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alpha-zoo

Browse and bench the bundled alpha zoos — prebuilt cross-sectional factor libraries (Kakushadze 101, GTJA 191, Qlib 158, Fama-French / Carhart). Use when the user asks "which alphas exist", wants metadata on a named alpha, or wants to run IC/IR on a whole zoo over a universe.

75

Quality

93%

Does it follow best practices?

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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 skill body is lean, dense, and highly actionable, routing user requests to specific tools with exact parameters and guarding against real pitfalls. Its main gap is the absence of explicit validation/verification checkpoints before batch bench operations, which keeps workflow clarity below full marks.

Suggestions

Add an explicit pre-bench validation step to the decision tree or a short workflow: e.g. run `alpha_zoo action=health` (and confirm the universe loader is implemented) before invoking `alpha_bench` on a whole zoo, since benching is a batch operation.

Spell out a feedback loop for bench failures — e.g. when `alpha_bench` reports `universe loader for X not yet implemented` or `loaded=0`, route back to `action=health` and surface per-error reasons before retrying.

Clarify the relationship between the Purpose section and the frontmatter description, which partially restate each other; merging would tighten the body further.

DimensionReasoningScore

Conciseness

The body is dense and token-efficient — a tool table, a parameter-level decision tree, an inventory table, constraints, and pitfalls — and assumes Claude's competence without explaining what factors, IC, or IR are, matching the 'lean and efficient; every token earns its place' anchor.

3 / 3

Actionability

The decision tree gives exact, copy-pasteable invocations with concrete parameters (e.g. `alpha_zoo` with `action=list_alphas, theme=momentum`) and the tool table enumerates specific action names, fully executable rather than abstract guidance.

3 / 3

Workflow Clarity

The decision tree provides clear routing of requests to tools and the pitfalls act as implicit guardrails, but there is no explicit validation checkpoint or feedback loop (e.g. check `action=health` before benching a whole zoo, confirm a universe loader exists before a long run); the rubric caps batch operations at 2 without such validation.

2 / 3

Progressive Disclosure

A compact, well-organized single file (Purpose, Tools, Decision Tree, Inventory, Constraints, Pitfalls, Reference) with clearly signaled, one-level-deep references to source paths in the Reference section; no nested reference chains and no bundle bloat, fitting the well-organized-sections anchor.

3 / 3

Total

11

/

12

Passed

Description

100%

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

A well-crafted description that states concrete capabilities, names the specific libraries in scope, and provides explicit natural-language trigger phrases with a clear 'Use when' clause. It is concise, distinctive, and unlikely to conflict with other skills.

DimensionReasoningScore

Specificity

Names multiple concrete actions ("Browse", "bench", get metadata, run IC/IR) and enumerates the specific factor libraries (Kakushadze 101, GTJA 191, Qlib 158, Fama-French / Carhart), matching the 'lists multiple specific concrete actions' anchor rather than the partial 2 anchor.

3 / 3

Completeness

Explicitly answers both what (browse/bench prebuilt cross-sectional factor libraries) and when via a clear "Use when the user asks..." clause, satisfying the 'both what AND when with explicit triggers' anchor.

3 / 3

Trigger Term Quality

Includes natural quoted phrases a user would say — "which alphas exist", "metadata on a named alpha", "run IC/IR on a whole zoo over a universe" — giving good coverage of natural trigger terms rather than only jargon.

3 / 3

Distinctiveness Conflict Risk

Occupies a clear niche (cross-sectional alpha zoos / factor benchmarking) with distinctive triggers unlikely to fire for unrelated skills, matching the 'clear niche with distinct triggers' anchor.

3 / 3

Total

12

/

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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