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asset-allocation

Asset allocation theory and optimizer usage — MPT / Black-Litterman / risk budgeting / all-weather strategy, including guides for 4 optimizers and rebalancing rules.

62

Quality

73%

Does it follow best practices?

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SecuritybySnyk

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/asset-allocation/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

65%

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

Highly actionable optimizer guidance carried by a concise reference core, but the body is a single large file padded with theory Claude already knows and lacks validation checkpoints in its rebalancing workflow. Trimming known theory and splitting detail into reference files would raise both conciseness and progressive disclosure.

Suggestions

Cut or compress the MPT / Black-Litterman / risk-budgeting "Core idea" explanations that restate concepts Claude already knows, keeping only the project-specific formulas and parameters.

Add a validation step to the rebalancing workflow (e.g., assert sum of weights ≤ 1.0 and flag any single-asset weight above a bound before writing signals).

Move the detailed optimizer reference and the cross-asset correlation matrix into separate files under references/, linked from a lean overview, to improve progressive disclosure.

DimensionReasoningScore

Conciseness

The optimizer reference (config snippets, parameter tables, decision tree) is efficient, but the Asset Allocation Theory section re-explains MPT, Black-Litterman, and risk budgeting concepts Claude already knows and could be tightened.

2 / 3

Actionability

It provides copy-paste-ready `config.json` blocks for each of the four optimizers, concrete parameter tables with defaults, a selection decision tree, and an executable rebalancing code example.

3 / 3

Workflow Clarity

The optimizer-selection decision tree gives a clear sequence, but the rebalancing workflow (a batch operation) has no validation checkpoint (e.g., verify weights sum to 1.0 or flag extreme weights), capping clarity at 2.

2 / 3

Progressive Disclosure

The body is well-sectioned but monolithic at ~285 lines with no external reference files; the detailed theory and optimizer reference are inline content that could be split into separate files.

2 / 3

Total

9

/

12

Passed

Description

82%

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 specific, well-scoped description with strong trigger-term coverage and clear distinctiveness, weakened only by the absence of an explicit "Use when..." trigger clause. Adding usage triggers would round it out.

Suggestions

Append an explicit trigger clause such as "Use when the user needs asset allocation advice, portfolio optimization, choosing among optimizers, or rebalancing rules" to lift completeness to 3.

DimensionReasoningScore

Specificity

The description enumerates multiple concrete capabilities — "MPT / Black-Litterman / risk budgeting / all-weather strategy" and "guides for 4 optimizers and rebalancing rules" — rather than vague abstractions.

3 / 3

Completeness

It clearly answers what the skill does but lacks any "Use when..." clause or equivalent explicit trigger guidance, so the "when" is only implied — capping completeness at 2 per the rubric guideline.

2 / 3

Trigger Term Quality

It covers natural terms a user would say ("asset allocation", "optimizer", "rebalancing") alongside the domain vocabulary (MPT, Black-Litterman, all-weather) the target audience uses.

3 / 3

Distinctiveness Conflict Risk

The asset-class framing and named frameworks carve out a clear niche ("asset allocation theory and optimizer usage") unlikely to conflict with other skills.

3 / 3

Total

11

/

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
charliedream1/ai_quant_trade
Reviewed

Table of Contents

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