Conciseness

The body is exceptionally dense with non-obvious judgment (thresholds, ratios, pitfalls) and contains no filler or library-introduction padding, but a few passages explain mechanics Claude already knows and could be trimmed to their decision-relevant conclusions — e.g. 'SFT 与预训练是同一个数学任务（预测下一个词）...' and the PPO-vs-GRPO value-model explanation 'PPO 训练价值模型逐步估计"通常能做到多好"'. This fits 'efficient; minor instances of over-explanation that could be trimmed'; score 3 would require more substantial unnecessary explanation, and score 5 would require every passage to assume Claude's competence with no such explanations.

Actionability

For an instruction-only skill the guidance is fully concrete: a decision table with an explicit entry threshold column, a numbered ①→④ decision order, a worked GRPO example (16 sandboxes, 4 pass → reward 1, 12 fail → 0, group advantage computation), a formula 'pass@k = 1-(1-p)^k', specific LoRA hyperparameters (rank 64–256 for SFT, 8–32 for RL, ~10x full-FT learning rate), named evaluation gates (LongBench v2, IFEval), and a pre-launch checklist naming exact metrics (概率比均值/分位数/最大值、近似 KL、被裁剪比例、策略 staleness). Specific examples cover the common cases, matching anchor 5.

Workflow Clarity

Multi-step processes are explicitly sequenced with validation checkpoints throughout: the route-decision order ①→④, Mid-training's five-step data construction with '多维门禁决定停止', SFT's '线上数据 → 任务蓝图 → 合成任务 → 多次候选轨迹 → 任务验证与轨迹验证 → SFT 数据' pipeline with train/eval dedup rules, RL's four-step rollout→reward→advantage→update with the 全成功或全失败组 gradient-vanishing caveat, environment engineering order with '先用少量 rollout 检查任务是否可完成', and the 上线前的自检清单 checklist. Validation gates and error-recovery diagnostics (e.g. 'loss 降而 pass@k 不动 = 数据没覆盖所需能力') are pervasive, matching anchor 5.

Progressive Disclosure

The body itself is well organized into 何时使用/核心原则/实践模式/常见陷阱 sections, and deep material is delegated via clearly signaled one-level references with per-item descriptions (nine 'chapter8/...' projects and nine 'book/chapter8.md' sections). However, the actual bundle contains none of these files — no references/, scripts/, assets/, chapter8/, or book/ directories exist alongside SKILL.md — so every deep-dive reference resolves to a missing path and the disclosure layer cannot navigate anywhere. Scored against the actual bundle structure this is a real organizational gap, better than anchor 2 (nothing is wrongly inlined) but below anchor 4's 'minor organization gaps' since the entire reference layer is unshipped.

Specificity

The description lists multiple concrete, comprehensive actions within the post-training domain: '判断该先补 Mid-training、用 SFT 立协议还是直接上 RL；构造 Mid-training 语料与 SFT 数据、搭建 RL 环境、在 GRPO/PPO/DPO 与蒸馏之间取舍；排查 pass@k 近零、格式不稳就训 RL、组内无奖励差异、训练-推理数值失配、过度训练泛化下降'. These are specific capabilities plus named failure modes, matching the 'multiple specific concrete actions; comprehensive coverage' anchor; score 4 would require notable coverage gaps, which are not present.

Completeness

It explicitly answers 'what' (route decision, corpus/SFT data construction, RL environment building, algorithm trade-offs, failure-mode troubleshooting) and 'when' ('为 Agent 模型做后训练或选路线时使用' plus an explicit 触发词 list). Both are present with concrete trigger phrases, matching the anchor-5 example structure; anchor 4 would require the 'when' clause to be less explicit, which it is not.

Trigger Term Quality

The explicit trigger list '后训练、Mid-training、继续预训练、SFT、RL、GRPO、PPO、DPO、拒绝采样、RFT、蒸馏、on-policy、RLVR、LoRA' covers the natural vocabulary practitioners use, including synonyms (拒绝采样/RFT, 继续预训练). A few common natural terms are still missing (e.g. 微调/finetuning, RLHF, reward model), which keeps it at 'good keyword coverage; a few natural terms missing' rather than the fully comprehensive anchor 5.

Distinctiveness Conflict Risk

The niche is distinct — route diagnosis between Mid-training/SFT/RL keyed to signals like pass@k and 组内奖励差异 is unlikely to trigger for unrelated skills. Minor overlap risk remains with closely related fine-tuning/LoRA or reward-modeling skills, since 'LoRA' and 'RL' are broad triggers, matching 'mostly distinct; minor overlap risk' rather than the minimal-conflict anchor 5.