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Found 170 Skills
Dispatches many independent items in parallel: create a table, fan out to subagents, aggregate results. One row = one unit of work.
Use when working with multi-item data, batches, paginated APIs, rate-limited APIs, fan-out across multiple branches, anything that needs to "do this for each", or any time the user mentions looping, iterating, batching, paging, parallelism, or "loop over items". Triggers on "loop", "iterate", "for each", "batch", "page through", "paginate", "rate limit", "process all", "fan-out", "parallel branches", "concurrency", or any node that should run once vs once-per-item.
OMP adapter for poteto-mode. Maps canonical pstack roles and lifecycle protocols to current OMP task agents, batching, isolation, IRC follow-ups, and durable result resources.
Detect language of text with confidence scores, support for 50+ languages, and batch text classification.
Extract vendor, date, items, amounts, and total from receipt images using OCR and pattern matching with structured JSON output.
Explains core Apache Beam programming model concepts including PCollections, PTransforms, Pipelines, and Runners. Use when learning Beam fundamentals or explaining pipeline concepts.
Batch create professional certificates with customizable templates for courses, achievements, and awards. Supports CSV input and PDF export.
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.
Polish raw screenshots into LP-ready heroes.
Generates images with Codex (OpenAI's coding agent and its built-in image tool, included in a ChatGPT plan) - YouTube thumbnails, covers, B-roll stills, callout visuals, profile pictures - one Codex job per image, several in parallel, with the creator's photos as reference when the face matters, then copies them into the project with a manifest. Use when the user says generate an image, a thumbnail, a cover, make me a visual, codex images, génère une image, miniature, or when video-publish or video-edit needs an image.
Build RAG systems and semantic search with Gemini embeddings (gemini-embedding-001). 768-3072 dimension vectors, 8 task types, Cloudflare Vectorize integration. Prevents 13 documented errors. Use when: vector search, RAG systems, semantic search, document clustering. Troubleshoot: dimension mismatch, normalization required, batch ordering bug, memory limits, wrong task type, rate limits (100 RPM).
Provides strategies for efficiently transforming large text files (thousands to millions of lines) using text editors like Vim, sed, or awk. This skill should be used when tasks involve bulk text transformations, CSV manipulation at scale, pattern-based edits across massive files, or when keystroke/operation efficiency is constrained. Applicable to tasks requiring macros, regex substitutions, or batch processing of structured text data.