Content
61%Weight 40%Scale 1-5Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
The content is well-organized and actionable with concrete config examples, but it spends significant tokens explaining standard asset-allocation theory Claude already knows and lacks an explicit validation-gated workflow.
Suggestions
Trim or move the 'Asset Allocation Theory' section into a separate reference file, keeping SKILL.md focused on the built-in optimizers and rebalancing rules that Claude does not already know.
Turn the optimizer-selection decision tree plus config and output steps into an explicit numbered workflow with a validation checkpoint (e.g. confirm weight sum ≤ 1.0 before writing config.json).
Make the rebalancing code snippet fully executable by defining or stubbing the referenced helpers (calculate_target_weights, data_map, signals) instead of leaving them abstract.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The practical optimizer guide is efficient, but the lengthy 'Asset Allocation Theory' section explains standard finance concepts (MPT, Black-Litterman, risk budgeting, all-weather) that Claude already knows, adding unnecessary padding. | 3 / 5 |
Actionability | Provides copy-paste-ready config.json blocks, parameter tables, a selection decision tree, and an output template, with only minor gaps such as the rebalancing snippet referencing undefined helper functions. | 4 / 5 |
Workflow Clarity | A decision tree gives a selection sequence and the output section frames the deliverable, but there is no explicit multi-step workflow with validation checkpoints or error-feedback loops. | 3 / 5 |
Progressive Disclosure | The body is well-structured with clear headers, tables, and a decision tree, and no broken references; the main gap is that the theory section (which Claude largely already knows) could be split out or trimmed rather than inlined. | 4 / 5 |
Total | 14 / 20 Passed |