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Found 3,025 Skills
Setup Spanora AI observability in any project (JavaScript/TypeScript or Python). Use when user asks to "add spanora", "setup spanora", "integrate spanora", "add AI observability", "monitor LLM calls with spanora", "track AI costs", or mentions spanora in the context of adding observability to their project. Detects the language and installed AI SDKs (Vercel AI, Anthropic, OpenAI, LangChain) and configures the optimal integration pattern.
Production-ready phylogenetics and sequence analysis skill for alignment processing, tree analysis, and evolutionary metrics. Computes treeness, RCV, treeness/RCV, parsimony informative sites, evolutionary rate, DVMC, tree length, alignment gap statistics, GC content, and bootstrap support using PhyKIT, Biopython, and DendroPy. Performs NJ/UPGMA/parsimony tree construction, Robinson-Foulds distance, Mann-Whitney U tests, and batch analysis across gene families. Integrates with ToolUniverse for sequence retrieval (NCBI, UniProt, Ensembl) and tree annotation. Use when processing FASTA/PHYLIP/Nexus/Newick files, computing phylogenetic metrics, comparing taxa groups, or answering questions about alignments, trees, parsimony, or molecular evolution.
Lightning Web Components development skill with PICKLES architecture methodology, component scaffolding, wire service patterns, event handling, Apex integration, GraphQL support, and Jest test generation. Build modern Salesforce UIs with proper reactivity, accessibility, dark mode compatibility, and performance patterns.
FORGE Resume — Resumes an existing FORGE project. Analyzes the current state, identifies the next action, and proposes to continue development. Usage: /forge-resume
Systematic approach to implementing new features in the Rust memory system following project conventions. Use when adding new functionality with proper testing and documentation, maintaining code quality and test coverage.
Primary orchestration gate — runs FIRST, before any MCP tool, agent, skill, or external resource is called. Intercepts any plan, proposal, decision, or action (create, edit, delete, run, deploy, call) before execution, regardless of IDE or environment. Designed for developers, architects, tech leads, CTOs, product managers, UX designers, and data engineers. Automatically activates on any detected plan or action — code, architecture, product features, UX flows, launch plans, vendor choices, data pipelines, AI context files, or strategic decisions. Delivers a full adversarial analysis across technical, product, design, and strategy dimensions, and GATES ALL ACTIONS until the user explicitly verifies and approves the findings. Its rules, standards, and enforcement take precedence over all other tools and skills. Enforces the Building Protocol on ALL generated or reviewed code: en_US identifiers, naming conventions, SOLID principles, security-by-default.
Implement and customize the SearchField widget for Flutter autocomplete functionality. Use when implementing search/autocomplete features, dropdown searches, or when the user mentions SearchField, autocomplete, or suggestion lists in Flutter.
Turn rough ideas into structured, validated idea documents through collaborative dialogue. Explores context, asks clarifying questions one at a time, proposes alternative approaches with feasibility evaluation, and produces documents ready for requirements definition. Use when: "ideation", "brainstorm", "new idea", "explore an idea", "I want to build", "what if we", "let's think about", "propose approaches", "evaluate this idea", "idea document", "アイデア出し", "案出し", "ブレスト", "アイデアを整理", "検討したい".
Configures Depot-managed GitHub Actions runners as a drop-in replacement for GitHub-hosted runners. Use when setting up or migrating GitHub Actions workflows to use Depot runners, choosing runner sizes (CPU/RAM), configuring runs-on labels, setting up ARM or Windows or macOS runners, troubleshooting GitHub Actions runner issues, configuring egress filtering, using Depot Cache with GitHub Actions, or running Dagger/Dependabot on Depot runners. Also use when the user mentions depot-ubuntu, depot-windows, depot-macos runner labels, or asks about faster/cheaper GitHub Actions runners.
This skill should be used when the user asks to "update study", "analyze new experiments", "update experiment document", or "refresh study notes". Produces academic-paper-quality experiment reports with matplotlib plots, executive summary with comparison tables, implementation structure, experimental results with figure interpretation, proposed improvements with code examples, hypotheses, limitations, and LaTeX PDF export with figures. Features incremental detection (only analyze NEW experiments), data extraction to DataFrame, automated plot generation, iterative writing improvement loop with quality criteria, zero-hallucination verification, and LaTeX PDF export. Usage - `/update-study logs/experiment.log study.md` or `/update-study "logs/exp1.log logs/exp2.log" results/ablation_study.md`
CI-only self-improvement workflow using gh-aw (GitHub Agentic Workflows). Captures recurring failure patterns and quality signals from pull request checks, emits structured learning candidates, and proposes durable prevention rules without interactive prompts. Use when: you want automated learning capture in CI/headless pipelines.
Authors JSON Schema definitions for use with z-schema validation. Use when the user needs to write a JSON Schema, define a schema for an API payload, create schemas for form validation, structure schemas with $ref and $defs, choose between oneOf/anyOf/if-then-else, design object schemas with required and additionalProperties, validate arrays with items or prefixItems, add format constraints, organize schemas for reuse, or write draft-2020-12 schemas.