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Found 3,156 Skills
Run the standard post-change validation flow after a fix, refactor, or new feature. Use when implementation work is done and you should validate the latest changes by invoking the repo's review skills, starting with review-changes and then repo-doc-maintainer, before giving the final close-out.
Pre-logic validation tool that proactively identifies logical flaws before providing answers, used for rationality verification in scenarios such as product recommendation, quotation, data analysis, etc. Use when: - Validate product price authenticity before product recommendation - Data analysis consistency check - Budget-plan matching validation - Unit conversion verification - Factual statement validation before making factual claims - Proactively find logic flaws Cross-references: content-extractor, long-form-writer, rss-feed, document-hub Part of UniqueClub toolkit. Learn more: https://uniqueclub.ai
UI and design review: evaluate visual quality, responsive behavior, accessibility, color/contrast, typography, layout consistency, and i18n readiness using browser-based validation against industrial standards.
[Hyper] Create and refactor AI-readable docs, instruction bases, runbooks, specs, and harness-ready rule packs for context, prompt, tool, eval, sourcing, safety, and validation workflows.
A valid skill used for testing frontmatter validation. Do NOT use for production workflows.
Assess construction data quality using completeness, accuracy, consistency, timeliness, and validity metrics. Automated validation with regex patterns, thresholds, and reporting.
Package a TeX/LaTeX project into a clean tarball or zip for arXiv upload: file selection, build-artifact exclusion, 00README.XXX generation, ancillary file organization, archive validation. Triggers on: "package for arXiv", "create arXiv tarball", "bundle submission", "zip for arXiv", "prepare arXiv upload", "arXiv submission archive". Companion to arxiv-preflight and arxiv-figures.
Use when a Head of Ops, Knowledge Manager, or TPM-Internal needs to author, validate, or clean up company SOPs and internal runbooks (procurement intake, vendor offboarding, incident-comms cascade, employee onboarding, expense reimbursement, system-access provisioning, customer-escalation playbook) — including 5W2H completeness checks (Who-What-When-Where-Why-How-HowMuch), cross-link and orphan-page validation across a sprawling Notion/Confluence/Obsidian wiki, KB ingestion + hygiene reporting, ops onboarding doc generation, and runbook step verification (named owner, expected duration, observable success signal, rollback path, escalation contact). Pairs Kaoru Ishikawa's 5W2H method, Atul Gawande's *The Checklist Manifesto*, ISO 9001, ITIL v4 Service Operation, FDA 21 CFR Part 211, and Google SRE Workbook runbook discipline with deterministic stdlib-only Python tools that score completeness, detect anti-patterns, and emit prioritized cleanup lists. Distinct from `engineering/llm-wiki` (Karpathy-style personal PKM second brain), `engineering-team/runbook-generator` (system-ops production debugging runbook), `project-management/*` (Jira/Confluence delivery + ticket tracking), and sibling `business-operations/process-mapper` (BPMN process *design*, while knowledge-ops is process *documentation*).
Type-safe React forms with React Hook Form and Zod validation. Use for form schemas, field arrays, multi-step forms, or encountering validation errors, resolver issues, nested field problems.
Plan a migration onto MotherDuck. Use when moving from Snowflake, Redshift, PostgreSQL, dbt-heavy stacks, or lakehouse tooling and the key decisions are target pattern, cutover slices, validation, rollback, and native-versus-DuckLake posture.
Apply when wiring validation, error handling, or framework adapters. Concentrate guards at system boundaries (CLI, config, network, external APIs); trust internal types and keep business logic in pure functions.
Build high-performance FastAPI applications with async routes, validation, dependency injection, security, and automatic API documentation. Use when developing modern Python APIs with async support, automatic OpenAPI documentation, and high performance requirements.