Content
65%Weight 40%Scale 1-5Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
A dense, knowledgeable A-share event-driven playbook with genuinely useful non-generic content (empirical return tables, filter rules, worked calculations, an output template), weakened by the total absence of executable data-retrieval code despite declared dependencies, no validation checkpoints in the workflow, and a monolithic 260-line structure with no reference-file split for the rulebook detail.
Suggestions
Add a minimal executable data-retrieval snippet (e.g. tushare calls for 增减持/定增/激励公告) or drop the 'pip install pandas numpy' dependency line — currently code is promised but never shown, which is the main actionability gap.
Insert validation checkpoints into the workflow: verify announcement source freshness against 交易所官网/巨潮资讯 before acting, and sanity-check computed spreads against deal terms before sizing a position.
Move the detailed rulebooks (ST/退市 2024 rules, 股权激励 解读细则, the output-format template) into one-level-deep reference files under references/ and keep SKILL.md as a tighter overview with signaled links.
Isolate time-sensitive content (2024 ST 新规, 注册制-era notes, the 2023 spin-off example) into a versioned rules section so stale thresholds are easy to spot and update.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is dense reference material that assumes domain competence — it never explains what M&A or an incentive plan is, and delivers A-share-specific knowledge Claude lacks (empirical excess-return ranges, 2024 ST rules, discount-rate thresholds) in tight tables and rule blocks. It sits at level 4 ('efficient; minor instances that could be trimmed') rather than 5 because time-sensitive specifics like "2024新规", "注册制后减少", and the dated 2023 spin-off example are not isolated in a rules-version section, and the sample output report adds length without being essential. | 4 / 5 |
Actionability | Concrete, decision-ready guidance throughout: a worked spread calculation ("价差 = (25 - 23.5) / 23.5 = 6.38%"), quantitative filter rules ("排除: 被动减持(质押平仓)"), a fundraising-use scoring rubric (+3/-1), and a complete output report template with position sizes and stop levels. It misses level 5 because no executable code is provided despite a "pip install pandas numpy" dependency block and a mention that "tushare 提供增减持/股权激励/定增数据接口" — the data-retrieval step has no commands or code. | 4 / 5 |
Workflow Clarity | There is a clear event timeline (T-30 → T → T+1..T+20 → T+N) and per-event analysis frameworks, giving a recognizable sequence, which places it above level 2. But no validation checkpoints exist anywhere — nothing on verifying announcement freshness (critical, since the notes warn third-party platforms lag), no cross-check of computed spreads against deal terms, and no error-recovery loop — matching level 3 ('sequence present but checkpoints missing or implicit') rather than 4. | 3 / 5 |
Progressive Disclosure | The body is well-sectioned (概述 → 核心概念 → 分析框架 → 输出格式 → 注意事项) but is a ~260-line monolith with zero bundle files: no references/, scripts/, or assets/ exist, and substantial detail (ST/退市规则, 股权激励解读细则, 输出格式模板) that would sit better in one-level-deep reference files is inlined. This fits level 3 ('some structure but content that should be separate is inline'); the skill is far beyond the under-50-line simple-skill exception that would allow a 5 without external files. | 3 / 5 |
Total | 14 / 20 Passed |