Loading...
Loading...
Found 3,125 Skills
Grilling session that mines the user for fragments — heterogeneous nuggets of writing (claims, vignettes, sharp sentences, half-thoughts) — and appends them to a single document as raw material for a future article. Use when the user wants to develop ideas before imposing structure, or mentions "fragments", "ideate", or "raw material" for writing.
Azure Storage Services including Blob Storage, File Shares, Queue Storage, Table Storage, and Data Lake. Provides object storage, SMB file shares, async messaging, NoSQL key-value, and big data analytics capabilities. Includes access tiers (hot, cool, archive) and lifecycle management.
Models how much pipeline a team needs relative to quota - coverage ratios derived from win rates (raw multiplier, stage-weighted, conversion-inversion), segment-level coverage targets, in-quarter timing and the point of no return, seasonality indexing, and pipeline-gap math that turns a ratio into new-pipeline-required. A macro modeling exercise for sales leadership and RevOps, covering B2B and high-velocity/B2C motions. Use whenever the user mentions pipeline coverage, 3x pipeline, a pipeline gap, forecast shortfall, or "we missed quota with 4x coverage", even without the word coverage. Do NOT use for deriving the quota itself (mbfinotti/sales-skills@sales-quota-setting) or inspecting one deal (mbfinotti/sales-skills@deal-red-flags).
Build RAG (Retrieval Augmented Generation) pipelines with web search and LLMs. Tools: Tavily Search, Exa Search, Exa Answer, Claude, GPT-4, Gemini via OpenRouter. Capabilities: research, fact-checking, grounded responses, knowledge retrieval. Use for: AI agents, research assistants, fact-checkers, knowledge bases. Triggers: rag, retrieval augmented generation, grounded ai, search and answer, research agent, fact checking, knowledge retrieval, ai research, search + llm, web grounded, perplexity alternative, ai with sources, citation, research pipeline
S3-compatible object storage that branches with your Neon project, so files and the database stay in sync across every branch. Use when a user wants object storage, a bucket, blob/file storage, or somewhere to put uploads, images, documents, avatars, or user-generated files for their app or agent — especially when they already use (or are setting up) Lakebase Postgres and don't want to add a separate storage provider like AWS S3, Cloudflare R2, or Supabase Storage. Triggers include "object storage", "bucket", "blob storage", "file storage", "store uploads/images/files", "S3-compatible storage", "presigned URL", "where do I put files", "storage logs", "bucket logs", "Neon Object Storage", "Neon Storage", and "storage that branches with my database".
Azure Blob Storage SDK for Python. Use for uploading, downloading, listing blobs, managing containers, and blob lifecycle. Triggers: "blob storage", "BlobServiceClient", "ContainerClient", "BlobClient", "upload blob", "download blob".
General file/object storage, such as for images, videos, files, documents and other bulk data. Perfect fit for image galleries, video galleries, and other file or object management. Supports large files beyond IC limit, with browser-cached HTTP URL access.
Vercel data and storage services including Postgres, Redis, Vercel Blob, Edge Config, and data cache. Use when selecting data storage or caching on Vercel.
Analyze test coverage gaps. Use when user says "test coverage", "what's not tested", "coverage gaps", "missing tests", "coverage report", or "what needs testing".
Interact with Google Cloud Storage to manage buckets, objects, and access controls for scalable data storage.
Run pytest tests with coverage, discover lines missing coverage, and increase coverage to 100%.
Iterate on RAG systems with structured evals instead of eyeballing. This skill should be used when the user is tuning a RAG pipeline — changing retrieval prompts, swapping models, adjusting chunking, or debugging poor answers — and wants a cheap, ranked set of experiments with cost tracking and structured feedback on the stack. Also use when the user asks "how do I know if my RAG is working?", "this RAG eval is burning money", or "what should I try next on retrieval?".