oh-my-skills

Build data ingestion + transformation pipelines on top of cocoindex v1.0.0 — a Python framework with a Rust engine for ultra-performant, incremental indexing (ETL, RAG, knowledge graphs, vector search). Use when authoring cocoindex v1.0.0 components — App / Environment / @lifespan / @fn / mount + TargetState reconciliation + connectors + ops. **This is a complete paradigm change from v0.3.37**; the old FlowBuilder/DataScope/DataSlice/flow_def API was wholly removed in v1.0.0 — do NOT mix vocabularies.

Builds apps on top of cognee v1.0.0, the knowledge-graph memory engine for AI agents. Use when ingesting text/files/URLs into persistent memory, building knowledge graphs, searching graph-backed memory with multiple SearchType modes, enriching graphs with memify/improve, scoping memory with datasets and node_sets, configuring LLM/embedding/ graph/vector backends, running custom task pipelines, tracing operations, decorating agent entrypoints with `agent_memory`, connecting to Cognee Cloud with `serve`, or visualizing the graph. Covers cognee/__init__.py exports: the V1 API (add, cognify, search, memify, datasets, prune, update, run_custom_pipeline, config, SearchType, visualize_graph, pipelines, Drop, run_startup_migrations, tracing) and the V2 memory-oriented API (remember, RememberResult, recall, improve, forget, serve, disconnect, visualize, agent_memory). Do NOT use for: cognee internals, the HTTP REST API (use cognee-mcp or the FastAPI server), non-cognee memory/RAG libraries.

Authors Storybook v10 stories using the consolidated `storybook` package import surface for React plus Vite. Use when writing or editing `*.stories.tsx`, `preview.ts`, or `.storybook/main.ts` files on a Storybook 10.3+ project, including CSF3 story syntax, `play` functions with `storybook/test`, `preview-api` hooks, theming, MDX doc blocks from `@storybook/addon-docs/blocks`, and the a11y, themes, and vitest addons. Covers the `@storybook/react-vite` framework, `@storybook/react` renderer, and `@storybook/builder-vite` layer model so defects can be located at the right layer. Do NOT use for first-time project setup, framework selection, or upgrade migration (those flows are covered by the official upgrade CLI). Do NOT generate CSF2 default-export story arrays — v10 uses CSF3 named exports with the `satisfies` meta pattern, and training data frequently shows the wrong `@storybook/*` sub-package import paths that v10 consolidated into the single `storybook` package.

Installs React 19 and Next.js 16 UI components, blocks, and pages from the uitripled shadcn-compatible registry (the react-shadcn variant). Use when composing landing pages, dashboards, or UI from 171 animated Framer Motion components via "npx shadcn@latest add @uitripled/COMPONENT_ID". Covers the registry catalog, CLI install paths, ThemeProvider and UILibraryProvider setup, shadcn/ui primitives, and the @uitripled/utils helpers. Do NOT use this skill for the deferred react-baseui or react-carbon variants (they will ship as separate skills), and do NOT generate barrel imports from the react-shadcn package — its src/index.ts file is empty by design.

Drift detection between skill and current source code. Use when the user requests to "audit a skill" or "audit skill" for drift.

Design a skill scope through guided discovery. Use when the user requests to "create a skill brief" or "brief a skill."

Cognitive completeness verification — quality gate before export. Use when the user requests to "test a skill" or "verify skill completeness."

Smart regeneration preserving [MANUAL] sections after source changes. Use when the user requests to "update a skill" or "regenerate a skill."

Compile a skill from a brief. Supports --batch for multiple briefs. Use when the user requests to "create a skill" or "compile a skill."

Consolidated project stack skill with integration patterns — code-mode (analyzes manifests) or compose-mode (synthesizes from existing skills + architecture doc). Use when the user requests to "create a stack skill."

Package for distribution and inject context into CLAUDE.md/AGENTS.md/.cursorrules. Use when the user requests to "export" or "package a skill."

Fast skill from a package name or GitHub URL — no brief needed. Use when the user requests a "quick skill" or "skill from URL" or "skill from package."

Pre-code stack feasibility verification against architecture and PRD documents. Use when the user requests to "verify a tech stack" or "verify stack."

Discover what to skill in a large repo and produce recommended skill briefs. Use when the user requests to "analyze source for skills" or "discover skill opportunities."

Drop a specific skill version or an entire skill — soft (deprecate) or hard (purge) with platform context rebuild. Use when the user requests to "drop" or "remove a skill."

Skill compilation specialist — the forge master. Use when the user asks to "talk to Ferris" or requests the "Skill Forge agent."

Improve architecture doc using verified skill data and VS feasibility findings. Use when the user requests to "refine skill architecture" or "improve architecture doc."

Rename a skill across all its versions — transactional copy-verify-delete with platform context rebuild. Use when the user requests to "rename a skill."

Initialize forge environment, detect tools, and set capability tier (Quick/Forge/Forge+/Deep). Use when the user requests to "set up" or "initialize the forge."

Use when builds data transformation flows on top of cocoindex — a Python framework with a Rust engine for ultra-performant, incremental data indexing (ETL, RAG ingestion, knowledge graphs, vector search). Covers the flow-building public API: FlowBuilder, DataScope, DataSlice, Flow, FlowLiveUpdater, sources, targets (Postgres/Qdrant/Neo4j/Pinecone/LanceDB/etc.), functions (SplitRecursively, SentenceTransformerEmbed, ExtractByLlm, EmbedText, ColPali), LlmSpec, index defs, settings, and runtime lifecycle. Use for authoring indexing flows, chunking+embedding pipelines, LLM extraction, and live updates. Do NOT use for authoring custom Rust engine components — users write flows in Python via pyo3 bindings.

Builds apps on top of cognee v0.5.8, the knowledge-graph memory engine for AI agents. Use when ingesting text/files/URLs into persistent agent memory, building knowledge graphs with entities and relationships, searching graph-backed memory with multiple search modes (GRAPH_COMPLETION, CHUNKS, SUMMARIES, TEMPORAL, CYPHER, CODING_RULES), enriching existing graphs with memify, scoping memory with datasets and node_sets, configuring LLM/embedding/graph/vector backends, running custom task pipelines, tracing cognee operations, or visualizing the resulting graph. Covers the top-level exports from cognee/__init__.py: add, cognify, search, memify, datasets, prune, update, run_custom_pipeline, config, SearchType, visualize_graph, and the tracing API. Do NOT use for: cognee internals (cognify task implementation, graph adapters), the HTTP REST API (use cognee-mcp or the FastAPI server instead), non-cognee memory or RAG libraries.