Content
80%Weight 40%Scale 1-3Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
The content is lean, highly actionable, and well-organized with copy-paste-ready CLI and Python examples, but it lacks an explicit error-recovery feedback loop for gate stages and keeps everything inline rather than splitting into bundle reference files.
Suggestions
Add an explicit validate->fix->retry feedback loop for gate stages (5, 9, 20) instead of only offering '--auto-approve'.
Move the detailed Python API, experiment-modes, and output-structure sections into separate reference files (e.g., PYTHON_API.md, OUTPUT_STRUCTURE.md) and link to them from SKILL.md.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is dense with executable commands, code, option tables, and an output tree, with no padding explaining concepts Claude already knows; every section earns its place. | 3 / 3 |
Actionability | Provides fully executable CLI commands ('researchclaw run --topic ... --auto-approve') and complete Python API code with imports, plus a troubleshooting table — copy-paste ready. | 3 / 3 |
Workflow Clarity | Sequenced prerequisites and three run options are present with a config-validation checkpoint, but there is no explicit validate->fix->retry feedback loop for the gate stages (5, 9, 20) beyond '--auto-approve', leaving checkpoints implicit. | 2 / 3 |
Progressive Disclosure | No bundle files exist and the 125-line body is monolithic; it references tool runtime outputs rather than skill bundle references, and content like the Python API / output structure could be split into separate one-level-deep reference files. | 2 / 3 |
Total | 10 / 12 Passed |