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Found 1,577 Skills
Optimizing vector embeddings for RAG systems through model selection, chunking strategies, caching, and performance tuning. Use when building semantic search, RAG pipelines, or document retrieval systems that require cost-effective, high-quality embeddings.
Data pipelines, feature stores, and embedding generation for AI/ML systems. Use when building RAG pipelines, ML feature serving, or data transformations. Covers feature stores (Feast, Tecton), embedding pipelines, chunking strategies, orchestration (Dagster, Prefect, Airflow), dbt transformations, data versioning (LakeFS), and experiment tracking (MLflow, W&B).
LLM and ML model deployment for inference. Use when serving models in production, building AI APIs, or optimizing inference. Covers vLLM (LLM serving), TensorRT-LLM (GPU optimization), Ollama (local), BentoML (ML deployment), Triton (multi-model), LangChain (orchestration), LlamaIndex (RAG), and streaming patterns.
Vector database implementation for AI/ML applications, semantic search, and RAG systems. Use when building chatbots, search engines, recommendation systems, or similarity-based retrieval. Covers Qdrant (primary), Pinecone, Milvus, pgvector, Chroma, embedding generation (OpenAI, Voyage, Cohere), chunking strategies, and hybrid search patterns.
Building promotion cases, brag documents, tracking wins, and self-advocacy for career advancement. Use when preparing for promotions, documenting accomplishments, or building your case for advancement.
Use when defining ABM tiers, scoring logic, and coverage rules.
Comprehensive test automation specialist covering unit, integration, and E2E testing strategies. Expert in Jest, Vitest, Playwright, Cypress, pytest, and modern testing frameworks. Guides test pyramid design, coverage optimization, flaky test detection, and CI/CD integration. Activate on 'test strategy', 'unit tests', 'integration tests', 'E2E testing', 'test coverage', 'flaky tests', 'mocking', 'test fixtures', 'TDD', 'BDD', 'test automation'. NOT for manual QA processes, load/performance testing (use performance-engineer), or security testing (use security-auditor).
Reviews Elixir documentation for completeness, quality, and ExDoc best practices. Use when auditing @moduledoc, @doc, @spec coverage, doctest correctness, and cross-reference usage in .ex files.
Document Q&A with RAG using Supabase pgvector store.
Map papers from the core set to each outline subsection and write `outline/mapping.tsv` with coverage tracking. **Trigger**: section mapper, mapping.tsv, coverage, paper-to-section mapping, 论文映射, 覆盖率. **Use when**: structure 阶段（C2），已有 `papers/core_set.csv` + `outline/outline.yml`，需要确保每小节有足够支持论文再进入 evidence/writing。 **Skip if**: 还没有 outline（先跑 `outline-builder`）或 core set 还没收敛。 **Network**: none. **Guardrail**: 覆盖率可审计（避免所有小节重复用同几篇）；为弱覆盖小节留下明确补救方向（扩 query / 合并小节）。
Planner-pass coverage + redundancy report for an outline+mapping, producing `outline/coverage_report.md` and `outline/outline_state.jsonl`. **Trigger**: planner, dynamic outline, outline refinement, coverage report, 大纲迭代, 覆盖率报告. **Use when**: you have `outline/outline.yml` + `outline/mapping.tsv` and want a verifiable, NO-PROSE planner pass before writing. **Skip if**: you don't want any outline/mapping diagnostics (or you have a frozen/approved structure and will not change it). **Network**: none. **Guardrail**: NO PROSE; do not invent papers; only report coverage/reuse and propose structural actions as bullets.
Provides comprehensive guidance for AWS S3 including bucket creation, object storage, access control, and S3 management. Use when the user asks about AWS S3, needs to store files in S3, configure S3 buckets, or work with S3 storage.