Use this skill to generate a self-contained HTML evaluation report from a completed eval run. Includes metrics, baseline comparison, per-class breakdown, training context from upstream DAG, and embedded charts. Triggers for: '生成评伋报告', 'make eval report', 'create HTML report from eval results', 'share eval results with team'. Not for running evaluation (use eval-run).
Use this skill whenever the user wants to execute a model evaluation run — testing a trained model on a dataset to measure metrics like mAP, accuracy, IoU, precision, recall, or per-class AP. Trigger for: launching eval runs (debug or production mode), running evaluation on a remote server, checking status of a running/crashed eval job, collecting results when eval finishes, forking a previous eval run with changed parameters (threshold, NMS, confidence, dataset split), and comparing metrics against a baseline. Also trigger for Chinese requests like "跑评估", "测一下", "跑一下eval", "对比baseline". This is the execution skill — not for initial config setup (use eval-init) or HTML report generation (use eval-report).
Use this skill whenever the user wants to run inference — feeding data through a trained model to get predictions, detections, embeddings, or any output. Trigger for: running inference on images/video/data, executing a model on new inputs, testing a model quickly, checking inference speed/FPS, running in debug or production mode, checking status of a running inference job, forking a previous run with different inputs or model. Also trigger for Chinese requests like "跑推理", "推一下", "跑一下模型", "测试一下", "看看效果". This is the execution skill — not for initial config setup (use infer-init).
Use this skill to create a new MLClaw project. Triggers when the user wants to start tracking an ML model, set up a project workspace, or initialize project structure. Use for: '新建项目', 'create project', 'initialize', 'set up a new model'. Not for running inference/evaluation (use the stage-specific skills).
Execute one refactoring round — make changes, run verification, commit or revert. Each invocation is one round; multiple rounds across conversations until the codebase is clean. Trigger for: running a refactoring round, verifying benchmark after changes, reverting a failed round, resuming an interrupted round, checking refactoring progress, promoting refactored code to other stages. Also trigger for: "跑重构", "重构一轮", "跑一下refactor", "验证benchmark", "回退这轮". This is the execution skill — not for initial analysis (use refactor-init) or audit reports (use refactor-report).
Use this skill whenever the user wants to execute a training run — launching, monitoring, or finalizing a model training job. Trigger for: starting a training run (debug or production mode), checking status of an in-progress training job, diagnosing a crashed run, finalizing a completed run (picking best checkpoint, applying retention), forking a previous training with changed hyperparameters, or continuing/resuming training from a prior checkpoint. Also trigger for Chinese requests like "跑训练", "开训", "继续训", "训练崩了看一下", "训练完了". This is the execution skill — not for initial schema setup (use train-init) or comparing runs (use train-compare, when available).
Use this skill to render a train-tune session as a human-readable markdown chain (chain.md). Reads all runs belonging to the session, extracts hypothesis / outcome / config diff per run, and assembles a structured narrative: headline, best-so-far curve, coverage map, decision timeline, confirmed/refuted distillation, open questions, and final recipe. Triggers when: /train-tune session closes (auto-invoked), user asks to re-render an existing session report, user asks for mid-session preview. Use for: "render the lr search report", "show me the chain.md for last train-tune session", "渲染 tune 报告", "总结一下这次搜索". This is a pure rendering skill — does not modify runs, does not run training.
Use this skill to run adaptive hyperparameter optimization on a fixed model + dataset. Triggers when user wants to find best hyperparameters via an agent-driven search loop: the agent reads prior runs, identifies coverage gaps, hypothesizes the next config, launches trials, observes outcomes, and iterates until budget exhausted or coverage sufficient. Trigger for: "tune lr / hyperparams", "find best config", "search hyperparams", "调超参", "tune 一下", "搜个 lr". This is the HPO loop skill — not for architecture search (that's /explore-*) or single-trial training (that's /train-run). Auto-invokes /train-tune-report at session close.