Content
57%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 highly actionable for system-specific configuration — executable JSON snippets, parameter defaults, and a selection decision tree — but roughly a third of it re-explains portfolio theory Claude already knows. There is no explicit end-to-end workflow with validation, and no progressive disclosure: everything lives inline in one large file.
Suggestions
Trim or move the Asset Allocation Theory section to a references/ file (e.g. THEORY.md), keeping only the practical guidance (parameter values, constraints advice) in SKILL.md.
Add an explicit end-to-end workflow with a validation step, e.g. select optimizer via the decision tree → write config.json → validate the config loads and the optimizer name is recognized before presenting the recommendation.
Make the rebalancing Python example executable (define or link where calculate_target_weights and signal_engine.py live) or label it explicitly as a pattern to adapt.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is dense and table-driven with no padded prose, but the ~80-line "Asset Allocation Theory" section explains textbook MPT, Black-Litterman, and risk-budgeting concepts (including generic pros/cons tables) that Claude already knows. Mostly efficient with some unnecessary explanation; not 2 because the system-specific material (parameters, defaults, China-focused examples) is genuinely additive. | 3 / 5 |
Actionability | Provides copy-paste-ready JSON config blocks for each optimizer, parameter tables with defaults, a selection decision tree, and an output-format template. Not 5 because the Python rebalancing snippet is partial pseudocode (calculate_target_weights and signal_engine.py are undefined) and the output example carries placeholder numbers. | 4 / 5 |
Workflow Clarity | The optimizer-selection decision tree gives a clear sequenced choice, but there is no end-to-end workflow (gather inputs → select optimizer → configure config.json → verify) and no validation checkpoint that the emitted configuration is valid. Steps are implied rather than explicitly ordered with checkpoints. | 3 / 5 |
Progressive Disclosure | Sections are well organized with clear headers, but the skill is a single ~290-line monolith with no bundle files or references; the theory deep-dive, correlation matrix, and rebalancing tables are candidates for separate reference files. Better than 2 because structure and navigation within the file are good. | 3 / 5 |
Total | 13 / 20 Passed |