Skills
97,501 packaged capabilities and playbooks for agents. Install any of them with one command.
Data & databases
Interpret a missense variant via ESMC-6B Sparse Autoencoder (SAE) feature activations. For a given protein + variant, computes which interpretable SAE features (catalytic, ligand-binding, PTM, structural motif, domain, etc.) are lost or gained at the mutation site. Use when standard pathogenicity scores (AlphaMissense, ClinVar) say a variant is damaging but you need a MECHANISTIC explanation — e.g. 'why is this variant LoF?' Complements (does not replace) variant-interpretation and variant-to-mechanism skills, which focus on ACMG classification or regulatory mechanism.
Given a set of residues in a protein, explain WHY they are functionally critical by combining structural context (binding interface, ligand pocket, core, secondary structure), UniProt features (active sites, binding sites, PTM sites, disulfides), optional SAE feature evidence, and optional DMS data. Accepts residues from any source: DMS hotspots (top-K by max effect), ClinVar recurrent variants, literature-reported hot regions, evolutionarily conserved positions, or user-curated lists. Returns a per-cluster mechanism call: catalytic / ligand-binding / interface / structural-core / PTM / regulatory / unknown.
Venice billing and usage analytics - GET /billing/balance, GET /billing/usage-history (keyset-paginated per-request ledger, JSON or CSV), and GET /billing/usage-analytics (aggregated by date/model/key). All require an ADMIN key. GET /billing/usage was sunset on 2026-09-16 and now always returns 410. Covers the DIEM/EARNED_CREDITS/BUNDLED_CREDITS/USD consumption priority and building dashboards. (Beta)
Designs database schemas, indexing strategies, query optimization, and migration patterns for SQL and NoSQL databases. Use when designing tables, optimizing queries, fixing N+1 problems, planning migrations, or when asked about database performance, normalization, ORMs, or data modeling.
Produce and structure slow-query performance reports for PostHog's production ClickHouse (US and EU). Use when asked for a slow query report, query performance analysis over the last N days, per-team query cost, OOM or timeout investigation, cluster cost/memory regressions, or materialization candidates. Covers the modern `query_log_archive` source (typed `lc_*` columns, multi-day retention), how to categorize and attribute slow queries, root-cause patterns (unmaterialized JSONExtract, high-cardinality breakdowns, heavy joins), and the report structure. Runs queries via the `querying-production-databases-via-metabase` skill.
Use when the user wants Google Trends trend data through Frevana, including keyword interest over time, regional trend data, related queries, category-specific trends, country-specific trends, Google property filtering, language selection, date ranges, or timezone-aware trend results. Use this whenever the user asks to query Google Trends, compare trend interest, check search trend demand, or requests the Frevana endpoint.
Change how the Rerun viewer looks by writing a blueprint, for a recording or for a table. Read this whenever asked to change the look of the viewer, such as laying out a recording or dataset, designing a default blueprint, deciding which views show which entities, or making a segment or catalog table look nice by hiding columns, changing column order or filtering rows. Covers archetype-to-view mapping, layout reasoning, the rrb construction API, the contents grammar, table blueprints, and the screenshot loop.
Owns the `tests/` folder of an ML workspace and the pairing rule between an experiment and its tests. Lightweight router: every test category has its own subskill (`smoke-test-ml-pipeline` is the only one for v1; `regression-test-ml-pipeline` / `distribution-test-ml-pipeline` etc. plug in as siblings as the workspace grows). This skill places an empty `tests/<category>/` folder, enforces the stem-pairing rule between `tests/<category>/test_NN_<short_name>.py` and `experiments/NN_<short_name>.py`, and dispatches to the matching subskill when the user asks for a test. TRIGGER when: a new design note was just approved by `iterate-ml-experiment` and the matching test has to be drafted before the experiment can be marked done; the user asks "write the smoke test for `02`", "add the regression test", "do we have a test for X?"; an experiment script was edited and the paired test needs revisiting; about to run `pytest tests/` and one of the expected paired tests is missing. SKIP when: the design note does not yet exist (route to `iterate-ml-experiment` first); the test is for the package's internal helpers and unrelated to a specific experiment (regular unit tests live wherever the project's `pytest` config picks them up — out of scope here); the question is *what does the test result mean* rather than *should the test exist* (route to the matching subskill). HOW TO USE: this skill is a router. Read the dispatch table to figure out which subskill owns the test category the user is asking about, then hand off. Do not write the test body yourself — that belongs to the subskill. Do place the empty test file with the matching stem and the pytest scaffolding, then hand control over
Pharmacokinetic (PK) analysis of concentration-time data — non-compartmental analysis (NCA) for Cmax, Tmax, AUC (0-t and 0-∞), terminal half-life, clearance (CL), volume of distribution (Vd), MRT, and absolute bioavailability (F). Also one-compartment fitting. Use when you have plasma/serum drug concentrations over time after a dose and need PK parameters, or to compute bioavailability from IV + oral AUCs. NOT for ADMET property prediction from structure (use tooluniverse-admet-prediction).
Add knowledge retrieval (RAG) to AI agents using Twilio Enterprise Knowledge. Covers provisioning Knowledge Bases, uploading sources from web URLs, PDFs, and raw text, and running semantic search to retrieve relevant chunks at runtime. Use this skill to ground agent responses in your organization's actual approved content — FAQs, policies, product docs — rather than hallucinated answers.
Helps PostHog engineers and agents pick the right chart when a request is about visualizing change over time, before/after comparisons, or period-over-period movement while building UI with @posthog/quill-charts (dashboards, reports, the mcp_analytics frontend, custom visualizations). Use when a user asks how something "changed", "moved", "grew", "dropped", "improved", or "regressed" between two points or periods, or mentions "before/after", "period over period", "week over week", "delta", "slope graph", "slopegraph", or comparing many series across two snapshots. Surfaces both the `SlopeChart` quill component and the native `ChartDisplayType.SlopeGraph` insight display as options.
Design, create, inspect, update, attach, detach, preview, execute, and verify reusable Vapi Structured Outputs through public API or Server SDK workflows. Use for post-call extraction, typed call artifacts, AI-versus-regex extraction, JSON Schema design, backfilling existing calls, or retrieving structured results programmatically.
Write or update the user-facing posthog.com documentation for a PostHog Data warehouse import source. Use when adding a new source doc, fixing an inconsistent or stub source doc, or standardizing the docs at contents/docs/cdp/sources. Covers the canonical template, shared snippets, the auto-rendered <SourceParameters /> and <SourceTables /> components, frontmatter, and the docsUrl/slug rule that prevents 404s.
Analyze images using AI — segment objects, detect objects, extract text (OCR), describe images, and answer questions about images. Use when the user requests "Segment image", "Detect objects", "OCR", "Extract text from image", "Describe image", "What's in this image", or "Image analysis".
Invoke when the user asks about game economy, currency design, monetization, virtual currency, inflation, sink/source balance, F2P economy, premium currency, loot boxes, season pass, battle pass economics, dual currency, or resource flow modeling. Triggers on: "economy", "currency", "monetization", "F2P", "premium", "loot box", "battle pass", "sink/source", "inflation", "resource flow". Do NOT invoke for core gameplay mechanics (use game-designer) or legal advice on gambling laws (consult legal counsel). Part of the AlterLab GameForge collection.
When the user wants to define GTM metrics, build a metrics dashboard, measure pipeline efficiency, or track AI product performance. Also use when the user mentions 'GTM metrics,' 'revenue latency,' 'pipeline metrics,' 'TTFV,' 'time-to-first-value,' 'data health,' 'attribution,' 'conversion rate,' 'CAC,' 'LTV,' 'NRR,' 'GTM dashboard,' 'magic number,' 'pipeline velocity,' or 'funnel metrics.' This skill covers GTM measurement from metric selection through dashboard design, including AI-specific cost metrics, attribution models, and weekly review cadences.
Expert in Natural Language Processing, designing systems for text classification, NER, translation, and LLM integration using Hugging Face, spaCy, and LangChain. Use when building NLP pipelines, text analysis, or LLM-powered features. Triggers include "NLP", "text classification", "NER", "named entity", "sentiment analysis", "spaCy", "Hugging Face", "transformers"
Build search applications with OpenSearch from scratch. Use this skill when the user mentions search app, index setup, search architecture, semantic search, vector search, hybrid search, BM25, dense vector, sparse vector, agentic search, RAG, embeddings, KNN, PDF ingestion, document processing, or any related search topic. Activate even if the user says search quality, evaluation, nDCG, precision, relevance tuning, or search builder without mentioning OpenSearch.
Guide for migrating to Polar from other payment platforms (Stripe Billing, Paddle, Lemon Squeezy). Use this skill when: (1) Planning a migration from another payment provider to Polar; (2) Moving customers, subscriptions and saved cards with Polar's Stripe migration; (3) Running both billing systems during the transition; (4) Mapping products and pricing between platforms; (5) Updating webhook and access handling for migrated subscriptions; (6) Communicating the migration to customers; (7) Testing the migration in sandbox first.
Run read-only SQL against the local Postgres app database (SELECT, EXPLAIN, EXPLAIN ANALYZE on SELECT). Default local URL postgres://posthog:posthog@localhost:5432/posthog; else DATABASE_URL. Use when querying the local DB, inspecting tables, debugging data, or analyzing query plans. Mutations are strictly forbidden.
Generate professional reports — sprint retrospectives, financial summaries, analytics dashboards, and incident postmortems — from structured data with templates, charts, and multi-format output. Use when the user requests report generation or provides relevant inputs for this workflow.
Configures Simulink simulations non-destructively using SimulationInput objects — parameter overrides without modifying the model, batch sweeps via parsim, custom input signals via Dataset, and simulation data retrieval via logsout. Use when running sim()/parsim() with setVariable, setBlockParameter, setExternalInput, or when performing parameter sweeps and multi-run analysis. Not needed for one-shot simulations without configuration.
Use when the user wants to plan, design, or implement an A/B test or experiment, or build a growth experimentation program. Also use when the user mentions “A/B test,” “split test,” “experiment,” “test this change,” “variant copy,” “multivariate test,” “hypothesis,” “should I test this,” “which version is better,” “test two versions,” “statistical significance,” “how long should I run this test,” “growth experiments,” “experiment velocity,” “experiment backlog,” “ICE score,” “experimentation program,” or “experiment playbook.” Use this whenever someone is comparing two approaches and wants to measure which performs better, or when they want to build a systematic experimentation practice. For tracking implementation, see analytics-tracking. For page-level conversion optimization, see page-cro.
Query Chinese and global financial data using the AKShare Python library. Use when asked to (1) fetch stock quotes, historical prices, or financial statements for A-shares/HK/US stocks, (2) query macroeconomic data like GDP/CPI/PMI, (3) get futures/options/bond/forex/fund data, (4) look up index data, or (5) retrieve alternative data like news sentiment. Covers 500+ data interfaces for stocks, futures, options, bonds, forex, funds, macro, indexes and alternative data.
Counts and compares blog posts across a target and competitors, audits content freshness via sitemap lastmod dates, and maps competitor organic keywords (US, non-branded, informational) against the target to surface ranking gaps. Use when asked to compare blog counts, content volume, publishing cadence, content freshness, keyword mapping, keyword gap analysis, or competitor keyword reports — e.g. \"how many blog posts does X have vs Y\", \"how fresh is content on X\", \"where does [target] rank for [competitor] keywords\", \"keyword gap between X and Y\". Works from company names alone. Always use for blog comparison, freshness audits, and keyword mapping tasks.
Inventing deterministic metrics — turning a fuzzy property like 'maintainability', 'risk', or 'how reducible this code is' into a deterministic, computable number an agent can trust and optimize. Covers the path from construct to adoption — operationalizing the construct, confronting computability limits (Kolmogorov, Rice) with sound proxies, picking the right measurement scale, proving properties (monotonicity, invariance, the Weyuker/Briand axioms), guaranteeing determinism, establishing construct validity (not just LOC in disguise), and hardening against Goodhart-style gaming when an agent optimizes the metric. Trigger when designing, reviewing, or validating a quantitative metric, score, measure, or index — and even when the user doesn't say 'metric' but wants to quantify, score, rank, or measure code/behavior, build a deterministic optimization target, or invent a measure for something previously unquantified (e.g., behavior-preserving codebase-size reduction).
Reference for the Rust AI/ML library fandhe-ai. A from-scratch implementation independent of Burn / candle / tch. Includes tape(), tape_for, Tape, Gradients, Tensor, Var, LinearVars, autodiff, kernel fusion, FusionPlan, BackendOps, Device (Cpu / Cuda / Metal), DeviceParamStore, compat::array, compat::Sequential, nn::Module, optim (AdamW / GradScaler), data::DataLoader, ONNX import / export, safetensors, inference::generate / KvCache, and guardrail / self-repair CLI.
Searches the EHunt Etsy category data synced to the MCP library via `_ehunt_etsyCategorySearch` (MCP display name “Etsy品类查询”) for category names, ids, and parentIds, to find category ids for product or shop filtering. Trigger when users mention EHunt Etsy categories, Etsy category id, the Etsy category tree, `_ehunt_etsyCategorySearch`, Etsy品类查询, or looking up categories after syncEtsyCategory / _ehunt_syncEtsyCategory (Etsy category sync). Also trigger when users search by keyword for category ids in the locally synced Etsy category library, even if they do not mention EHunt.
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.