etf-analysis

ETF分析：产品筛选、费率对比、跟踪误差、流动性评估、策略应用与中国市场ETF量化配置框架。

Quality

70%

Does it follow best practices?

Run evals on this skill

Adds up to 20 points to the overall score

View guide

Securityby

Passed

No findings from the security scan

Fix and improve this skill with Tessl

tessl review fix ./a_全网优秀资源/10_大模型/07_skill包/vibe_trading_skills/etf-analysis/SKILL.md

The canonical home for this skill is etf-analysis in HKUDS/Vibe-Trading

Quality

Content

73%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The content is highly actionable with executable, well-documented code and clearly sequenced quantitative workflows, but it is lengthy and monolithic — inlining tables, formulas, and code templates that would be better split into reference files for conciseness and progressive disclosure.

Suggestions

Move large reference material (data-analysis code templates in section 7, prompt-template library in section 8, and ratings/threshold tables) into separate files under references/ and replace them with concise summaries plus one-level-deep links.

Trim domain knowledge Claude already knows (basic definitions of LOF/分级基金, generic asset-allocation framing) to tighten conciseness.

Add explicit validation/checkpoint steps for data-dependent workflows (e.g. verify tushare API response is non-empty before computing tracking error) to strengthen workflow clarity for batch operations.

Dimension	Reasoning	Score
Conciseness	The body is mostly efficient and assumes Claude's competence on most concepts, but at ~870 lines it contains large reference tables, ratings thresholds, and worked formulas that re-explain domain knowledge Claude already knows and could be trimmed or moved to references.	3 / 5
Actionability	The skill provides multiple fully executable, copy-paste-ready Python functions (etf_score, fee_drag_analysis, calc_tracking_error, factor_exposure_analysis, tushare data loaders) with docstrings covering the common analytical cases.	5 / 5
Workflow Clarity	Multi-step processes such as the 5-step ETF selection framework and rebalancing triggers are clearly sequenced with explicit thresholds, and quantitative scoring models provide implicit checkpoints; however, data-dependent batch/analysis workflows lack explicit validate-then-proceed feedback loops, so it falls just short of a 5.	4 / 5
Progressive Disclosure	The body has clear section structure, but no bundle/reference files exist, so large blocks that belong in separate files (full data-analysis code templates, prompt-template library, ratings tables) are inlined in SKILL.md rather than split into one-level-deep references.	3 / 5
	Total	15 / 20 Passed

Description

56%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

The description is specific and distinct, naming concrete ETF analysis dimensions and a quantitative configuration framework, but it lacks an explicit 'Use when...' trigger clause and has limited natural trigger-term variation, which caps completeness and trigger quality.

Suggestions

Add an explicit 'Use when...' clause, e.g. 'Use when analyzing or selecting ETFs, comparing ETF fees or tracking error, or building ETF-based quantitative portfolios.'

Include more natural trigger phrases and synonyms a user might say (e.g. 'ETF selection', 'ETF portfolio', '基金筛选', '指数基金') to improve trigger-term coverage.

Use third-person action verbs (e.g. 'Analyzes', 'Compares', 'Evaluates') rather than noun phrases to make capabilities read as concrete actions.

Dimension	Reasoning	Score
Specificity	The description lists several concrete capabilities ('产品筛选、费率对比、跟踪误差、流动性评估、策略应用' and '量化配置框架') rather than vague language, though the actions are high-level noun phrases rather than verbs and coverage of what the skill actually does is somewhat implicit.	4 / 5
Completeness	It clearly states 'what' (ETF analysis dimensions and a quantitative configuration framework) but provides no 'Use when...' clause or equivalent explicit trigger guidance, which caps completeness at 3 per the rubric.	3 / 5
Trigger Term Quality	It contains relevant domain keywords (ETF, 跟踪误差, 折溢价, 费率) that a user might mention, but it omits common natural variations and synonyms and lacks file-format-style trigger phrases a user would naturally say.	3 / 5
Distinctiveness Conflict Risk	The description carves a clear niche (China-market ETF quantitative analysis) that is distinct from generic asset skills, with only minor overlap risk against other finance/investing skills.	4 / 5
	Total	14 / 20 Passed

Validation

87%

Warnings & errors only

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 14 / 16 Passed

Validation for skill structure

Criteria	Description	Result
skill_md_line_count	SKILL.md is long (871 lines); consider splitting into references/ and linking	Warning
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	14 / 16 Passed

Repository: charliedream1/ai_quant_trade
Path: a_全网优秀资源/10_大模型/07_skill包/vibe_trading_skills/etf-analysis/SKILL.md
Commit: b612402

Reviewed: 3 days ago

Table of Contents

Discovery Implementation Validation

Is this your skill?

If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.