Content
77%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
The body is highly actionable with full schemas, formulas, a worked example, and a clearly sequenced workflow including a validation checklist. Its main weakness is verbosity and inline reference-style detail that could be split into the existing research-basis file.
Suggestions
Move dense reference-style tables (personality trait effects, mood field definitions, emotion modifier tables) into references/research_basis.md and keep only the operationally essential subset inline.
Trim explanatory prose about emotion timescales and layer definitions, assuming Claude knows the basics, to improve token efficiency.
Add an explicit validate→fix→retry note around the Validation Checklist so the checkpoint reads as a feedback loop rather than a one-time pre-write check.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Mostly efficient tables encoding operational parameters Claude would not know (TPB scoring, appraisal checks), but it includes some explanatory prose ('Emotions operate on three timescales', 'Short-term, event-driven responses') that could be tightened. | 2 / 3 |
Actionability | Provides complete JSON output schemas, exact clamp/range values, a computation formula, a fully worked example calculation, and an executable script command — copy-paste ready guidance. | 3 / 3 |
Workflow Clarity | A numbered Execution Sequence (7 steps), a Selection Procedure (8 steps), and a pre-write Validation Checklist give a clear sequence with an explicit validation checkpoint for a complex process. | 3 / 3 |
Progressive Disclosure | A real one-level reference ('references/research_basis.md') and script are signaled, but the SKILL.md is a long monolith with detailed reference-style tables (modifiers, trait effects) that could live in the research basis file. | 2 / 3 |
Total | 10 / 12 Passed |