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

61%

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

Quality

Content

57%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 highly actionable with concrete configs and a decision tree, but it explains theory Claude already knows and bundles everything into one long file with no validation checkpoints. Splitting reference material and adding output validation would raise the weakest dimensions.

Suggestions

Move the Asset Allocation Theory deep-dive and per-optimizer detail into separate reference files (e.g. THEORY.md, OPTIMIZERS.md), keeping SKILL.md as an overview with one-level-deep links.

Add explicit validation steps to the workflow, e.g. after computing weights: verify Σw = 1, leverage ≤ 1.0, and no negative weights before writing config.json.

Trim the MPT/Black-Litterman concept recap to the system-specific implementation details, and fix the Overview's "four optimizers" vs "5 Optimizers" inconsistency.

Dimension	Reasoning	Score
Conciseness	Mostly dense and reference-like, but the Asset Allocation Theory section re-explains MPT, Black-Litterman, and all-weather concepts Claude already knows, and the Overview says "four optimizers" while the guide covers five — a tighten-able inconsistency.	3 / 5
Actionability	Provides copy-paste-ready JSON config blocks for each optimizer, parameter tables with defaults, a selection decision tree, and an output template; the only gap is the illustrative rebalancing Python snippet using pseudocode function names.	4 / 5
Workflow Clarity	The optimizer-selection decision tree gives a clear sequence, but there are no explicit validation checkpoints (e.g. verify weights sum to 1, leverage ≤ 1.0) for the consequential output written into config.json.	3 / 5
Progressive Disclosure	Headers and tables are well organized, but the 310-line document is monolithic with no bundle files — theory deep-dives and per-optimizer details that belong in separate reference files are all inlined in SKILL.md.	3 / 5
	Total	13 / 20 Passed

Description

66%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 is specific and well-stocked with distinctive domain terms, but it omits any explicit "Use when..." trigger guidance, which caps its completeness. Adding a when-to-use clause would lift the weakest dimension.

Suggestions

Add an explicit trigger clause, e.g. "Use when the user asks about portfolio asset allocation, optimizer selection, or rebalancing configuration."

Lead with a verb-based action ("Configures and explains portfolio asset allocation optimizers...") to strengthen specificity.

Include common synonyms like "portfolio weights", "diversification", or "Sharpe" to broaden trigger-term coverage.

Dimension	Reasoning	Score
Specificity	Enumerates concrete components — "MPT / Black-Litterman / risk budgeting / all-weather strategy", "guides for 5 optimizers and rebalancing rules" — giving several specific items, though the actions are noun-based rather than verb-based like the 5-anchor example.	4 / 5
Completeness	The "what" is clear (asset allocation theory and optimizer usage with named frameworks), but there is no "Use when..." clause or equivalent trigger guidance, which caps completeness at 3 per the judging guidelines.	3 / 5
Trigger Term Quality	Contains natural domain terms a user would say ("asset allocation", "rebalancing", "risk budgeting", "all-weather", "optimizer"), with only minor synonyms missing (portfolio, weights, diversification, Sharpe).	4 / 5
Distinctiveness Conflict Risk	The named frameworks (Black-Litterman, all-weather, risk budgeting) carve a distinct niche within portfolio optimization, with only minor overlap risk against a generic portfolio-management skill.	4 / 5
	Total	15 / 20 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: e90b6c6

Reviewed: 3 days ago

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