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.

Quality

77%

Does it follow best practices?

Impact

—

No eval scenarios have been run

Securityby

Passed

No known issues

Optimize this skill with Tessl

npx tessl skill review --optimize ./scientific-skills/Academic Writing/cover-letter-generator/SKILL.md

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 task, includes natural trigger terms researchers would use, and explicitly states both what the skill does and when to use it. The description is concise yet comprehensive, with a well-defined niche that minimizes conflict risk 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 a user 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 'academic submission package' creates a clear, unique trigger profile.	3 / 3
	Total	12 / 12 Passed

Implementation

55%

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The skill has strong workflow clarity with a well-structured validation-first approach and quality checklist, but suffers from broken file references and lack of concrete output examples. The content is moderately concise but could be tightened in places where it explains things Claude already understands about professional academic writing. Adding actual example output and ensuring referenced bundle files exist would significantly improve this skill.

Suggestions

Add a concrete example of a completed cover letter (or at least a representative excerpt) showing the expected output format with realistic placeholder content.

Either provide the referenced bundle files (assets/cover_letter_template.md and references/guide.md) or remove the references to avoid broken paths.

Trim the 'When to Use' and 'When Not to Use' sections—Claude can infer most of these from the skill description; keep only the non-obvious constraints like 'do not invent results or declarations'.

Consolidate the 'Deterministic Rules' section into the 'Output Contract' section to reduce redundancy and improve token efficiency.

Dimension	Reasoning	Score
Conciseness	The skill is reasonably well-structured but includes some unnecessary verbosity. The 'When to Use' and 'When Not to Use' sections could be tightened, and some instructions (e.g., explaining what not to hype) are things Claude already knows about professional academic writing. The journal-specific declaration matrix adds value but could be more compact.	2 / 3
Actionability	The skill provides clear structural guidance and a specific output contract, but lacks a concrete example of a completed cover letter or even a partial example showing the expected output format. The workflow steps are instructional rather than executable—there's no template content shown inline, and the referenced assets (assets/cover_letter_template.md, references/guide.md) don't exist in the bundle.	2 / 3
Workflow Clarity	The drafting workflow is clearly sequenced with five explicit steps, includes a validation-first approach with a well-defined missing-input recovery mechanism that acts as a feedback loop, and ends with a quality checklist. The stop-before-drafting gate in step 1 is a strong validation checkpoint.	3 / 3
Progressive Disclosure	The skill references 'assets/cover_letter_template.md' and 'references/guide.md' but no bundle files are provided, meaning these references are broken. The skill itself is also somewhat monolithic—the declaration matrix and quality checklist could potentially be in separate files, but more critically, the referenced files simply don't exist.	1 / 3
	Total	8 / 12 Passed

Validation

90%

Warnings & errors only

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: 73f6514

Reviewed: about 17 hours ago

Table of Contents

Discovery Implementation Validation

Is this your skill?

If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.