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Found 920 Skills
Retrieve sector P/E ratios using Octagon MCP. Use when comparing company valuations to sector benchmarks, analyzing sector valuations across exchanges, and understanding market-wide valuation trends.
Agent-optimized CLI for Bluesky (ATProto) and X (Twitter). YAML in, YAML out, exit codes for automation. Use when the task involves posting, replying, reading feeds, searching, annotating URLs, or running a sync/check/dispatch agent loop across social platforms.
Owns the smoke test contract for an ML experiment: a small, diagnostic-by-construction pytest that fits the experiment's learner on a portion of the real `data/` source and predicts on a *disjoint* portion that deliberately carries **no pre-history buffer**. The assertion is structural — the number of predictions must equal the number of rows in the predict grid. A pipeline that loads-then-features-then-splits will silently drop the cold-start rows of the predict slice and the test will fail with a row-count mismatch; a pipeline that marks X early and references upstream history nodes from feature steps will pass trivially. The smoke test is the executable proof of the X-marker placement rule from `build-ml-pipeline`. TRIGGER when: `test-ml-pipeline` has dispatched here to write the smoke test for an approved experiment; `pytest tests/smoke/` is failing on row count; the user asks "why is the smoke test failing?"; a pipeline edit in `build-ml-pipeline` needs an executable proof; an experiment script changes the pipeline shape and the matching smoke test needs revisiting. SKIP when: the design note does not exist or is not yet approved (route to `iterate-ml-experiment`); the user is asking about a regression test or schema invariant (route to `regression-test-ml-pipeline` / `distribution-test-ml-pipeline` once those exist); the question is the *interpretation* of CV metrics, not predict-time correctness (route to `evaluate-ml-pipeline`). HOW TO USE: read the matching experiment's `journal/NN_*.md` and `experiments/NN_*.py` first to understand the pipeline's source binding (what env-dict keys does `build_learner` expect?). Then construct two env-dicts from the **real `data/` source** — a train env and a predict env — such that the predict env carries *only the rows we want predictions for* and *no pre-history buffer*. The hard assertion is that the prediction count matches the predict-env row count exactly. The soft assertion is that the smoke set's MAE is within `3 × CV_mean` (or the task-appropriate analogue). **Do not write the design note or run CV — that's other skills' job.**
Run OpenMMDL molecular dynamics workflows via the FastFold Workflows API (`openmmdl_v1`) from local topology + optional ligand files, prepare draft scripts, execute drafts, wait for completion, fetch artifacts/metrics, and extract trajectory frames. Use when users ask for OpenMMDL, protein-ligand MD, OpenMMDL script preparation, or `/openmmdl/results/<workflow_id>` reruns.
Clone/replicate websites into production-ready Next.js 16 code using Firecrawl MCP. Use when user asks to: clone website, vibe clone, replicate landing page, copy website design, rebuild this site, recreate this page, clone specific sections (hero, pricing, footer, etc). Triggers: "clone this website", "vibe clone [url]", "replicate this landing page", "rebuild this site in Next.js", "clone the hero section from [url]", "copy this design".
Customer-centric conversion rate optimization methodology based on "Making Websites Win" by Karl Blanks and Ben Jesson (Conversion Rate Experts). Use when optimizing websites, landing pages, funnels, improving conversion rates, analyzing why visitors don't convert, creating persuasive copy, designing A/B tests, auditing UX, or building customer-centric websites. Provides systematic CRO process, objection/counter-objection framework, and evidence-based optimization techniques.
Complete Valyu API toolkit for AI agents. Use this skill when asked to perform real-time search across web, academic, medical, transportation, financial sources, content extraction from URLs, AI-powered answers with citations, or comprehensive deep research reports.
Guide for implementing gRPC-based key-value store services in Python. This skill should be used when building gRPC servers with protobuf definitions, implementing KV store operations (Get, Set, Delete), or troubleshooting gRPC service connectivity. Applicable to tasks involving grpcio, protobuf code generation, and background server processes.
Guide for video analysis and frame-level event detection tasks using OpenCV and similar libraries. This skill should be used when detecting events in videos (jumps, movements, gestures), extracting frames, analyzing motion patterns, or implementing computer vision algorithms on video data. It provides verification strategies and helps avoid common pitfalls in video processing workflows.
Guidance for identifying and fixing security vulnerabilities in code. This skill should be used when asked to fix security issues, address CVEs or CWEs, remediate vulnerabilities like injection attacks (SQL, command, CRLF, XSS), or when working with failing security-related tests.
Guidance for text embedding retrieval tasks using sentence transformers or similar embedding models. This skill should be used when the task involves loading documents, encoding text with embedding models, computing similarity scores (cosine similarity), and retrieving/ranking documents based on semantic similarity to a query. Applies to MTEB benchmark tasks, document retrieval, semantic search, and text similarity ranking.
Search Yelp for local businesses, get contact info, ratings, and hours. Use when finding services (cleaners, groomers, restaurants, etc.), looking up business phone numbers to text, or checking ratings before booking. Triggers on queries about finding businesses, restaurants, services, or "look up on Yelp".