Content
82%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 a lean, well-structured seven-step workflow with concrete commands and a built-in evaluation checkpoint. Its main gaps are placeholder-laden commands without a literal example and a crash path that reverts without a fix-and-retry loop.
Suggestions
Add one fully-resolved example invocation (e.g. /ar:run engineering/api-speed) showing the concrete git and python commands with real values, so the guidance is copy-paste ready.
Add an explicit fix-and-retry feedback loop for the CRASH case (read the error, attempt a fix, re-run run_experiment.py) rather than only reverting.
Consider splitting the strategy-escalation table and self-improvement check into a short reference file to keep the core iteration loop as the primary surface.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is lean and tightly organized with no padding or explanation of concepts Claude already knows; every line (steps, escalation table, rules) earns its place. | 5 / 5 |
Actionability | Provides concrete executable commands (setup_experiment.py --list, git checkout/commit, run_experiment.py --single) but they rely on unresolved placeholders ({skill_path}, {domain}, {name}, {target}) with no literal worked example, leaving minor gaps. | 4 / 5 |
Workflow Clarity | Seven steps are clearly sequenced with an evaluation checkpoint (evaluate.py as ground truth) and explicit KEEP/DISCARD/CRASH reporting, but error recovery on crash is just 'Reverted' with no fix-and-retry feedback loop, a minor validation gap. | 4 / 5 |
Progressive Disclosure | Well-organized into clear sections (Usage, What It Does, Rules) and self-contained with no nested references, but it is over 50 lines and references runtime scripts rather than splitting deeper detail into doc files. | 4 / 5 |
Total | 17 / 20 Passed |