Content
86%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.
Excellent executable reference content with near-zero padding, but it is a monolithic single-file skill: the entire API surface lives in SKILL.md with no progressive disclosure via reference files, and no explicit local validation loop for iterating on a verifier.
Suggestions
Move the exhaustive built-ins list, the '[judge]' options paragraph, and the aggregation-mode semantics into a references/ file (e.g. references/api.md), keeping SKILL.md to setup, core patterns, and 'When to reach for what', so the full reference is loaded only when needed.
Add a short validation step showing how to run the verifier outside Harbor (e.g. 'uvx --from 'harbor-rewardkit==0.2.*' rewardkit /tests') and inspect reward-details.json to debug failing criteria before a real run.
Restructure the '[judge] options' paragraph as a bulleted list or table so each option (timeout, reasoning_effort, guard, isolated, samples) is scannable rather than buried in dense prose.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Lean and dense throughout — no padding and no explanations of concepts Claude already knows; every sentence carries tool-specific facts ('Zero-parameter criteria auto-register', 'all-pass, any-pass, and required-pass count a score as passing only when it is 1.0', the network_mode baseline/override semantics). Not 4 because no instance of over-explanation was found to trim; the length is informational, not verbosity. | 5 / 5 |
Actionability | Fully executable, copy-paste-ready examples cover the common cases: the 'uvx --from 'harbor-rewardkit==0.2.*' rewardkit /tests' test.sh, Python criteria with '@criterion' and parameterized calls, judge TOMLs for LLM/agent/JEV judges, and reward.toml aggregation snippets with expected JSON outputs. Not 4 because the examples are complete and executable as written, not just fragments with gaps. | 5 / 5 |
Workflow Clarity | Sections form a coherent sequence (setup → programmatic → custom → judges → scoring → multi-reward → multi-step → selection guide) with most checkpoints present: output files are documented ('/logs/verifier/reward-details.json — per-criterion results, judge reasoning, errors') and risky behaviors are flagged (isolated=True to avoid corrupting the workspace). Not 5 because there is no explicit validation/feedback loop for locally running and debugging a verifier before a Harbor run; not 3 because the sequence and checkpoints are clearly better than implicit. | 4 / 5 |
Progressive Disclosure | The body is well-sectioned with clear headers, but roughly 340 lines of exhaustive reference material — the full built-ins list, the dense '[judge]' options paragraph, all aggregation modes, and multi-step semantics — are fully inlined in SKILL.md with no bundle files at all (the only pointer is external: 'See examples/tasks/reward-kit-example/ in the Harbor repo'). This matches the anchor 'content that should be separate is inline'; not 4 because there are no well-signaled one-level-deep references to offset the inlined volume. | 3 / 5 |
Total | 17 / 20 Passed |