Skill UI

This tool executes the advanced `metaharness/darwin` security benchmark (ADR-155). It rigorously evaluates the security detection capabilities of AI systems against a controlled ground-truth corpus, providing critical metrics like TPR, FPR, and patch-pass rates. It compares the champion model against multiple baselines, offering empirical data crucial for iterative security improvement and drift detection.

This utility is designed to reconcile the ADR (Architecture Decision Record) index when source files have been deleted or modified, preventing the accumulation of stale or dangling reference rows. Unlike simple upserts, this tool performs a hard purge of the entire index namespace (adr-patterns and adr-edges) and then fully rebuilds the index from the files currently present on disk. It is crucial for maintaining data integrity after deletions.

A comprehensive guide for configuring and deploying the RuView wireless sensing system. It covers firmware build-time settings (sdkconfig), runtime parameters (NVS provisioning), multi-node mesh setup, and integrating advanced edge intelligence modules (ADR-041). Essential for tuning system behavior and capabilities without modifying core code.

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.

This comprehensive guide details the rigorous, multi-stage peer review pipeline used by major academic conferences (like ACM CHI). It breaks down the roles of the 1AC, 2AC, and external reviewers, explaining screening mechanisms like Desk Reject (DR) and Assisted Desk Reject (ADR). It covers the entire journey from initial recommendations to the final PC meeting, providing authors with a framework to interpret feedback and strategically revise their work.

This guide provides a comprehensive framework for designing rigorous and defensible evaluation sections for machine learning and information retrieval papers, particularly for top-tier conferences like WSDM. It covers the four critical evidence quadrants—Effectiveness, Validity, Efficiency, and Robustness—and details necessary protocols, such as temporal splits, proper baseline selection (including heuristics), ablation studies, and hardware-aware performance reporting. Use this when auditing or writing the experimental section of a research paper.

This robust pipeline guides development teams through a rigorous, five-phase planning process *before* writing any code. It systematically analyzes PRDs, design specifications (UX, accessibility), and existing architecture to produce a comprehensive implementation plan. Key outputs include Architecture Decision Records (ADRs), detailed task breakdowns, and verification strategies, ensuring all technical and design decisions are documented and scoped.

Audits AI-assisted legal, compliance, governance, and institutional decisions before reliance to determine whether the responsible human or institution has enough access to evidence, reasoning, uncertainty, authority, and review path to genuinely own the decision rather than merely approve, sign, or transmit it.

Use to clarify distinctions between accounting and finance concepts such as cash flow vs profit, EBIT vs EBITDA, CapEx vs OpEx, debt vs equity, and ROI vs ROE. Ideal for calculators, due diligence, dashboards, investor memos, and client-facing financial explanations.

This skill provides a comprehensive audit of legal AI outputs, documents, and prompts before they are trusted or used in professional contexts. It checks for seven critical 'fault lines,' including grounding, causality, metacognition, and adherence to authority boundaries. Use this for legal memos, contract reviews, compliance reports, or any AI-generated content requiring high reliability.

Transform a legal professional's bounded knowledge, workflow, or judgment patterns into a safe, structured, and reusable AI skill. This tool processes inputs like legal memos, compliance checklists, or negotiation notes to extract core capabilities, risk taxonomies, and review standards. It strictly separates the extracted capability from the individual's personal voice or identity, ensuring full source traceability and mandatory human review points for high-stakes legal work.

This skill provides a comprehensive audit framework for evaluating AI agents used in legal, compliance, or business operations. It assesses the risks associated with delegated authority—such as sending messages, filing documents, or making payments—by checking for adequate controls, logging, revocation mechanisms, and clear accountability chains. It is essential for firms connecting agents to sensitive systems like CRM, court portals, or financial accounts to ensure safe and compliant deployment.