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Found 1,660 Skills
Navigate the stardust design pipeline — assess project state under `stardust/` and recommend the next design stage. Use when the user wants to check design-pipeline progress, doesn't know which stage to run next, asks a general question about `stardust/` artifacts without naming a specific stage, says `/stardust`, or asks about files under `stardust/` (brand, briefings, wireframes, prototypes) without a clear edit target.
Create, implement, deploy, and debug Adobe Runtime actions with consistent layout, validation, and error handling. Use this skill whenever the user needs to add actions to an App Builder project, understand action structure (params, response format, web/raw actions), configure actions in the manifest, use App Builder SDKs (State, Files, Events, database), deploy and invoke actions via CLI, debug action issues, or implement patterns such as webhook receivers, custom event providers, journaling consumers, large payload redirects, action sequence pipelines, and Asset Compute workers. Also trigger when users mention serverless functions in Adobe context, action logging, IMS authentication for actions, or cron-style scheduled actions.
Manage DocuSeal e-signature workflows from the terminal via the DocuSeal CLI - create templates from PDF/DOCX/HTML, send documents for signing, track submissions, and update submitters. Use when the user wants to run DocuSeal commands in the shell, scripts, or CI/CD pipelines. Always load this skill before running `docuseal` commands.
Workbench agent panel system — ef-edit CustomEvent pipeline, registry roll-up, selector grouping, and element property schema. Use when adding new GUI edit capture points, expanding the inspector schema, or continuing development of the EFAgentPanel feature.
Debug production render failures in telecine. Inspect render state in the database, Valkey queues, and Cloud Run logs. Restart failed renders, trace the render pipeline flow, and diagnose fragment-level failures.
Workbench agent panel system — ef-edit CustomEvent pipeline, registry roll-up, selector grouping, and element property schema. Use when adding new GUI edit capture points, expanding the inspector schema, or continuing development of the EFAgentPanel feature.
KWCode (天工开物) — a CLI coding agent optimized for local open-source models (8B-30B), featuring deterministic expert pipelines, BM25+AST code location, runtime debugging, and a self-improving flywheel — all running fully offline.
Neo4j Python Driver v6 — driver lifecycle, execute_query, managed and explicit transactions, async (AsyncGraphDatabase), result handling, data type mapping, error handling, UNWIND batching, connection pool tuning, and causal consistency. Use when writing Python code that connects to Neo4j via GraphDatabase.driver, execute_query, execute_read, execute_write, AsyncGraphDatabase, neo4j.Result, or RoutingControl. Package name is `neo4j` (not neo4j-driver) since v6. Python >=3.10 required. Does NOT handle Cypher query authoring — use neo4j-cypher-skill. Does NOT cover driver upgrades or breaking changes — use neo4j-migration-skill. Does NOT cover GraphRAG pipelines (neo4j-graphrag package) — use neo4j-graphrag-skill.
Ingests unstructured and semi-structured documents into Neo4j as a knowledge graph. Use when chunking PDFs, HTML, plain text, or Markdown; extracting entities and relationships from text with an LLM (SimpleKGPipeline, neo4j-graphrag); loading JSON via apoc.load.json; building Document→Chunk→Entity graph structures; or connecting LangChain/LlamaIndex document loaders to Neo4j. Covers neo4j-graphrag SimpleKGPipeline, LLM Graph Builder web UI, entity resolution, chunking strategies, and graph schema design for RAG pipelines. Does NOT handle structured CSV/relational import — use neo4j-import-skill. Does NOT handle GraphRAG retrieval after ingestion — use neo4j-graphrag-skill. Does NOT handle vector index creation — use neo4j-vector-search-skill.
Implements knowledge graphs for AI-enhanced relational knowledge. Covers ontology design, graph database selection (Neo4j, Neptune, ArangoDB, TigerGraph), entity extraction, hybrid graph-vector architecture, query patterns, and AI integration. Use when implementing knowledge graphs, designing ontologies, extracting entities and relationships, selecting a graph database, or building hybrid graph-vector search. Use for knowledge graph, ontology design, entity resolution, graph RAG, hallucination detection. For architecture selection and governance, use the knowledge-base-manager skill. For document retrieval pipelines, use the rag-implementer skill.
Expert AI/ML engineer specializing in machine learning model development, deployment, and integration into production systems. Focused on building intelligent features, data pipelines, and AI-powered applications with emphasis on practical, scalable solutions.
Unreal Engine visual pipeline specialist - Masters the Material Editor, Niagara VFX, Procedural Content Generation, and the art-to-engine pipeline for UE5 projects