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Found 1,484 Skills
Data validation with quality scoring and quarantine for suspicious records. Validates incoming data without blocking the pipeline, enabling manual review of edge cases.
Maximally Endowed Graph Architecture — λ-calculus over bounded n-SuperHyperGraphs with grounded uncertainty, conditional self-duality, and autopoietic refinement. Use when (1) simple graphs insufficient (η<2), (2) multi-scale reasoning required, (3) uncertainty is structured not stochastic, (4) knowledge must self-refactor. Pareto-governed: complexity added only when simpler structures fail validation.
Professional Pydantic v2.12 development for data validation, serialization, and type-safe models. Use when working with Pydantic for (1) creating or modifying BaseModel classes, (2) implementing validators and serializers, (3) configuring model behavior, (4) handling JSON schema generation, (5) working with settings management, (6) debugging validation errors, (7) integrating with ORMs or APIs, or (8) any production-grade Python data validation tasks. Includes complete API reference, concept guides, examples, and migration patterns.
Conducts comprehensive requirements review including completeness validation, clarity assessment, consistency checking, testability evaluation, and standards compliance. Produces detailed review reports with findings, gaps, conflicts, and improvement recommendations. Use when reviewing requirements documents (BRD, SRS, user stories), validating acceptance criteria, assessing requirements quality, identifying gaps and conflicts, or ensuring standards compliance (IEEE 830, INVEST criteria). Trigger when users mention "review requirements", "validate requirements", "check requirements quality", "find requirement issues", or "assess BRD/SRS quality".
Creates commits with conventional format and validation. Use when committing changes or generating commit messages.
Security patterns for authentication, defense-in-depth, input validation, OWASP Top 10, LLM safety, and PII masking. Use when implementing auth flows, security layers, input sanitization, vulnerability prevention, prompt injection defense, or data redaction.
Full RPI lifecycle orchestrator. Research → Plan → Pre-mortem → Crank → Vibe → Post-mortem. One command, sequential skill invocations with human gates and hands-free validation. Triggers: "rpi", "full lifecycle", "end to end", "research to production".
Define a Proof of Life (PoL) probe—a lightweight validation artifact that surfaces harsh truths before expensive development. Use it to test hypotheses with minimal investment.
Use when skills fail to activate, produce errors, need structural validation, or the library needs a health audit. Diagnose YAML frontmatter, XML sections, config.json schema, file structure, and cross-skill dependencies.
Help developers write Services in accordance with project guidelines, following the best practices of the tRPC + Service + DAO architecture. Provide guidance on Service structure, dependency injection, error handling, code examples, templates, boilerplate code generation, and best practice validation. Use this when creating or refactoring Service files in the codebase.
Guidance for working with the Beltic KYA (Know Your Agent) ecosystem - a credential-based trust framework for AI agents. Use when: (1) Working in any Beltic repository (beltic-spec, beltic-cli, beltic-sdk, fact-python, kya-platform, wizard, nasa), (2) Implementing agent credential signing/verification, (3) Using @belticlabs/kya SDK or beltic-sdk Python, (4) Understanding agent safety certification, (5) Working with verifiable credentials for AI. Triggers on: Beltic CLI commands, agent credentials, HTTP message signatures (RFC 9421), safety scores, KYB tier verification, trust chain validation.
Pre/post-operation validation to detect missing components and prevent future issues