AI DevKit · Implementation phase guidance for executing feature plans and checking implementation against design. Use when the user wants to implement planned tasks, update implementation docs, verify code matches design, or run dev-lifecycle phases 5 and 7.

Python OpenTelemetry style for Maple: module-scope tracers/meters, decorators for bounded work, error spans, OTLP-bridged logs via LoggingHandler + LoggingInstrumentor, inline endpoint + ingest key, and no helper-API wrappers.

Java OpenTelemetry style for Maple: zero-code Java agent or manual SDK with OTLP HTTP exporters, inline endpoint + ingest key, semconv resource attributes, OTLP-bridged Logback / SLF4J logs.

Effect-TS OpenTelemetry style for Maple via @maple-dev/effect-sdk: Maple.layer() bootstrap, Effect.withSpan / Effect.annotateCurrentSpan call sites, Effect.log for trace-correlated logging, server / browser / Cloudflare entry points.

Index of all COSS UI particle examples. Use when implementing UI features to find copy-paste-ready component patterns built on coss primitives. Each particle has a description and a JSON URL for easy installation.

Use when a user wants to deploy ClickHouse to the cloud, go to production, use ClickHouse Cloud, host a managed ClickHouse service, or migrate from a local ClickHouse setup to ClickHouse Cloud.

GitHub workflow for ToolUniverse - push code safely by moving temp files, activating pre-commit hooks, running tests, and cleaning staged files. Use when pushing to GitHub, fixing CI failures, or cleaning up before commits.

Small molecule identification, characterization, and procurement — PubChem, ChEMBL, BindingDB, ADMET-AI, SwissADME, eMolecules, Enamine. Covers compound name to structure to activity to ADMET properties to commercial sourcing. Use for chemical biology, lead identification, probe selection, and the full small-molecule discovery pipeline.

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.

Propose the mechanism by which a missense variant causes loss-of-function (LoF), synthesizing evidence from 5 independent layers: AlphaMissense pathogenicity, AlphaFold structural context, ESMC sequence likelihood, SAE feature disruption, and DynaMut2 stability ΔΔG. Distinguishes 'structural stability LoF' (mis-folding) from 'direct functional disruption' (catalytic / binding / PTM site damage). Use for coding missense variants where you need a mechanistic causal model, not just a pathogenicity score.

Lipid analysis and lipid-disease associations using LIPID MAPS classification, HMDB metabolite data, KEGG/Reactome lipid pathways (sphingolipid, eicosanoid, steroid, fatty acid), and PubChem chemical info. Use for lipid identification, lipid metabolism pathway mapping, and lipid-associated disease analysis (cardiovascular, diabetes, NAFLD).

KEGG-based disease-drug-variant network research. Connects diseases to causal genes, drugs to molecular targets, and variants to pathways using KEGG's editorially curated databases (KEGG Disease, Drug, Network, Variant, Pathway). Use for drug repurposing via shared pathways, mechanistic disease-gene-drug networks, and pathway-based target discovery. Distinguishes direct (binding) vs indirect (pathway co-membership) drug-target relationships.

Generate comprehensive disease research reports covering genetics (causal genes, GWAS, OMIM), pathways (Reactome, KEGG), drugs (existing therapies, repurposing candidates), clinical trials, epidemiology (prevalence, incidence), and phenotypes (HPO). Use for full disease overviews, comprehensive disease characterization, and orphan/rare-disease profiling.

Multi-omics integration — orchestrate per-layer analysis (transcriptomics, proteomics, epigenomics, genomics, metabolomics) then perform cross-omics correlation, multi-omics clustering, and pathway-level integration. Use for integrative systems-biology analysis, multi-modal disease characterization, and cross-omics biomarker discovery.

Inorganic chemistry, physical chemistry, and materials science — crystal structures, coordination chemistry, lattice parameters, thermodynamic properties, electronic structure. Use for unit cell volume calculations, coordination geometry, materials property estimation, and inorganic-mechanism reasoning. Complementary to tooluniverse-organic-chemistry.

Use when defining or modifying an MPS generation plan — explicit ordering of generators, checkpoints for cross-model reference resolution, forks for parallel branches, IncludePlan composition, conditional PlanContribution activation, ParameterEquals/ConceptListSelector fork selectors, and InitModelAttributes for targetFacet routing. Apply when working with @genplan models, the jetbrains.mps.lang.generator.plan language, attaching plans via DevKits or the Custom generation facet, or debugging cross-model mapping label resolution.

Creates a validation project for testing a specific vscode-cmake-tools pull request. Use this skill when asked to create a validation project, test a PR, validate a pull request, or set up a PR validation environment. The skill creates a structured project directory with documentation, metadata, and checklists for systematically validating PR changes.

get an env var, fetch a secret, missing env var, missing token/API key, load secrets from Infisical, infisical. Fetch secrets from the team's Infisical workspace into the shell environment so subsequent commands can use them.

Reviews vMCP code changes for known anti-patterns that make the codebase harder to understand or more brittle. Use when reviewing PRs, planning features, or refactoring vMCP code.

Finds flaky tests on the main branch by analyzing GitHub Actions failures, ranks them by frequency, and enters parallel plan mode to design deflake strategies. Use when you want to find and fix the flakiest tests.