Conciseness

The body is dense with judgment-carrying content ('惩罚是通常可达的那一半，进展奖励是受可达性门控的那一半', concrete numbers like '3.71→0.66' and '从 7.0 降至 4.4') rather than padded explanation. However, the reward-seeking/early-stop behavior is explained repeatedly across 核心原则, 实践模式2, and 常见陷阱, and PPO/GRPO credit-assignment mechanics appear in both 核心原则 and 实践模式5 — minor duplication that could be trimmed, matching the 'efficient with minor over-explanation' anchor rather than the flawless one.

Actionability

Guidance is mostly concrete and executable in intent: '通过 +1、不通过 −1', '计算策略梯度时必须屏蔽这些反馈 token', '两路各自归一化后再合并', plus four numbered RLVP design rules. The reward-merging code sketch relies on placeholder functions (verify_final_state, deterministic_path_signal, normalize) with no worked example of a single real path rule, so it falls short of fully copy-paste-ready — the 'mostly executable with minor gaps' anchor.

Workflow Clarity

There is a clear ordered engineering path ('先用结果奖励建立可靠基线，再只为真正可验证的中间事件加入过程信号') with explicit validation checkpoints ('先用少量 rollout 检查"任务是否可完成、验证器能否区分对错"，再扩大采样规模'; '先测信号可达性，再决定是否增加奖励维度'). It falls short of the top anchor because there is no explicit error-recovery loop describing what to change when the rollout checks fail.

Progressive Disclosure

The body itself is well-sectioned with clearly annotated pointers (配套代码 and 深度阅读 entries each describe their target's content), but every referenced path (chapter8/RLVP/, chapter8/premature-completion-dpo/, book/chapter8.md, …) is absent from the bundle — no references/, scripts/, assets/, or any other files exist — so the promised deeper layer dangles and all substantive guidance is inlined in SKILL.md. This sits above the 'references not clearly signaled' midpoint but below good structure, since references that cannot resolve are more than a minor organization gap.

Specificity

The description lists multiple concrete actions — '为 RL 训练设计奖励函数或排查奖励黑客', '决定奖励来自规则、人类偏好还是模型评判', '结果奖励还是过程奖励', '标量、向量还是生成式诊断', '信用分配、隐藏测试与早停判定设计' — comprehensively covering source, timing, representation, path constraints, and credit assignment. This matches the anchor for comprehensive coverage of specific concrete actions rather than the anchor with minor gaps.

Completeness

It explicitly answers 'what' (design reward functions, decide source/timing/representation, apply RLVP path constraints, credit assignment, hidden tests, early-stop determination) and 'when' ('为 RL 训练设计奖励函数或排查奖励黑客时使用' plus a dedicated trigger-word list). Both what and when are explicit with concrete trigger phrases, matching the top anchor.

Trigger Term Quality

The explicit trigger list '奖励设计、reward hacking、reward seeking、RLVR、RLHF、GRM、ORM、PRM、RLVP、信用分配、隐藏测试、过程奖励、奖励塑形、组内差异' covers the natural phrases an RL practitioner would actually say, including synonyms in both Chinese and English (过程奖励/PRM, reward hacking/reward seeking). Comprehensive coverage with synonyms matches the top anchor; no meaningful synonym group is missing to justify a 4.

Distinctiveness Conflict Risk

The description occupies a clear niche (RL reward design) with distinctive triggers such as RLVP, reward seeking, 奖励塑形, and 组内差异 that would not naturally fire for unrelated skills. Minimal conflict risk matches the top anchor.