Content
100%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 an exemplary orchestration skill body: fully executable commands for every mode, a numbered activation sequence with halt-on-failure validation, explicit exit-code and error-handling semantics, and clean one-level-deep delegation of detail to existing reference and script files. No padding or over-explanation competes for the context window.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is lean and dense: every sentence carries a command, path, exit code, or policy ("Exit 0 is a complete run; 1 means a case errored or timed out... 3 that no harness is recorded"), with no concept explanations Claude already knows and nothing to trim. | 5 / 5 |
Actionability | Complete, copy-paste-ready commands are given for each mode ("uv run {skill-root}/scripts/run_evals.py --cases <cases-file> --skill-path <skill> --output-dir <dir> --mode quality|baseline|variant [--variant-path <skill>]") plus config-resolution, conversion, and aggregation commands, covering all common cases. | 5 / 5 |
Workflow Clarity | "On activation" is a numbered sequence with explicit validation checkpoints ("halt with a clear error if not", "If nothing is found, halt and say so"), exit-code semantics, and error-recovery feedback loops (a grader error marks the case `grading_error`, "never a default verdict"). | 5 / 5 |
Progressive Disclosure | The body is a clear overview — a mode table mapping each mode to its script and reference — with deep material explicitly delegated one level deep to real bundle files ("The full shape and the strong-versus-weak expectation taxonomy are in references/eval-format.md", plus grader.md, harness.md, description-optimization.md, self-improvement.md), all of which exist. | 5 / 5 |
Total | 20 / 20 Passed |