Skill UI

The Santa Method is a multi-agent adversarial verification framework designed to improve output quality by requiring two independent reviewers (Agent B and Agent C) to pass the content before publication. It eliminates single-agent bias and systematically improves outputs through iterative refinement loops until convergence or manual escalation.

A comprehensive CI/CD quality gate checklist for Spring Boot projects. It guides developers through mandatory steps including building, static analysis (SpotBugs, PMD), running unit/integration tests, measuring code coverage, performing security scans (OWASP), and reviewing diffs before merging or deploying.

Provides a structured workflow for systematically accessing and analyzing official intellectual property data from the USPTO. This skill covers patent search (PatentSearch API), trademark status checks (TSDR), ownership transfer tracking, and building reproducible IP landscape logs. It is designed for data collection and record verification, emphasizing the use of official APIs and rigorous logging practices. (Warning: Not for legal advice.)

A comprehensive, multi-stage skill designed to simulate a professional Continuous Integration (CI) pipeline. It guides users through critical quality gates, including build verification, type checking, linting, running test suites (with coverage checks), security scanning for secrets, and final diff review. Use this skill before creating a Pull Request or finalizing any significant feature to ensure code robustness and adherence to best practices.

The Witness toolkit provides cryptographic signing and verification for all documented code fixes. It ensures that specific fix markers remain present across all releases, and crucially, it maintains a temporal history (JSONL) allowing users to pinpoint the exact commit that introduced a regression. Ideal for robust CI/CD pipelines and release gating.

This tool takes vague, rambling descriptions of desired outcomes and transforms them into a single, highly polished, autonomous prompt. Instead of building the deliverable, it crafts the comprehensive prompt (including quality bars, tool mandates, and verification loops) that a fresh AI session can execute completely on its own. Ideal for structuring complex, multi-step AI tasks.

Guides builders to verify task delivery by collecting required evidence such as CLI output, screenshots, or DOM snapshots, then submitting artifacts for review. Useful when a task must self-certify against a verify plan.

This guide provides a comprehensive onboarding process for RuView (WiFi-DensePose). It details three deployment tiers: Docker simulation for quick testing and UI exploration, source code build verification (Rust/Python), and live sensing setup utilizing ESP32 hardware. Ideal for new users or rapid prototyping.

This comprehensive tool provides a multi-stage verification pipeline for the RuView system. It executes full Rust workspace tests, runs a deterministic Python proof (Trust Kill Switch) to detect behavioral drift, and generates a detailed ADR-028 witness bundle. Use this before merging pull requests or when creating an official attestation package for recipients.

A comprehensive quality gate designed to validate all marketing content (ads, blogs, emails, etc.) before publication. It checks for factual accuracy (hallucination detection, claim verification), brand voice consistency, structural adherence, and compliance (C2PA provenance). Use this skill as the final checkpoint for all client-facing copy.

A comprehensive tool for importing structured data from various sources (CSV, JSON, Google Sheets) into connected platforms like CRMs, email marketing systems, or custom data stores. It manages the entire import pipeline, including automated field mapping, rigorous data validation, deduplication against existing records, and critical compliance verification (GDPR, CCPA). Features a mandatory execution gate for risk management.

This tool allows users to fine-tune the evaluation criteria for AI-generated content. Users can configure minimum scoring thresholds, adjust the relative weights of dimensions (such as brand voice, hallucination risk, and claim verification), and set composite auto-reject rules. It ensures the quality bar matches specific brand guidelines, industry needs, or content formats, making quality trade-offs explicit and adjustable.