Pressure-tests any AI plan with six Chief AI Officer questions — evals, hallucination SLO, EU AI Act tier, build-vs-buy, cost curve, next hire — and returns a SHIP/SHARPEN/BLOCK verdict.
A strict operating procedure for building, retrieving, and correcting evidence-grounded local person profiles using only Distilly's five MCP tools.
An installation skill that wires a local Ollama MCP server into NanoClaw so the container agent can run local models and optionally manage the model library.
A step-by-step skill that wires a Signal messaging channel into a NanoClaw assistant using a signal-cli device link.
A guided, re-runnable setup skill that wires a Microsoft Teams bot channel into a self-hosted NanoClaw assistant.
Runs six Article-cited forcing questions to pressure-test an AI system's readiness for the EU market.
Installs NanoClaw's native Baileys WhatsApp channel with a mandatory number-safety gate, QR/pairing-code linking, and explicit .env configuration.
An operating spec that forces Claude to build, retrieve, and correct evidence-grounded local person profiles using only Distilly's exact five MCP tools.
A skill for querying and analyzing DepMap CRISPR gene dependency and drug sensitivity data to validate cancer-specific targets.
Splits a research goal into parallel sub-goals, runs each in a headless `claude -p` subprocess, and aggregates everything into a polished standalone report file.
A fan-out audit pipeline that runs every analysis skill in parallel and produces a unified health dashboard in Markdown and HTML.
Runs a project-wide, analysis-only sweep for complexity hotspots, deprecated APIs, duplication and architecture rot, then ranks it all by impact vs. refactor effort.
Assembles AI-generated short-drama shots into a finished film by first writing a reviewable edit decision list, then rendering with burned-in subtitles and normalized loudness.
Scans a Karpathy-pattern LLM wiki (raw sources + wikilinked markdown + schema) and builds an interactive knowledge graph with entities, implicit relationships, and topic clusters.
Turns a clinical dataset into reproducible Python/R analyses with journal-ready tables, figures, and manuscript text.
Locates babysitter processes and committed runs in an external public repo and writes an evidence-backed retrospective with concrete improvement suggestions.
Orchestrates a 2-layer parallel agent hierarchy for large refactors, multi-file migrations, and codebase-wide audits.
Splits huge data workloads into batches, runs them across parallel sub-agents, and merges everything back into one validated output.
A corporate-development skill that disciplines M&A thinking from deal thesis and target screening through valuation, diligence, and integration planning.
Instruments Spring AI apps with token, latency, cost and prompt-audit telemetry using Micrometer and the GA advisor API.