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Found 5 Skills
Senior Code Architect & Quality Assurance Engineer for 2026. Specialized in context-aware AI code reviews, automated PR auditing, and technical debt mitigation. Expert in neutralizing "AI-Smells," identifying performance bottlenecks, and enforcing architectural integrity through multi-job red-teaming and surgical remediation suggestions.
Build and maintain a Karpathy-style LLM knowledge base — a self-compiling Obsidian markdown wiki where an Agent ingests raw sources, compiles cross-linked concept/entity/summary pages, answers queries against the corpus, lints the graph for health, and audits in-context human feedback filed from Obsidian or the local web viewer. Use when (1) scaffolding a new knowledge base for any research topic, (2) ingesting articles/papers/PDFs/web pages into raw/, (3) compiling or restructuring wiki articles from existing raw material, (4) answering questions against the wiki and filing durable answers back, (5) running lint passes for dead links / orphan pages / coverage gaps / audit shape, (6) processing human feedback from the audit/ directory and applying corrections. Not for general note-taking, daily journals, or non-wiki Obsidian use.
A Metabase AI-governance coach, covering the *controls and rollout* side of AI: who may use Metabot, what it can see, what it costs, where the model runs, and the audit trail. Use this skill whenever the user wants to roll out AI analytics safely, control who can use Metabot or what it can see, set spend or token limits on AI, restrict Metabot's system prompt, audit AI usage, evaluate bring-your-own model or self-hosting options for AI, or prep for a security review of AI features. Trigger it even if the user just says "how do we control AI access", "can we limit what Metabot sees", "we need an AI security review", or "run the AI governance checklist" in a Metabase context. **Not this skill** if the question is about whether the underlying data is modeled, documented, and trustworthy enough for AI — Transforms, metadata, the Glossary, Metrics, the Library — that's `ai-readiness-checklist`. Rough test: this skill is about who gets to point AI at the data; that one is about whether the data is good enough.
Apply AI ethics frameworks (fairness, accountability, transparency, privacy) to evaluate AI systems for algorithmic bias, explainability gaps, and value alignment failures. Use this skill when the user needs to audit an AI system for ethical risks, design fairness constraints, assess explainability requirements, or when they ask 'is this AI system fair', 'how do we detect algorithmic bias', 'what are the ethical implications of this AI deployment', or 'how do we make this model explainable to stakeholders'.
Check the consistency and authenticity risks of citations and references in NSFC proposal text (read-only): Verify the existence of bibkey, format issues such as BibTeX fields and DOI, and generate structured input for the host AI to evaluate item-by-item whether the text expression actually cites the literature; by default, only an audit report is output, and the proposal or .bib file is not directly modified (unless the user explicitly requests it).