Content
60%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.
The content is well-structured with a concrete output contract, defined inputs, and a real supporting script, but it is weighed down by generic boilerplate sections and a vague core recommendation step that lacks an executable method for sourcing journal/Impact Factor data.
Suggestions
Remove or condense the generic When to Use / When Not to Use / Required Inputs / Output Contract / Validation / Failure Handling / User Checkpoints boilerplate into skill-specific statements, keeping only what is unique to journal recommendation.
Add concrete guidance for the recommendation step — e.g., named data sources or how Impact Factors and acceptance rates should be obtained/verified — so the core action is executable rather than a generic "search for and recommend journals".
Tie validation checkpoints to concrete commands (e.g., the journal_ranker.py sort step) instead of relying on abstract "validate required inputs before execution" phrasing.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The core sections (Output Format, Workflow, Inputs) are efficient, but roughly seven generic boilerplate sections (When to Use, When Not to Use, Required Inputs, Output Contract, Validation and Safety Rules, Failure Handling, User Checkpoints) add padded, non-skill-specific prose such as "Use this skill when the request matches its documented task boundary" that could be tightened. | 3 / 5 |
Actionability | A concrete copy-paste output table template, explicit inputs, and a referenced sort script provide solid guidance, but the central recommendation step ("search for and recommend journals") gives no concrete method for obtaining journal data or Impact Factors, leaving a key execution gap. | 3 / 5 |
Workflow Clarity | A clear Assess→Recommend sequence is present with validation checkpoints (input validation, Quick Validation), feedback loops (Failure Handling), and a checklist (Quality Rules); the cap for missing validation on batch operations does not apply because validation is present, though checkpoints are somewhat generic rather than tied to concrete commands. | 4 / 5 |
Progressive Disclosure | The body is organized into clearly headed sections and the single bundle reference (scripts/journal_ranker.py) is explicitly signaled and verified to exist at one level of depth; the large inline output template is reasonable to keep in SKILL.md, with only minor organization gaps. | 4 / 5 |
Total | 14 / 20 Passed |