Content
57%Scale 1-5Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a well-organized skill with clear command structures and output format specifications, making it easy to understand the three workflows. However, it lacks concrete executable examples (no sample tool invocations or example outputs), has minimal error handling/validation steps for operations that depend on external API calls, and the referenced bundle files are not provided, making it impossible to verify the progressive disclosure structure actually works.
Suggestions
Add a concrete example showing a sample tool invocation (e.g., `edgar.get_financials('AAPL')` with expected return shape) so Claude knows exactly how to call the bundled tools.
Add validation checkpoints after data retrieval steps, e.g., 'Verify EDGAR response contains revenue and net_income fields before proceeding to scoring' with explicit fallback behavior.
Include a brief example of a completed `/score` output (even abbreviated) so the output format is unambiguous and copy-paste verifiable.
Integrate reference file pointers inline within the workflow steps (e.g., 'Apply framework per references/methodology.md §Pillar Definitions') rather than listing them all at the bottom.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Generally efficient and well-structured. Minor verbosity in the 'When to Use' section (explaining what each command does before the Commands section repeats it) and some light padding in Execution Rules ('Use plain English, avoid generic finance fluff'), but overall respects Claude's intelligence and avoids explaining basic concepts. | 4 / 5 |
Actionability | The skill provides clear command structures and output format templates, but lacks executable code examples. The workflows reference tools (tools/edgar.py, tools/market.py) and methodology files without showing concrete usage patterns, API calls, or example invocations. The output format is well-specified but the execution steps are high-level descriptions rather than concrete executable guidance. | 3 / 5 |
Workflow Clarity | Each command has a numbered sequence of steps, which is good. However, there are no validation checkpoints or feedback loops. The only error handling is a single sentence ('If a tool call fails, state exactly what data is missing and continue'), which is minimal. For a workflow that pulls external data from EDGAR and market APIs—operations that can fail—there should be explicit validation steps between data retrieval and analysis. | 3 / 5 |
Progressive Disclosure | The skill references external files (references/methodology.md, references/scoring.md, references/edgar-guide.md, tools/edgar.py, tools/market.py) which is good structure, but no bundle files are provided to verify these exist. The references are listed at the bottom rather than being clearly signaled inline where they're relevant. The SKILL.md itself is a reasonable length but could better signal when to consult which reference file. | 3 / 5 |
Total | 13 / 20 Passed |