“machine learning”

用户要求给笔记排序、分优先级、找最值得学/最直观的篇目，或站点数据刷新时触发。全库价值排序 + 教学成果检测。

任何一批改动交付前触发。独立审校：只看 diff 挑错，不参与改写，与执行者分离。

Sketch types, signatures, and module structure before code, then stay in the loop while implementation fills in.

Use for "automate me", "create/update/refresh my -mode skill", "turn/capture my preferences or working style into a skill", or wanting agents to follow how…

Explain a subsystem's behavior from source, callers, data structures, and runtime evidence.

Challenge a code change with independent adversarial reviewers and consolidate evidence-backed findings.

Explain how to install, configure, or choose a pstack workflow in Claude Code and Codex.

Check pstack installation and configure role effort and panel size for Codex and Claude Code.

Author or update a portable pstack skill with precise triggers, self-contained resources, and realistic behavioral verification.

Review a branch, working diff, or named files for reachable defects and missing behavioral coverage.

Persist collection evidence and plan unread room windows separately from learned facts.

Email-observation learning — voice-learn and task-completion producers plus learning-digest review tools.

Read, run and grow a persistent skill library of verified, executable sub-task skills that carries what agents learn across sessions and tasks on the same…

Help a human learn or understand a topic, technical system, code change, error, agent output, or source material.

Vet a perf measurement (limiter, tuning, limits, errors, repeatability, relevance, and whether the work happened) before you report or act on it.

Find the mistakes agents keep repeating in this repo and make each one impossible.

Spawn N parallel candidates at the same task, pick a base, graft the strongest parts of the losers into it.

Find what a change could break somewhere else before it ships, beyond the diff, and prove the one fact it's safe because of by running real code instead of…

Use when the user asks to see what they learned this session, create a session summary, or visualize learning progress.

Entry point.

Restate the last message in plain human language, with no jargon.

[Beta] Cross-session analysis of accumulated .learnings/ files.

Builds and differentiates PennyLane quantum circuits, hybrid PyTorch or JAX models, molecular VQE and QAOA workflows.

Learn how XState owns a lifecycle while Effect owns its work, errors, and services.