Agent Skills

lead-research-assistant

★ 66,917

by composiohq

Identifies high-quality leads for your product or service by analyzing your business, searching for target companies, and providing actionable contact strategies. Perfect for sales, business development, and marketing professionals.

slack

★ 37,903

by vercel-labs

Interact with Slack workspaces using browser automation. Use when the user needs to check unread channels, navigate Slack, send messages, extract data, find information, search conversations, or automate any Slack task. Triggers include "check my Slack", "what channels have unreads", "send a message to", "search Slack for", "extract from Slack", "find who said", or any task requiring programmatic Slack interaction.

recsys-pipeline-architect

★ 37,559

by wshobson

Design composable recommendation, ranking, and feed pipelines using the six-stage Source→Hydrator→Filter→Scorer→Selector→SideEffect framework popularized by xAI's open-sourced X For You algorithm. Use when building any system that picks "the top K items for a (user, context)" — content feeds, search ranking, RAG rerankers, task prioritizers, notification triage, ad selection.

similarity-search-patterns

★ 37,559

by wshobson

Implement efficient similarity search with vector databases. Use when building semantic search, implementing nearest neighbor queries, or optimizing retrieval performance.

vector-index-tuning

★ 37,559

by wshobson

Optimize vector index performance for latency, recall, and memory. Use when tuning HNSW parameters, selecting quantization strategies, or scaling vector search infrastructure.

rag-implementation

★ 37,559

by wshobson

Build Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search. Use when implementing knowledge-grounded AI, building document Q&A systems, or integrating LLMs with external knowledge bases.

embedding-strategies

★ 37,559

by wshobson

Select and optimize embedding models for semantic search and RAG applications. Use when choosing embedding models, implementing chunking strategies, or optimizing embedding quality for specific domains.

hybrid-search-implementation

★ 37,559

by wshobson

Combine vector and keyword search for improved retrieval. Use when implementing RAG systems, building search engines, or when neither approach alone provides sufficient recall.

qdrant-monitoring-debugging

✓★ 36,202

by github

Diagnoses Qdrant production issues using metrics and observability tools. Use when someone reports 'optimizer stuck', 'indexing too slow', 'memory too high', 'OOM crash', 'queries are slow', 'latency spike', or 'search was fast now it's slow'. Also use when performance degrades without obvious config changes.

qdrant-search-quality-diagnosis

✓★ 36,202

by github

Diagnoses Qdrant search quality issues. Use when someone reports 'results are bad', 'wrong results', 'not relevant results', 'missing matches', 'recall is low', 'approximate search worse than exact', 'which embedding model', or 'quality dropped after quantization'. Also use when search quality degrades without obvious changes.

qdrant-indexing-performance-optimization

✓★ 36,202

by github

Diagnoses and fixes slow Qdrant indexing and data ingestion. Use when someone reports 'uploads are slow', 'indexing takes forever', 'optimizer is stuck', 'HNSW build time too long', or 'data uploaded but search is bad'. Also use when optimizer status shows errors, segments won't merge, or indexing threshold questions arise.

qdrant-minimize-latency

✓★ 36,202

by github

Guides Qdrant query latency optimization. Use when someone asks 'search is slow', 'how to reduce latency', 'p99 is too high', 'tail latency', 'single query too slow', 'how to make search faster', or 'latency spikes'.

pinecone-rag

✓★ 36,202

by github

Build production RAG pipelines and persistent agent memory using Pinecone as the vector database backend. ALWAYS USE THIS SKILL when the user mentions Pinecone, wants to index documents for semantic search, build a retrieval-augmented generation system, store agent memory across sessions, implement hybrid search, or connect an LLM to a searchable knowledge base — even if they don't say "Pinecone" explicitly. Also use when the user asks about vector databases for RAG, namespace isolation for mult

qdrant-scaling-qps

✓★ 36,202

by github

Guides Qdrant query throughput (QPS) scaling. Use when someone asks 'how to increase QPS', 'need more throughput', 'queries per second too low', 'batch search', 'read replicas', or 'how to handle more concurrent queries'.

qdrant-search-speed-optimization

✓★ 36,202

by github

Diagnoses and fixes slow Qdrant search. Use when someone reports 'search is slow', 'high latency', 'queries take too long', 'low QPS', 'throughput too low', 'filtered search is slow', or 'search was fast but now it's slow'. Also use when search performance degrades after config changes or data growth.

qdrant-search-strategies

✓★ 36,202

by github

Guides Qdrant search strategy selection. Use when someone asks 'should I use hybrid search?', 'BM25 or sparse vectors?', 'how to rerank?', 'results are not relevant', 'I don't get needed results from my dataset but they're there', 'retrieval quality is not good enough', 'results too similar', 'need diversity', 'MMR', 'relevance feedback', 'recommendation API', 'discovery API', 'ColBERT reranking', or 'missing keyword matches

qdrant-sliding-time-window

✓★ 36,202

by github

Guides sliding time window scaling in Qdrant. Use when someone asks 'only recent data matters', 'how to expire old vectors', 'time-based data rotation', 'delete old data efficiently', 'social media feed search', 'news search', 'log search with retention', or 'how to keep only last N months of data'.

technical-job-search

✓★ 36,202

by github

Use this skill when a software engineer asks for help with job search tasks: parsing or analyzing a job description, tailoring a CV/resume, writing a cover letter, evaluating a job offer, or drafting a post-interview follow-up email. Do not activate for general career advice unrelated to an active job search action.

x-twitter-scraper

✓★ 36,202

by github

Build GitHub Copilot workflows with Xquik X API SDKs, REST endpoints, MCP tools, TweetClaw OpenClaw plugin installs, signed webhooks, tweet search, user lookup, follower exports, media actions, and agent automation.

doublecheck

✓★ 36,202

by github

Three-layer verification pipeline for AI output. Extracts verifiable claims, finds supporting or contradicting sources via web search, runs adversarial review for hallucination patterns, and produces a structured verification report with source links for human review.

flowstudio-power-automate-mcp

✓★ 36,202

by github

Foundation skill for Power Automate via FlowStudio MCP — auth setup, the reusable MCP helper (Python + Node.js), tool discovery via `list_skills` / `tool_search`, and oversized-response handling. Load this skill first when connecting an agent to Power Automate. For specialized workflows, load `flowstudio-power-automate-build`, `flowstudio-power-automate-debug`, `flowstudio-power-automate-monitoring` (Pro+), or `flowstudio-power-automate-governance` (Pro+) — each contains the workflow narrative,

make-repo-contribution

✓★ 36,202

by github

All changes to code must follow the guidance documented in the repository. Before any issue is filed, branch is made, commits generated, or pull request (or PR) created, a search must be done to ensure the right steps are followed. Whenever asked to create an issue, commit messages, to push code, or create a PR, use this skill so everything is done correctly.

onboard-context-matic

✓★ 36,202

by github

Interactive onboarding tour for the context-matic MCP server. Walks the user through what the server does, shows all available APIs, lets them pick one to explore, explains it in their project language, demonstrates model_search and endpoint_search live, and ends with a menu of things the user can ask the agent to do. USE FOR: first-time setup; "what can this MCP do?"; "show me the available APIs"; "onboard me"; "how do I use the context-matic server"; "give me a tour". DO NOT USE FOR: actually

qdrant-search-quality

✓★ 36,202

by github

Diagnoses and improves Qdrant search relevance. Use when someone reports 'search results are bad', 'wrong results', 'low precision', 'low recall', 'irrelevant matches', 'missing expected results', or asks 'how to improve search quality?', 'which embedding model?', 'should I use hybrid search?', 'should I use reranking?'. Also use when search quality degrades after quantization, model change, or data growth.

sponsor-finder

✓★ 36,202

by github

Find which of a GitHub repository's dependencies are sponsorable via GitHub Sponsors. Uses deps.dev API for dependency resolution across npm, PyPI, Cargo, Go, RubyGems, Maven, and NuGet. Checks npm funding metadata, FUNDING.yml files, and web search. Verifies every link. Shows direct and transitive dependencies with OSSF Scorecard health data. Invoke with /sponsor followed by a GitHub owner/repo (e.g. "/sponsor expressjs/express").

postgresql-optimization

✓★ 36,202

by github

PostgreSQL-specific development assistant focusing on unique PostgreSQL features, advanced data types, and PostgreSQL-exclusive capabilities. Covers JSONB operations, array types, custom types, range/geometric types, full-text search, window functions, and PostgreSQL extensions ecosystem.

qdrant-scaling-query-volume

✓★ 36,202

by github

Guides Qdrant query volume scaling. Use when someone asks 'query returns too many results', 'scroll performance', 'large limit values', 'paginating search results', 'fetching many vectors', or 'high cardinality results'.

integrate-context-matic

✓★ 36,202

by github

Discovers and integrates third-party APIs using the context-matic MCP server. Uses `fetch_api` to find available API SDKs, `ask` for integration guidance, `model_search` and `endpoint_search` for SDK details. Use when the user asks to integrate a third-party API, add an API client, implement features with an external API, or work with any third-party API or SDK.

winmd-api-search

✓★ 36,202

by github

Find and explore Windows desktop APIs. Use when building features that need platform capabilities — camera, file access, notifications, UI controls, AI/ML, sensors, networking, etc. Discovers the right API for a task and retrieves full type details (methods, properties, events, enumeration values).

datapack-builder

✓★ 33,111

by anthropic

Build professional financial services data packs from various sources including CIMs, offering memorandums, SEC filings, web search, or MCP servers. Extract, normalize, and standardize financial data into investment committee-ready Excel workbooks with consistent structure, proper formatting, and documented assumptions. Use for M&A due diligence, private equity analysis, investment committee materials, and standardizing financial reporting across portfolio companies. Do not use for simple financ