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factor-research

Factor research framework with IC/IR analysis, quantile backtesting, and factor combination. Suitable for cross-sectional factor evaluation across multiple instruments.

54

Quality

68%

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tessl review fix ./a_全网优秀资源/10_大模型/07_skill包/vibe_trading_skills/factor-research/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

63%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 body is well-organized and unusually rich in concrete interpretation standards, but its guidance stops short of executable: no example call to the `factor_analysis` tool and no code for the factor/return computation steps. Moving the combination methods and pitfalls into reference files would improve both progressive disclosure and conciseness.

Suggestions

Add a concrete example invocation of the `factor_analysis` tool (e.g. its call syntax with the three required parameters) so step 3 is executable rather than descriptive.

Move the Factor Combination Methods and Common Pitfalls sections into reference files (e.g. references/combination.md, references/pitfalls.md), keeping a one-line pointer in SKILL.md.

Provide a short code snippet for steps 1-2 (computing factor exposures and forward N-day returns into aligned CSVs), since the alignment precondition is the skill's most error-prone step.

DimensionReasoningScore

Conciseness

The body is dense and operational (threshold tables, tool parameters, a stated alignment precondition), with only minor over-explanation of concepts Claude already knows (e.g. the definitional sentence under Survivorship Bias), matching the 'efficient; minor instances of over-explanation' anchor; not 5 because the Common Pitfalls section runs long.

4 / 5

Actionability

The parameter and output-file tables plus numeric IC/IR thresholds are concrete, but there is no actual invocation example for the `factor_analysis` tool and steps 1-2 (computing factor and return CSVs) have no executable code — the combination formulas are schematic rather than runnable, fitting the 'some concrete guidance but incomplete' anchor.

3 / 5

Workflow Clarity

The five-step workflow is clearly sequenced with an explicit data-alignment precondition and interpretation criteria serving as pass/fail checkpoints before screening/combination; not 5 because there is no explicit verify step (e.g. checking CSV alignment) before invoking the tool.

4 / 5

Progressive Disclosure

At ~135 lines with no bundle files, the single SKILL.md inlines substantial material (Common Pitfalls, three Factor Combination Methods) that belongs in separate reference files — 'some structure but content that should be separate is inline'; the under-50-line exception does not apply.

3 / 5

Total

14

/

20

Passed

Description

58%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 communicates a specific, well-scoped capability set for quant factor research, but lacks an explicit 'Use when...' trigger clause, which limits discoverability and completeness. Adding natural trigger phrasing would resolve both gaps.

Suggestions

Append an explicit trigger clause, e.g. "Use when the user wants to evaluate, backtest, screen, or combine quantitative factors, or mentions IC, IR, or quantile backtests."

Include natural synonyms users actually say ("factor backtest", "factor validity", "factor screening") alongside the technical IC/IR terminology to improve trigger-term coverage.

Optionally mention the factor-decay and industry-comparison capabilities the body covers so the description's coverage matches the skill's scope.

DimensionReasoningScore

Specificity

Names the domain and three concrete capabilities ("IC/IR analysis, quantile backtesting, and factor combination"), matching the 'several specific actions; minor gaps' anchor; not 5 because narrower than the body's actual coverage (screening, decay analysis, industry comparison).

4 / 5

Completeness

The "what" is explicit, but the "when" is only weakly implied by "Suitable for cross-sectional factor evaluation across multiple instruments" — there is no explicit 'Use when...' trigger clause, which caps completeness at 3 per the judging guidelines.

3 / 5

Trigger Term Quality

Keywords like "IC/IR", "quantile backtesting", and "cross-sectional factor evaluation" are relevant but natural user phrasings such as "backtest a factor", "factor validity", or "alpha" are missing, fitting the 'some relevant keywords but missing common variations' anchor.

3 / 5

Distinctiveness Conflict Risk

"Factor research framework with IC/IR analysis" carves a distinct quant niche with minimal conflict risk; not 5 because the absence of explicit trigger phrases leaves minor overlap with generic backtesting/data-analysis skills.

4 / 5

Total

14

/

20

Passed

Validation

93%

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

Validation — 15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

15

/

16

Passed

Repository
charliedream1/ai_quant_trade
Reviewed

Table of Contents

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