Content
72%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
An actionable, well-structured body with executable commands and good reference navigation, weakened by time-sensitive inline data and the absence of explicit validation/feedback loops in the workflow.
Suggestions
Move hard-coded dates and APY percentages out of the output sample into a clearly labelled example, or mark them as illustrative, to avoid time-sensitive noise that decays.
Add explicit validation checkpoints between workflow steps (e.g. verify API response before optimizing, confirm positions parsed before projecting yields) with a fix→retry loop rather than only delegating to errors.md.
Cross-link the examples.md reference from the Examples section so the one-level-deep navigation is consistent with errors.md and implementation.md.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Mostly lean and free of concept over-explanation, but inline time-sensitive values (e.g. '2025-01-15 15:30 UTC', hard-coded APY percentages in the output block) add noise that does not earn its place. | 2 / 3 |
Actionability | Fully executable, copy-paste-ready commands with concrete flags ('--asset ETH --amount 10', '--optimize --positions "10 ETH @ lido 4.0%..."') plus a specific sample output table. | 3 / 3 |
Workflow Clarity | Steps are clearly numbered and sequenced, but there are no explicit validation checkpoints or fix→retry feedback loops; error handling is delegated to a reference file rather than inline. | 2 / 3 |
Progressive Disclosure | SKILL.md is an overview that points to real one-level-deep references (errors.md, implementation.md) plus a Resources list, with content appropriately split and easy to navigate. | 3 / 3 |
Total | 10 / 12 Passed |