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.
A high-quality, reference-style skill body: nearly every token conveys Reward-Kit-specific detail Claude cannot know, with executable examples for every feature area and clear decision guidance. The main improvement opportunities are tightening the densest prose passages (environment/network modes, judge options) and moving the built-ins API catalog and [judge] options into a reference file to keep SKILL.md a leaner overview.
Suggestions
Move the full built-ins API listing and the "Useful [judge] options" catalog into a references/ file (e.g., references/builtins.md and references/judge-options.md), keeping SKILL.md as a lean overview with one-level-deep pointers.
Add an explicit validation checkpoint: "Run `uvx --from 'harbor-rewardkit==0.2.*' rewardkit /tests` locally and inspect /logs/verifier/reward-details.json for errors before finalizing the task" as a step near the top of the workflow.
Tighten the shared-vs-separate environment section — the phase-override vs. baseline distinction spans three paragraphs of intertwined conditionals that could be reduced to a short decision table.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is dense and almost entirely novel API detail Claude cannot know (built-ins, TOML schemas, aggregation modes), with no filler explaining known concepts. It is not a 5 because a few passages could be trimmed or tightened — notably the shared-vs-separate environment paragraphs ("Put `[verifier].network_mode` only when verify() needs different network access... (a phase override, not a baseline)") and the single-paragraph wall of "Useful `[judge]` options" — but these are minor instances, matching the 4 anchor. | 4 / 5 |
Actionability | Fully executable, copy-paste-ready guidance throughout: the exact `uvx --python '>=3.12,<3.14' --from 'harbor-rewardkit==0.2.*' rewardkit /tests` invocation, runnable Python and TOML snippets, directory layouts, and concrete output JSON. Specific examples cover the common cases (built-ins, custom criteria, LLM/agent/JEV judges, multi-reward aggregation). It is not a 4 because there are no gaps — even edge configuration (extras, env keys, CA certificates) is given concretely. | 5 / 5 |
Workflow Clarity | Sections follow the natural authoring sequence — setup (test.sh, environments), then criteria types, then judges, then multi-reward/multi-step structure, then output files — with a "When to reach for what" decision guide. Validation feedback is partially present (reward-details.json exposes per-criterion results and errors), but there is no explicit "run the verifier locally and check reward-details.json before shipping" checkpoint loop, which keeps it below 5; the sequence and most checkpoints match the 4 anchor. | 4 / 5 |
Progressive Disclosure | A single-file skill with no bundle, but well organized: clear hierarchical headers, an early worked example, a decision guide, and one clearly signaled external pointer ("See `examples/tasks/reward-kit-example/` in the Harbor repo"). It is not a 5 because at ~340 lines some reference-grade material (the full built-ins API list, the `[judge]` options catalog) is inlined in SKILL.md where a references/ file would keep the overview lean; it is not a 3 because structure and navigation are genuinely good, not merely present. | 4 / 5 |
Total | 17 / 20 Passed |