cover-letter-generator
Generates a journal-ready cover letter from manuscript metadata, highlights, and journal-fit notes. Use when preparing an academic submission package and you need editor-facing language that clearly states novelty, relevance, declarations, and corresponding-author details.
84
81%
Does it follow best practices?
Impact
Pending
No eval scenarios have been run
Passed
No known issues
Quality
Discovery
100%Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.
This is an excellent skill description that clearly defines a specific academic writing task, includes natural trigger terms researchers would use, and explicitly states both what the skill does and when to use it. The niche is well-defined and unlikely to conflict with other skills.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: generates a cover letter from manuscript metadata/highlights/journal-fit notes, and specifies the output includes novelty, relevance, declarations, and corresponding-author details. | 3 / 3 |
Completeness | Clearly answers both what ('Generates a journal-ready cover letter from manuscript metadata, highlights, and journal-fit notes') and when ('Use when preparing an academic submission package and you need editor-facing language...'). | 3 / 3 |
Trigger Term Quality | Includes strong natural keywords users would say: 'cover letter', 'manuscript', 'journal', 'academic submission', 'editor', 'novelty', 'corresponding-author', 'declarations'. These are terms researchers naturally use when preparing submissions. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive niche — academic journal cover letters are a very specific task unlikely to overlap with other skills. The combination of 'journal-ready', 'manuscript metadata', 'editor-facing language', and 'submission package' creates a clear, unique trigger profile. | 3 / 3 |
Total | 12 / 12 Passed |
Implementation
62%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a well-structured skill with a clear workflow and good validation/recovery mechanisms. Its main weaknesses are the lack of concrete output examples (what does a good cover letter actually look like?) and moderate verbosity in sections that explain concepts Claude already understands about academic writing conventions. The workflow clarity is the strongest dimension, with explicit input validation and a quality checklist.
Suggestions
Add a concrete example showing a sample input set and the resulting cover letter output (even abbreviated) so Claude has a clear target format to follow.
Trim the 'When to Use' and 'When Not to Use' sections significantly—Claude can infer most of these conditions from the skill description and workflow.
Consider moving the Journal-Specific Declaration Matrix to a referenced file since it's supplementary detail that adds length without being needed on every invocation.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is reasonably well-structured but includes some unnecessary verbosity. Sections like 'When to Use' and 'When Not to Use' contain guidance Claude could infer, and the 'Journal-Specific Declaration Matrix' restates fairly obvious academic norms. Some tightening would improve token efficiency. | 2 / 3 |
Actionability | The skill provides clear structural guidance and a step-by-step workflow, but lacks concrete examples of actual cover letter output. There are no example inputs/outputs showing what a completed letter looks like, which would make the guidance much more executable. The references to template files (assets/cover_letter_template.md) are good but the actual content isn't shown. | 2 / 3 |
Workflow Clarity | The workflow is clearly sequenced with five numbered steps, includes explicit validation as the first step with a well-defined missing-input recovery mechanism, and ends with a quality checklist. The feedback loop for missing inputs (validate → report → stop) is well-defined. | 3 / 3 |
Progressive Disclosure | The skill references external files (assets/cover_letter_template.md, references/guide.md) which is good progressive disclosure, but the main file itself is quite long with content that could be split out (e.g., the declaration matrix, the detailed drafting workflow steps). The structure is clear but the balance between overview and detail could be improved. | 2 / 3 |
Total | 9 / 12 Passed |
Validation
90%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 10 / 11 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
frontmatter_unknown_keys | Unknown frontmatter key(s) found; consider removing or moving to metadata | Warning |
Total | 10 / 11 Passed | |
- Repository
- aipoch/medical-research-skills
- Commit
e1f6461
- Reviewed
Table of Contents
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