-preferred, zero-install for the JS + Python majority) → 8 parallel LLM axis reviewers → Haiku validators on sub-80 findings (verbatim rubric, ≥80 threshold) → synthesis with no-silent-drop + Conventional Comments JSONL. Every report closes with "What I did NOT check" (security → /security-review, runtime perf, flaky detection). Opt-in flags `--verify-build`, `--mutation-test`, `--reconcile`, `--apply-safe`. Public-skill posture — zero auto-install, graceful skip on missing native tools.
Full-stack AI content pipeline for automated research, script generation, Facebook posting, and video rendering using Claude/OpenAI and Remotion
Automate content creation from research to video generation using AI-powered content pipeline with Claude, OpenAI, and Remotion
End-to-end ETL pipeline for Harvard Art Museums API with SQL analytics and Streamlit visualization
Build end-to-end ETL pipelines with Harvard Art Museums API, SQL analytics, and Streamlit visualization