Skill UI

This skill enforces rigorous governance and discipline when AI modifies systems, debugs code, or makes root-cause claims. It mandates safety gates (e.g., backup, blast radius check), demands evidence for every diagnosis, and prevents common AI shortcuts like guessing or deflecting. Use this for critical code modifications, complex debugging, and high-stakes deployments to ensure thoroughness and safety.

A structured methodology for coordinating multiple, simultaneous investigation threads to determine the root cause of complex system failures, performance regressions, or integration bugs. This skill decomposes a large problem into independent tracks (e.g., database, application code, infrastructure), assigns ownership, and manages structured sync points (Continue/Pivot/Converge) to converge on a solution faster than serial debugging. Essential for high-stakes production incident response.

Provides developmental, qualitative, and evidence-traceable review of scholarly works (papers, protocols, ideas). It also rigorously audits low-stakes research assessment rubrics for construct validity, fairness, and governance. Crucially, this tool is strictly forbidden from making high-impact personnel decisions, ranking individuals, or determining final outcomes.

This tool simulates a Chief Technology Officer's comprehensive review, stress-testing any major technical plan. It systematically interrogates critical areas including scaling limits (scaling cliffs), accumulated technical debt, team capacity planning, build-vs-buy economics, system reliability (SLOs), and security compliance. Use it before committing to core architecture changes or making high-stakes engineering decisions.

This skill enables the creation of a robust 'harness' system for coding agents within a repository. It addresses the need to prevent repeated AI-generated coding mistakes by implementing durable instructions, guardrails, regression tests, and failure memory. It treats the target repository as the source of truth, ensuring consistency and maintaining high reliability across all automated contributions.

An adversarial Quality Assurance (QA) pass designed to deep-dive into any codebase for logic errors, security flaws, and functional bugs. Instead of confirming that code 'looks right,' this skill actively attempts to break the code by simulating hostile inputs, tracing critical data paths, and checking for subtle, real-world correctness issues. Essential for high-stakes, critical code reviews.

This advanced skill performs code review by simulating the documented engineering philosophies of historical figures (e.g., Linus Torvalds, Steve Jobs, Ken Thompson). It moves beyond generic AI findings by grounding critiques in specific, citable principles—such as 'good taste' or 'customer experience.' Ideal for high-stakes PRs, architectural reviews, and ensuring deep, pre-submission code hardening.

This skill enforces a rigorous, multi-phase coding workflow (Discuss -> Map -> Decompose -> Execute -> Verify) to ensure code is structurally grounded, complete, and free from invented APIs or assumptions. It is designed for high-stakes, complex, or multi-file engineering tasks, acting as a senior, disciplined coding partner that plans meticulously before writing code.

This guide outlines best practices for ensuring rigorous reproducibility of complex interaction claims, especially in game theory and multi-agent systems (MAS). It details required evidence for proofs, experimental setups (agents, opponents, seeds, compute), and consistency checks between the paper's claims and the provided artifacts. Essential for high-stakes academic submissions like AAMAS.

This comprehensive guide provides expert strategies for structuring and auditing the "Related Work" section for top-tier NLP conferences (like ACL). It covers citation hygiene (Anthology vs. arXiv), maintaining temporal freshness against rapidly evolving baselines, and articulating clear technical contrasts to prove the novelty of your research. Essential for high-stakes academic publishing.

This skill guides the process of elevating specific empirical findings into general, portable theoretical contributions suitable for high-level academic journals. It focuses on structuring the core argument by explicitly defining mechanisms, scope conditions, observable implications, and conceptual stakes, transforming mere results into discipline-level theory.

This master preflight checklist guides authors through every necessary detail before submitting a high-impact manuscript. It covers complex academic requirements, including narrative structure, figures integrity, data transparency (DOIs, accession numbers), required metadata (CRediT, Lead Contact), and high-stakes cover letter strategy, ensuring the work is both scientifically sound and submission-ready.