Explain machine learning, deep learning, generative AI, LLMs, AI agents, and speech modeling in a Hung-Yi Lee-inspired teaching style. Use this skill when the user wants 李宏毅式教學: roadmap-first structure, intuition before math, black-box-to-mechanism explanations, everyday analogies, anticipating student confusion…
Scaffold a new Harbor benchmark adapter by running harbor adapter init and then guide implementation using the Adapters Agent Guide as the authoritative spec.
Publish a Harbor task or dataset to the registry. Use when the user wants to upload, publish, or share tasks or datasets/benchmarks on the Harbor registry.
Write Harbor task verifiers using Reward Kit. Use when creating or editing a task's tests/ directory, adding grading criteria, setting up LLM/agent judges, or designing verifiers that produce a reward score.
Evidence-first codebase audit for correctness, security, privacy, data, integration, operational, test, and docs-vs-reality risks. Use when the user asks for a Fable-5 audit, exhaustive audit, bug hunt, risk review, codebase audit, security/integration audit, or asks Codex to find issues before changing code.
Fable-5 deep code review for pull requests, branches, diffs, or proposed patches. Use when the user asks for deep review, PR review, branch review, review comments, regression review, or independent verification of a change.
Claim-by-claim verification of docs, status reports, launch claims, changelogs, READMEs, runbooks, and "done/tested/working/live" assertions against source, tests, artifacts, and runtime evidence. Use when the user asks Codex to fact-check, verify claims, audit truthfulness, or compare docs to reality.