Skills
100,928 packaged capabilities and playbooks for agents. Install any of them with one command.
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Carry the current repository change through a verified GitHub pull request merge into the requested base, or the repository's remote default branch when none is named, then hand the merged branch to clean-branches so its worktree and local branch are cleaned up with the project's declared cleanup commands. Always use this skill for an actual PR merge or its post-merge cleanup, including requests to inspect, finish, land, or merge an existing PR; create a PR and merge it; preserve meaningful commits; or clean up an already-merged worktree. Report an existing merge instead of duplicating it. A sweep of a repository's accumulated branches and worktrees belongs to clean-branches.
Evaluate and manage suppliers using weighted scorecards across quality, delivery, price, and service dimensions. Use this skill when the user needs to assess supplier performance, compare vendors for selection, design a supplier rating system, or manage supplier development — even if they say 'which supplier should we choose', 'rate our vendors', 'this supplier keeps delivering late', or 'build a vendor evaluation system'.
This skill should be used when the user asks to optimize, export, serve, compress, or accelerate ML inference. PROACTIVELY activate for: (1) latency, throughput, p95/p99, batching, concurrency, KV cache, memory, or cost issues, (2) quantization INT8/INT4, GPTQ, AWQ, bitsandbytes, pruning, sparsity, distillation, (3) ONNX export, ONNX Runtime, TensorRT, TorchScript, torch.compile, XLA, OpenVINO, Core ML, TFLite, (4) Triton, TorchServe, TF Serving, BentoML, Seldon, KServe configuration, (5) edge deployment, CPU/GPU/TPU/Inferentia serving. Provides: hardware-aware inference optimization and safe benchmarking.
Dress a model in a real garment or accessory from a flat-lay, hanger or mannequin photo (on-model / virtual try-on) while preserving fabric, colour, fit, prints and logos. Supports pose references, multiple body types and ghost-mannequin output. Use for 模特图, 上身图, 试穿, try-on, on-model photo, apparel and jewellery.
Shared foundations for building reusable data models in PostHog, on either of two stacks: PostHog-native data-warehouse views / materialized views (HogQL, via the view-* MCP tools), or an external dbt project (sources.yml + staging/marts + schema tests) run against your own or PostHog's managed warehouse. Read before authoring any specific business model — covers the PostHog-vs-dbt decision, the view-create → view-materialize → sync_frequency workflow and the HogQL column-aliasing rule, the dbt project skeleton and the "no native dbt integration" picture, warehouse joins and star-schema dimensions, currency conversion with convertCurrency(), and checking/registering models in the data catalog for reuse. Companion to modeling-revenue-metrics, modeling-conversion-metrics, modeling-activation-metrics, modeling-product-usage-metrics, and modeling-dimension-tables. Use when the user asks how to build a view, materialized view, or dbt model in PostHog, or which of the two stacks to use.
Detects and modernizes outdated Move V1 syntax, patterns, and APIs to Move V2+. Use when upgrading legacy contracts, migrating to modern syntax, or converting old patterns to current best practices. NOT for writing new contracts (use write-contracts) or fixing bugs.
Runs and analyzes molecular dynamics simulations using OpenMM and MDAnalysis. Covers system preparation with PDBFixer and OpenFF/GAFF2, force field choice (AMBER14, CHARMM36m, ff19SB), energy minimization, NVT/NPT equilibration, production MD, and trajectory analysis (RMSD, RMSF, protein-ligand contacts). Use when simulating a protein or protein-ligand system on GPU, assessing how a mutation affects protein dynamics, characterizing ligand binding mode, quantifying per-residue flexibility, or modeling membrane proteins and disordered proteins. Not for GROMACS or NAMD workflows, which the skill lists only as alternatives.
Style adaptation development guide for Mpx cross-platform output to RN (referred to as Mpx2RN or Mpx2DRN). Must be invoked when the user's question or context contains all three key elements of Mpx, RN, and styles, e.g. Mpx2RN style adaptation, Mpx2RN style errors. Should not be invoked when the user's question does not involve Mpx cross-platform output to RN or is unrelated to styles, e.g. Mpx mini program output issues, RN-related issues.
Generate a consistent multi-angle product image set (front, back, left/right side, top, 45°, detail macro, packaging, in-hand) with identical lighting and background, optionally combined into a grid. Use for 多角度图, 多视角套图, 360 view, angle set, gallery images.
After navigating and interacting in Cursor's built-in browser, use browser_network_requests to audit every fetch/XHR for failures, slowness, duplicate calls, and suspicious payloads. Use for API-heavy pages and after backend or client networking changes.
Python package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks. Supports various graph types (Directed, Undirected, Multigraphs) and features a vast library of standard graph algorithms. Use for network analysis, graph theory, social network analysis, biological networks, infrastructure networks, path finding, centrality measures, community detection, graph algorithms, shortest paths, PageRank, connectivity analysis, and routing optimization.
Use NeuroKit2 to build or audit reproducible research workflows for physiological time-series preprocessing, event/interval analysis, multimodal alignment, variability, and complexity. Trigger when code imports neurokit2 or needs its current APIs, schemas, and method-aware validation—not for diagnosis or device validation.
Analyze Neuropixels extracellular recordings end-to-end with SpikeInterface. Covers loading SpikeGLX/Open Ephys/NWB data, preprocessing, drift/motion correction, Kilosort4 (and CPU) spike sorting, quality metrics, and unit curation (threshold-based, model-based UnitRefine, and AI-assisted visual review). Use when working with Neuropixels 1.0/2.0 recordings, spike sorting, or extracellular electrophysiology analysis.
Autonomous senior-operator mode for AI agents that resolve tasks end to end without babysitting and never create new problems. The agent verifies every claim against real evidence (web search dated to the current month and year, the codebase, and available tools, MCPs, and CLIs); it never guesses, never fakes confidence, and never claims something is done without proof. It stays silent and keeps working, interrupting the user only on three stops, namely a destructive or irreversible action, a dead-end with no evidence after exhausting sources, or genuine ambiguity that changes the outcome. Output is short, literal, and human. Use when the user says "not-your-babysitter", "nanny mode", "work autonomously", "stop babysitting", or "no hand-holding", or wants an agent that solves problems on its own, especially hands-on engineering and operational tasks. Do not use when the user explicitly wants a tutorial, a verbose walkthrough, or open-ended brainstorming.
(NS) Reverse-engineer a brownfield/legacy codebase into a technology-agnostic business description — rules, actors, permissions, lifecycles, integrations (no implementation detail). Use whenever the user mentions brownfield, legacy, modernization, reverse engineering, engenharia reversa, or extracting business rules from code. Do NOT use for code review, bug fixes, or technical architecture docs.
Bootstrap and operate an Obsidian vault with official Obsidian CLI first, then local filesystem/git fallback, then GitHub API fallback when local access is unavailable. Use when Codex needs first-run workspace init from a confirmed vault repo URL, project-scoped note reads and writes, vault health and readability audits, or structure cleanup in this vault. Verify current Obsidian CLI and Help syntax with Context7 instead of guessing, and use DeepWiki only for related public repo exploration.
USE FOR web search. Real-time web search for AI agents powered by Octen. Fast, fresh, and relevant — returns ranked results with highlights, full content, domain filtering, and time filtering. Average response under 80ms with minute-level index freshness. Also supports broad (multi-query) search that fans one question into several sub-queries for comprehensive coverage.
Securely inspect and automate microscopy data workflows against OMERO.server with omero-py, BlitzGateway, OMERO CLI, tables, annotations, ROIs, rendering, and documented OMERO.web APIs. Use for scoped OMERO inventory, metadata export, import/export planning, or reviewed write workflows.
Author, review, migrate, simulate, and troubleshoot official Opentrons Python Protocol API v2 protocols for Flex and OT-2 robots. Use for robot-specific liquid handling, deck and labware setup, pipettes, modules, runtime parameters, liquid classes, and Opentrons App analysis. Use pylabrobot instead when one workflow must support multiple robot vendors.
Python API v2 for Opentrons OT-2/Flex liquid handlers: protocols as Python files with metadata and run(); control pipettes, labware, and modules (thermocycler, heater-shaker, magnetic, temperature). Simulate via opentrons_simulate then upload. Use PyLabRobot for vendor-agnostic scripts (Hamilton, Tecan).
Take a judge live on production traffic — create an Opik online evaluation rule (LLM-as-judge or Python metric) on a project with sampling, filters, variable mapping, and a cost cap, then confirm new traces are being scored. Works over the SDK's REST client; reads rules and score names via the MCP when connected. Returns the rule, the score name it emits, and how to watch it. Use for "score production traces", "monitor hallucinations in prod", "take this judge live", "set up an online evaluation rule", "alert me when quality drops". Not for offline experiments (use evaluate or compare) or for finding what is already broken (use diagnose).
Improve a prompt with the Opik Agent Optimizer — resolve the prompt, a dataset, and a metric, pick the algorithm, run a bounded optimization, check the gain on held-out data, and save the winner as a new prompt version with the optimization run link. Runs via the opik-optimizer package; reads prompts and datasets via the MCP when connected. Use for "optimize this prompt", "improve my system prompt", "make the agent answer better", "tune the prompt against my dataset", "run the prompt optimizer". Not for measuring quality once (use evaluate), before/after on a suite (use compare), or hand-editing a prompt without data.
Decide ship or hold for a candidate from the compare skill's numbers, against an explicit release policy — regressions, pass rate, safety-tagged cases, subgroup consistency, latency and cost budgets, flakiness, evidence size, and whether the judge is validated. Reads two experiments on an Opik test suite via the SDK (or the MCP when connected) and returns a verdict with every criterion shown pass/fail. Use for "is this safe to ship", "can I merge this", "go/no-go on this change", "gate this release", "should I roll this out". Not for producing the numbers (use compare), building an evaluation (use evaluate), or deploying anything
Use the public `orca` CLI to operate Orca-managed worktrees, folder contexts, terminals, repos, automations, worktree comments, and the browser embedded inside the Orca app. Use when the user says "$orca-cli", "use orca cli", "Orca worktree", "child worktree", "cardStatus", "spawn codex/claude in a worktree", "read/wait/send Orca terminal", "terminal send", "full handoff", "handover", "give this to another agent", "another worktree", "Orca browser", or "control the browser inside Orca". Prefer this over raw `git worktree`, ad hoc PTYs, Playwright, or Computer Use when the task touches Orca-managed state. Use Computer Use for browser windows, webviews, or desktop UI outside Orca's embedded browser.
Internal machine-result contract for headless agentic-workflow drivers. The executable source is @gtrabanco/agentic-workflow-schema: strict Envelope v2 for workflow-status, compact SkillOutcome v1 for driven work, compatibility parsing, and deterministic document snapshots. Not a menu entry.
Create grounded paid-media creative concepts, copy angles, refresh hypotheses, and measurable test briefs across advertising platforms. Use for ad copy, creative briefs, UGC concepts, headlines, creative fatigue, asset ideas, or a creative refresh.
Produce a read-only, evidence-based paid-media performance review across connected platforms or supplied exports. Use for weekly or monthly reports, scorecards, performance, CPA, ROAS, CTR, spend trends, pacing, or conversion-tracking health.
Generate a paid social creative brief from a whitelisted or Spark Ad creator post, covering hook analysis, messaging angle, audience targeting, caption variants, and placement recommendations. This skill should be used when turning a creator post into a paid ad brief, writing a creative brief for whitelisted content, briefing the paid team on creator content, generating Spark Ads briefs from organic posts, creating paid media briefs from influencer content, translating UGC into a paid social strategy, building a media buyer brief from a creator video, preparing whitelisted content for ad spend, or generating placement recommendations for boosted creator content. For adapting captions into ad copy variants, see paid-ad-copy-adapter. For organic repost captions, see organic-repost-caption-writer. For FTC compliance, see ftc-disclosure-spot-checker.
Reverse-engineer raw materials (Sparse idea, Dense idea, experimental log) from an existing AI research paper to build a benchmark case for evaluating paper-writing pipelines. Replicates the PaperWritingBench dataset construction procedure from arXiv:2604.05018 §3 / App. C. TRIGGER when the user asks to "build a benchmark case from this paper", "reverse-engineer raw materials", or "evaluate my pipeline against PaperWritingBench".
Computational pathology toolkit for whole-slide images (WSIs): load slides, extract tiles, stain normalization, nuclear segmentation, feature extraction, and ML training. Supports H&E and multiplex. For end-to-end pipelines from raw WSIs to quantitative outputs.
Covers local, research-only computational pathology with PathML 3.0.5: loading and tiling whole-slide images (OpenSlide, Bio-Formats), preprocessing and QC pipelines run via SlideData.run(), .h5path data management, multiplex image quantification, spatial graph construction (KNN, RAG, HACT), and bounded local ONNX model inference planning. Use when loading or tiling slides, building tissue-mask or stain pipelines, managing .h5path files and patient-level splits, quantifying CODEX or Vectra multiplex images, or building cell and tissue graphs. Not for clinical diagnosis or patient care decisions.
Diagnose and fix how slow a frontend feels and measures: optimistic UI for mutations, latency masking (skeletons, blur-up, preloading, staggering), request batching/deduplication, and Core Web Vitals (LCP/INP/CLS) remediation. Use when the user says 'the app feels slow', 'clicks feel laggy', 'improve performance', 'my Lighthouse score is bad', 'fix my Core Web Vitals / LCP / CLS', 'make saves feel instant', or when interactions visibly wait on the network. Produces a performance audit (findings ranked by user impact, with measured or user-reported evidence) plus the implemented fixes — optimistic mutation code, masking treatments, and network-layer improvements.
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.