Conciseness

The body is dense and mostly operational, but the 常见陷阱 (pitfalls) section restates roughly eight points already made verbatim in 核心原则 (overcorrection, train/eval isolation, model self-report as completion signal, tokenizer audit, DPO mixing with general data), and the 案例参照 section carries full experimental result tables (3/12→11/12, 6/8→0/8, hyperparameters) that read as reference material. This fits 'Mostly efficient but includes some unnecessary explanation or could be tightened' rather than level 4, where over-explanation would be only minor.

Actionability

Concrete, runnable commands are given for the companion code (python demo.py, python -m pytest -q test_pipeline.py, python build_preference_data.py --teacher, python train_dpo.py --smoke, python evaluate.py --decision-score), along with reference hyperparameters (Qwen2.5-7B-Instruct + bf16 LoRA, 4 epochs, lr 3e-5, ~24GB) and exact metric definitions (过度矫正率 = 1 − 正常收尾率). It stops short of level 5 because no concrete artifact examples are shown inline — e.g., a sample preference-pair JSON or a truncated trajectory-prefix record — so a fresh user cannot fully reproduce the data construction without opening the external chapter8/ code.

Workflow Clarity

The pipeline is a clearly numbered ①–⑦ sequence (collect/attribute → triage whether to train → choose training form via table → extract prefixes and build preference pairs → LoRA DPO → dual-set validation → record/receipt) with explicit validation checkpoints: step ⑥ defines which metrics must fall, hold, or stay low after training, with the explicit expected direction for each; isolation rules are enforced by unit tests; tokenizer round-trip audit gates training. This matches 'Clear sequence with explicit validation steps; feedback loops for error recovery'.

Progressive Disclosure

The body has clear sections and dedicated 配套代码 / 深度阅读 navigation sections, but no bundle files exist (no references/, scripts/, assets/) — the chapter8/ and book/ paths point outside the skill, so navigation cannot be satisfied from the bundle itself. Meanwhile, the case-study experiment results, per-case handling details, and the duplicated pitfalls content are inlined in SKILL.md where they belong in separate reference files, fitting 'Some structure but could be better organized ... content that should be separate is inline' rather than level 4's 'most content is appropriately placed'.

Specificity

The description enumerates multiple concrete actions spanning the full pipeline: 失败归因 (failure attribution), 轨迹前缀截取 (trajectory prefix truncation), DPO 偏好对构造 (preference pair construction), LoRA 训练, 边界集与保留集双集验证 (boundary/retention dual-set validation), plus routing decisions between SFT/DPO/RL vs. fixing Harness/工具/tokenizer, and extracting evidence-backed learning signals from trajectories. This matches the anchor 'Lists multiple specific concrete actions; comprehensive coverage' — no generic filler like 'helps with training data'.

Completeness

Both 'what' and 'when' are explicit. What: the complete chain from attribution through prefix extraction, preference pair construction, LoRA training, to dual-set validation. When: '把生产 bad case / 失败轨迹转成训练数据时使用' plus the explicit trigger-word clause. This matches the top anchor 'Clearly and explicitly answers both what AND when with concrete trigger phrases'; the 'Use when...' guidance is present as required.

Trigger Term Quality

An explicit 触发词 (trigger word) list provides comprehensive natural-term coverage with both Chinese and English synonyms: bad case, 失败归因, DPO, 偏好对, 轨迹前缀, 拒绝采样, 过度矫正, 边界集, 保留集, 过早结束, reward hacking, LoRA 微调, 训练评估隔离. These are exactly the phrases a user working on post-training failure cases would say, matching the anchor for comprehensive coverage including synonyms.

Distinctiveness Conflict Risk

This is a clear niche (converting production failure cases into post-training data) with highly distinct triggers — DPO preference pairs, boundary/retention sets, reward hacking, premature completion — that no adjacent skill (general fine-tuning, SFT data synthesis, evaluation harness) would naturally claim. It matches 'Clear niche with distinct triggers; minimal conflict risk'.