菜单
Use this skill to generate a refactoring audit report documenting round-by-round changes, rollback points, verification results, and reproduction instructions. Triggers for: '重构报告', 'refactoring summary', 'audit report', 'show refactoring progress'. Requires at least one refactor-run round.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
| name | refactor-report |
| description | Use this skill to generate a refactoring audit report documenting round-by-round changes, rollback points, verification results, and reproduction instructions. Triggers for: '重构报告', 'refactoring summary', 'audit report', 'show refactoring progress'. Requires at least one refactor-run round. |
Generate a self-contained HTML report documenting the entire refactoring process. The report's core purpose is reproducibility and rollback — when someone needs to intervene, they can understand what happened, roll back to any point, and reproduce any state.
Ask one question at a time — multiple questions at once overwhelms users.
Follow the Workflow State Protocol from CLAUDE.md: push on entry, update step as you progress, pop on completion.
Requires: at least one {PROJECT}/stages/refactor/runs/*/run.json. If none, offer /refactor-run.
Locate project per CLAUDE.md conventions. Show recent projects, let user pick.
Read all data sources:
plan.json — module classification, paper targets, phasesruns/*/run.json — every round's recordcode/ — current state (file count, line count)git -C code/ log --oneline — commit history (rollback points)Output: {PROJECT}/stages/refactor/refactor_report.html (self-contained HTML).
Repository, paper, benchmark target, current status (phase/round), codebase size reduction (original vs current files/lines).
For each round: git commit hash (rollback point), target modules, changes (files modified/deleted/added with line deltas), auto-cleanup results, verification tier reached + results, pass/fail outcome with failure reasons.
Table of all rollback points with git commit hashes and git checkout commands. Include instructions for rolling back and returning to HEAD.
Aggregate across all rounds: how many rounds reached each verification tier, performance regression status, failed round count with reasons.
Current classification of every module: deleted (which round), refactored (which round, line delta), keep (reason), pending (which phase).
Step-by-step: clone original repo at exact commit, apply refactoring (copy code/ or replay rounds), verify with benchmark command + expected metrics, environment details.
Python script producing self-contained HTML with: inline CSS, collapsible round details, color-coded status (green=pass, red=fail, gray=skipped), copy-to-clipboard on git commands.
Fallback: generate HTML directly with Write tool if script fails.
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).