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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.

64

Quality

77%

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SecuritybySnyk

Passed

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tessl review fix ./agent/src/skills/factor-research/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

87%

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

The body is lean, highly actionable, and well-structured with concrete code, threshold tables, and one-level-deep references to sibling skills; its main gap is the absence of explicit validation/feedback checkpoints in the workflow.

Suggestions

Insert a validation checkpoint after computing the factor/return CSVs (e.g. 'Verify rows and columns align exactly before calling factor_analysis') and a feedback loop after interpretation (e.g. 'If IC > 0.10, re-check for look-ahead bias before accepting the factor').

Add a brief 'verify outputs' step that checks ic_summary.json values against the threshold table before proceeding to factor combination.

Optionally split the lengthy 'Common Pitfalls' section into a referenced file if the skill grows, though it currently remains lean enough to keep inline.

DimensionReasoningScore

Conciseness

The body is dense with domain-specific, actionable material (IC/IR threshold tables, combination formulas, look-ahead-bias mechanics) and assumes Claude's competence without explaining basic concepts like CSVs or pandas, matching the lean-and-efficient anchor.

3 / 3

Actionability

It provides executable code (the Registry example), a concrete tool-parameter table, numeric IC/IR cutoffs, and copy-paste-ready combination formulas, matching the fully-executable anchor.

3 / 3

Workflow Clarity

The 5-step workflow is clearly sequenced, but it lacks explicit validation checkpoints or feedback loops (e.g. verify CSV alignment before calling the tool; re-check for look-ahead bias when IC > 0.10), which the batch-operation guideline says should cap workflow clarity at 2.

2 / 3

Progressive Disclosure

Content is well-organized into clear sections and references two sibling skills with clearly signaled, one-level-deep pointers ('see the multi-factor skill's ZooSignalEngine', 'see the alpha-zoo skill'), matching the well-organized-overview anchor.

3 / 3

Total

11

/

12

Passed

Description

67%

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 factor-analysis techniques, but lacks an explicit 'Use when...' trigger clause and only partially covers the natural trigger terms a user would say.

Suggestions

Add an explicit 'Use when...' trigger clause, e.g. 'Use when evaluating single-factor predictive power, comparing factors, or determining multi-factor combination weights.'

Broaden trigger-term coverage with common phrasings like 'factor testing', 'alpha research', and 'single-factor analysis' so users can find the skill via varied wording.

Keep the current concrete action list, but ensure the 'when' guidance is as explicit as the 'what' to lift completeness from implied to explicit.

DimensionReasoningScore

Specificity

Lists three concrete analytical techniques ('IC/IR analysis, quantile backtesting, and factor combination') plus 'cross-sectional factor evaluation', matching the multiple-specific-actions anchor; above 2 which only names a domain plus some actions.

3 / 3

Completeness

The 'what' is clearly stated, but the 'when' is only implied via 'Suitable for cross-sectional factor evaluation' with no explicit 'Use when...' trigger clause, which per the judging guidelines caps completeness at 2.

2 / 3

Trigger Term Quality

Domain-natural terms like 'Factor research', 'IC/IR analysis', and 'quantile backtesting' are present, but common variations a user might say (e.g. 'factor testing', 'alpha research', 'single-factor analysis') are missing, so it sits at the some-relevant-keywords anchor rather than full coverage.

2 / 3

Distinctiveness Conflict Risk

The IC/IR-plus-quantile-backtesting factor-research niche is specialized and unlikely to trigger for unrelated skills, matching the clear-distinct-niche anchor; it only nominally borders adjacent quant skills rather than broadly overlapping.

3 / 3

Total

10

/

12

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.

Validation15 / 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
HKUDS/Vibe-Trading
Reviewed

Table of Contents

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