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Found 2,210 Skills
Use when explaining code, concepts, or system behavior to a specific audience level - provides a structured explanation workflow with depth control and validation steps.
Select the right Proof of Life (PoL) probe based on hypothesis, risk, and resources. Use this to match the validation method to the real learning goal, not tooling comfort.
Registers and manages custom format validators in z-schema. Use when the user needs to add custom format validation, create sync or async format validators, register formats globally or per instance, validate emails or dates or phone numbers or custom business rules with format, configure formatAssertions for vocabulary-aware behavior, use customFormats option, list registered formats, handle async format timeouts, or understand how format validation differs across JSON Schema drafts.
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.
MongoDB schema design patterns and anti-patterns. Use when designing data models, reviewing schemas, migrating from SQL, or troubleshooting performance issues caused by schema problems. Triggers on "design schema", "embed vs reference", "MongoDB data model", "schema review", "unbounded arrays", "one-to-many", "tree structure", "16MB limit", "schema validation", "JSON Schema", "time series", "schema migration", "polymorphic", "TTL", "data lifecycle", "archive", "index explosion", "unnecessary indexes", "approximation pattern", "document versioning".
Design lean startup experiments (pretotypes) for a new product. Creates XYZ hypotheses and suggests low-effort validation methods like landing pages, explainer videos, and pre-orders. Use when validating a new product idea, creating pretotypes, or testing market demand.
Design experiments to test assumptions for an existing product — prototypes, A/B tests, spikes, and other low-effort validation methods. Use when validating assumptions, testing feature ideas cheaply, or planning product experiments.
Turn a refined research proposal or method idea into a detailed, claim-driven experiment roadmap. Use after `research-refine`, or when the user asks for a detailed experiment plan, ablation matrix, evaluation protocol, run order, compute budget, or paper-ready validation that supports the core problem, novelty, simplicity, and any LLM / VLM / Diffusion / RL-based contribution.
Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for JWT, JWS, and JWE validation paths, header parsing, key selection, claim acceptance, audience and issuer checks, role derivation, and token-to-identity confusion bugs. Use when the user asks to inspect JWT headers or claims, key lookup, `kid` handling, `alg` confusion, audience or issuer validation, role claims, or explain how a token becomes accepted identity or privilege. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.
Implement Syncfusion SfNumericTextBox for numeric input with formatting, validation, and customization in Windows Forms. Use when creating numeric input controls with currency formatting, percent values, number validation, or decimal formatting. Covers numeric formatting options, value range validation, and formatted numeric data entry with validation capabilities.
Build type-safe APIs with Hono for Cloudflare Workers, Deno, Bun, Node.js. Routing, middleware, validation (Zod/Valibot), RPC, streaming (SSE), WebSocket, security (CSRF, secureHeaders). Use when: building Hono APIs, streaming SSE, WebSocket, validation, RPC. Troubleshoot: validation hooks, RPC types, middleware chains, JWT verify algorithm required (v4.11.4+), body consumed errors.
This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.