Content
100%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 body is an expert, actionable skill: terse prose, multiple executable code blocks, well-sequenced workflows with validation gates and feedback loops, and a clean one-level-deep reference structure with verified paths. It scores at the top of every dimension.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Terse expert voice ("Priors, not rules", "SFT-then-RL is not a law") that assumes competence and never re-explains RL/SFT basics; its length comes from domain breadth and evo-specific contracts, not padding. Not 2: there is little unnecessary explanation of concepts Claude already knows. | 3 / 3 |
Actionability | Multiple copy-paste-ready blocks: the warm-start Python snippet, the .evo/cache Python snippet, `evo config set max-attempts 1`, the TRL `report_to` one-liners, and the ideator `Task(...)` call. Not 2: these are executable code/commands, not pseudocode. | 3 / 3 |
Workflow Clarity | A numbered 5-step mid-eval/early-stop sequence with explicit validation checkpoints ("Mini-eval after each checkpoint", "Early-stop on regression", "Save the BEST checkpoint") plus the smoke-run gate and spot-check feedback loops. Not 2: validation steps and error-recovery loops are explicit, not implicit. | 3 / 3 |
Progressive Disclosure | The body is an overview pointing through an ASCII reference tree with per-file "Read FIRST/Read when..." triggers, one level deep; every cited path (glue.md, trace-schema.md, diagnostics.md, false-progress.md, observability.md, rl/art.md, sft/tinker.md, serving/vllm.md) was verified present. Not 2: content is appropriately split and clearly signaled rather than inlined. | 3 / 3 |
Total | 12 / 12 Passed |