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quality-screen

AI Berkshire skill: 去劣筛选:7条指标快速排除非一流公司. Source: skills/quality-screen.md.

53

Quality

59%

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SecuritybySnyk

Low

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tessl review fix ./codex-skills/quality-screen/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

68%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 skill body is a well-structured, actionable screening workflow with concrete thresholds and a complete output template. Its main weakness is workflow clarity: as a batch operation it lacks an explicit data-validation feedback loop, which the rubric caps at 3.

Suggestions

Add an explicit validation/retry step in the execution flow, e.g. after parallel data collection, verify each company's 7 metrics are complete and re-search any marked '数据不足' before final scoring.

Tighten or relocate the 'Codex adapter note' so the user-facing workflow begins with the skill's actual purpose rather than adapter boilerplate.

Specify concrete retrieval guidance (preferred data sources or example queries) so the data-collection step is fully executable rather than enumerated as a checklist.

DimensionReasoningScore

Conciseness

The body is efficient and well-organized with compact tables, explicit thresholds, and minimal concept padding; the only minor over-explanation is the 'Codex adapter note' boilerplate, which keeps it just below the 'lean and every token earns its place' anchor at 5.

4 / 5

Actionability

Concrete numeric thresholds for all 7 metrics, specific exemption conditions, enumerated per-company data fields, and a copy-ready output template give mostly executable guidance; minor gaps are the absence of exact retrieval commands or tool invocation specifics, which keeps it at 4 rather than 5.

4 / 5

Workflow Clarity

A clear 4-step sequence with mode branching and per-metric checking is present, but this is a batch operation over 10-30 companies with no explicit validate-and-retry feedback loop for missing/contradictory data; per the batch-operation cap, workflow clarity cannot exceed 3.

3 / 5

Progressive Disclosure

Content is organized into clear sections (design principles, metrics, exemptions, execution flow, caveats, limitations) with no nested references and no bundle files; structure is good and self-contained, just short of the 'clear overview with well-signaled one-level references' anchor at 5.

4 / 5

Total

15

/

20

Passed

Description

50%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 conveys a clear purpose and a concrete action but lacks an explicit 'when to use' trigger clause and reads with meta-noise ('AI Berkshire skill:', 'Source: skills/quality-screen.md') that dilutes specificity. Trigger keywords are present but thin on synonyms and natural user phrasing.

Suggestions

Add an explicit 'Use when...' clause stating when this skill should be triggered, e.g. 'Use when screening listed companies or industries to quickly exclude non-first-class candidates.'

Remove the meta-noise ('AI Berkshire skill:', 'Source: skills/quality-screen.md') from the description and keep only the user-facing purpose statement.

Broaden trigger terms with natural synonyms users would actually say, such as '筛选股票', '排除烂公司', '质量筛选', '选股指标'.

DimensionReasoningScore

Specificity

Names the domain ('去劣筛选') and one concrete action ('7条指标快速排除非一流公司') with a metric count, but does not enumerate the specific actions comprehensively, fitting the '1-2 concrete actions' anchor rather than the multi-action anchor at 4.

3 / 5

Completeness

The 'what' is clear (7-metric exclusion screening), but there is no 'Use when...' or equivalent trigger clause; per the judging guidelines a missing explicit trigger caps completeness at 3.

3 / 5

Trigger Term Quality

Contains relevant domain keywords a user might say ('去劣筛选', '排除非一流公司', '7条指标'), but misses common variations or synonyms, matching the 'some relevant keywords but missing common variations' anchor.

3 / 5

Distinctiveness Conflict Risk

The niche (listed-company quality screening) is somewhat specific and unlikely to broadly conflict, but the 'AI Berkshire skill:' meta-prefix adds generic noise and it could overlap with other screening skills, matching the 'somewhat specific but could still overlap' anchor.

3 / 5

Total

12

/

20

Passed

Validation

100%

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

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
xbtlin/ai-berkshire
Reviewed

Table of Contents

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