Conciseness

The body runs ~1,100 lines / ~57KB with substantial padding: nine multi-paragraph HARD-GATE blocks that restate overlapping instructions (agent_analysis.json requirements appear in the role section, the field table, a full JSON example, and again in Task 4), inline changelogs and version notes ("v2.13.5 改动", "v2.6 论坛 bug 修复" table, "用户原话" quotes), and time-sensitive version references not quarantined in a deprecation section. This matches the score-2 anchor ('noticeably verbose; several unnecessary explanations or padded sections') rather than score 3, because the duplication and changelog material could be cut or moved wholesale without losing guidance.

Actionability

Largely copy-paste ready: stage commands (`python -c "from run_real_test import stage1; stage1('<股票名或代码>')"`), the review loop with exit-code semantics (`# → exit 1 = 有 critical，必须先修`), a complete agent_analysis.json example, sub-agent prompts, and per-dimension re-search query templates. It stays at 4 rather than 5 because of minor gaps: the `cd <repo_root>/skills/deep-analysis/scripts` path assumes a repo layout that doesn't match the bundle (scripts live at ./scripts), Task 5 then uses `python scripts/assemble_report.py` from a different working directory, and several snippets hardcode internal state (`.cache/_global/...`) the agent cannot verify.

Workflow Clarity

The 6-task pipeline is explicitly sequenced with a hard ordering gate ("必须按 Task 1 → 1.5 → 2 → 3 → 4 → 5 顺序"), a mechanical review gate with an iterate-fix-rerun loop ("重跑 review… 必须最后跑一次确认 passed=true"), explicit exit-code checkpoints, and a completion checklist (完成定义) enumerating every artifact. This matches the score-5 anchor: clear sequence, explicit validation steps, and feedback loops for error recovery.

Progressive Disclosure

References exist, are one level deep, and are clearly signaled in a dedicated 详细参考文档 section, and all in-bundle targets were verified to exist (references/task*.md, references/fin-methods/README.md, assets/data-contracts.md). But the score-3 anchor fits: a large share of content that clearly belongs in separate files is inlined — nine full HARD-GATE policy texts, the 13-rule self-review table, the Codex-compatibility table, and the entire 40-line agent_analysis.json example — making SKILL.md itself a near-monolithic wall, and one reference (../../docs/industry-rotation.md) points outside the bundle where the file does not exist.

Specificity

The description enumerates many concrete, specific capabilities: "22 维数据 + 66 位大佬量化评审 + 6 种机构级估值建模 (DCF/Comps/LBO/3-Stmt/Merger) + 7 种研究产物… + 6 种决策方法 + 杀猪盘检测，最终生成 Bloomberg 风格 HTML 报告 + 社交分享战报". This is comprehensive coverage of concrete actions, matching the score-5 anchor; it goes beyond the score-4 anchor's 'minor gaps in coverage'.

Completeness

Both 'what' (core deep-analysis workflow producing the enumerated outputs across A股/港股/美股) and an explicit 'when' clause ("当用户要求…等涉及个股研究的请求时触发") are clearly present with concrete trigger phrases, exactly matching the score-5 anchor. A score of 4 would require the 'when' to be less explicit, which it is not.

Trigger Term Quality

It covers natural colloquial phrases users would actually say ("深度分析 / 全面分析 / 帮我看看 / 值不值得买"), plus a keyword tail with synonyms and tool names ("股票、个股、深度分析、估值、DCF、comps、首次覆盖、IC memo、杀猪盘、龙虎榜、akshare"). Coverage including colloquial variants matches the score-5 anchor; the score-4 anchor ('a few natural terms missing') understates this breadth.

Distinctiveness Conflict Risk

The single-stock deep-analysis niche is mostly distinct with clear triggers, matching the score-4 anchor ('mostly distinct; minor overlap risk with closely related skills'). However, it absorbs adjacent niches also claimed by its own related_skills — 杀猪盘检测 overlaps trap-detector and 龙虎榜/akshare keywords overlap lhb-analyzer — keeping it below the 'clear niche with minimal conflict risk' of score 5.