Content
50%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
The skill body is well-organized with clear workflows and concrete library code, but is held back by undefined helper functions that break executability, absent validation checkpoints in the training workflows, and three referenced files that do not exist on disk.
Suggestions
Define or stub the helper functions (create_dataset, parse_preferences, create_preference_dataset, majority_vote) so the code is executable end-to-end, or note them as user-supplied.
Add validation/feedback checkpoints in the workflows — e.g., evaluate the reward model's accuracy before PPO, and verify revised-response quality before fine-tuning.
Create the missing references/ files (constitution-design.md, rlaif-comparison.md, cot-critique.md) or remove the dead links so progressive disclosure navigation actually resolves.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is mostly efficient and action-oriented with code, but includes mild over-explanation (a 'Key concept' gloss, a re-explained constitution, and restated principles) that could be tightened. Not a 3 because some tokens restate what Claude already knows; not a 1 because it is not padded with basic concept explanations. | 2 / 3 |
Actionability | Provides substantial concrete Python (SFTTrainer, RewardTrainer, PPOTrainer, pipeline), but several steps rely on undefined helper functions — create_dataset(), parse_preferences(), create_preference_dataset(), majority_vote(), model.evaluate() — making the code non-copy-paste-executable. Not a 3 because of these incomplete pseudocode gaps; not a 1 because real, specific library calls are present. | 2 / 3 |
Workflow Clarity | Workflows are clearly sequenced into numbered steps (SL phase, RL phase), but there are no validation checkpoints or feedback loops for costly batch/training operations (e.g., verifying reward-model quality before PPO, or checking critique quality before fine-tuning). Per the rubric, missing validation in these contexts caps workflow clarity at 2; not a 1 because the sequence is explicit. | 2 / 3 |
Progressive Disclosure | The body signals three one-level-deep references (constitution-design.md, rlaif-comparison.md, cot-critique.md), but the references/ directory does not exist, so the links are dead. Per the rubric, progressive disclosure should be scored against the actual bundle structure, which here has structure but broken navigation; not a 1 because the overview is organized and references are shallow, not a deeply nested chain. | 2 / 3 |
Total | 8 / 12 Passed |