asset-allocation

Asset allocation theory and optimizer usage — MPT / Black-Litterman / risk budgeting / all-weather strategy, including guides for 5 optimizers and rebalancing rules.

Quality

73%

Does it follow best practices?

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Securityby

Passed

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Fix and improve this skill with Tessl

tessl review fix ./agent/src/skills/asset-allocation/SKILL.md

Quality

Content

65%

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

The content is highly actionable with concrete configs, a decision tree, and templates, but it is weighed down by lengthy theory exposition Claude already knows and is delivered as a monolithic document with no progressive disclosure or validation checkpoints.

Suggestions

Trim or move the MPT/Black-Litterman/all-weather theory derivations to a separate reference file, keeping only the decision-relevant summary in SKILL.md.

Add an explicit validation checkpoint before producing output (e.g. 'verify sum of weights <= 1.0 and each weight within bounds') to turn the workflow into a validate-then-emit loop.

Split the per-optimizer parameter tables and examples into references/ files (e.g. optimizers.md) and link them from a concise overview, enabling one-level-deep progressive disclosure.

Dimension	Reasoning	Score
Conciseness	Large stretches re-derive theory Claude already knows (MPT optimization problem, the full Black-Litterman posterior formula, risk-contribution math, all-weather environment tables, an annotated correlation matrix), while the optimizer configs and notes are lean; overall mostly efficient but padded with unnecessary theory exposition.	2 / 3
Actionability	Provides executable config.json blocks for all five optimizers with parameter tables, a copy-paste rebalancing snippet, a selection decision tree, and a concrete output-format template — copy-paste ready guidance.	3 / 3
Workflow Clarity	The optimizer-selection decision tree gives a clear sequence, but there are no explicit validation checkpoints or validate-fix-retry feedback loops (e.g. verifying weights sum to <=1.0 before output), leaving checkpoints implicit.	2 / 3
Progressive Disclosure	No bundle files exist and nothing is split out, so the ~318-line SKILL.md is a monolithic single file whose theory and per-optimizer API-reference tables could appropriately live in separate reference files despite clear section headers.	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.

The description is specific and distinctive, naming concrete frameworks and capabilities with natural trigger terms. Its main weakness is the absence of an explicit 'Use when...' clause telling Claude when to invoke the skill.

Suggestions

Add an explicit 'Use when ...' clause, e.g. 'Use when configuring portfolio allocation, selecting among the built-in optimizers, or setting rebalancing rules.'

Consider naming a couple of optimizer terms directly in the description (e.g. 'risk_parity', 'turnover_aware') to broaden natural trigger coverage.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete capabilities — 'MPT / Black-Litterman / risk budgeting / all-weather strategy', 'guides for 5 optimizers and rebalancing rules' — matching the anchor that names several specific actions.	3 / 3
Completeness	Clearly states what the skill does, but contains no 'Use when...' or equivalent explicit trigger guidance for when to invoke it, so completeness is capped at 2 per the judging guidelines.	2 / 3
Trigger Term Quality	Uses natural domain vocabulary a user would actually say ('asset allocation', 'MPT', 'Black-Litterman', 'risk budgeting', 'all-weather', 'optimizer', 'rebalancing'), giving good coverage of likely trigger terms.	3 / 3
Distinctiveness Conflict Risk	The named asset-allocation frameworks plus system-specific optimizers carve out a clear, narrow niche that is unlikely to conflict with other skills.	3 / 3
	Total	11 / 12 Passed

Validation

93%

Warnings & errors only

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

Criteria	Description	Result
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	15 / 16 Passed

Repository: HKUDS/Vibe-Trading
Path: agent/src/skills/asset-allocation/SKILL.md
Commit: 8643fcd

Reviewed: 2 days ago

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