medication-adherence-message-gen
Use medication adherence message gen for academic writing workflows that need structured execution, explicit assumptions, and clear output boundaries.
30
13%
Does it follow best practices?
Impact
Pending
No eval scenarios have been run
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./scientific-skills/Academic Writing/medication-adherence-message-gen/SKILL.mdQuality
Discovery
0%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 description is deeply problematic: it conflates two unrelated domains (medication adherence messaging and academic writing) without explaining what the skill actually does or when it should be used. The language is abstract and buzzword-heavy ('structured execution', 'explicit assumptions', 'clear output boundaries') without conveying any concrete capabilities. Claude would be unable to reliably select this skill for any appropriate use case.
Suggestions
Clarify the actual purpose of the skill—decide whether it's about medication adherence messaging or academic writing, and describe concrete actions (e.g., 'Generates patient medication reminder messages' or 'Structures academic papers with explicit methodology sections').
Add an explicit 'Use when...' clause with natural trigger terms that users would actually say, such as 'Use when the user asks about medication reminders, pill schedules, or patient adherence communications.'
Remove vague filler phrases like 'structured execution' and 'clear output boundaries' and replace them with specific capabilities and output formats.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | The description mentions 'medication adherence message gen' and 'academic writing workflows' but does not list any concrete actions. Terms like 'structured execution', 'explicit assumptions', and 'clear output boundaries' are abstract and vague. | 1 / 3 |
Completeness | The 'what' is unclear—it's impossible to determine what this skill actually does. The 'when' clause ('for academic writing workflows that need structured execution') is vague and doesn't provide explicit triggers. Neither question is adequately answered. | 1 / 3 |
Trigger Term Quality | The description combines two unrelated domains ('medication adherence message gen' and 'academic writing workflows') in a confusing way. Users would not naturally use these terms together, and the keywords are contradictory and unlikely to match real user queries. | 1 / 3 |
Distinctiveness Conflict Risk | The description is internally contradictory, mixing medication adherence and academic writing, making it unclear what niche it occupies. It could conflict with both healthcare-related skills and academic writing skills while not clearly belonging to either. | 1 / 3 |
Total | 4 / 12 Passed |
Implementation
27%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This skill suffers from severe verbosity and poor organization, burying genuinely useful content (CLI usage, Python API, psychological principles table, output format examples) under layers of generic boilerplate. The circular cross-references between sections suggest auto-generated content that wasn't reviewed. While the core medication message generation documentation is actionable, the surrounding framework of risk assessments, security checklists, lifecycle status, and response templates adds significant token cost with minimal value.
Suggestions
Remove all boilerplate sections (Risk Assessment, Security Checklist, Lifecycle Status, Evaluation Criteria, Response Template, Output Requirements) that don't provide skill-specific actionable guidance—these waste tokens on generic content Claude already knows.
Consolidate the three overlapping workflow descriptions (Example Usage run plan, Implementation Details, Workflow section) into a single clear numbered workflow with specific validation steps.
Eliminate circular cross-references ('See ## Usage above', 'See ## Prerequisites above') and reorder sections logically so dependencies appear before the sections that need them.
Move the detailed CLI options table and psychological principles table to a separate reference file, keeping only a quick-start example and API snippet in the main skill body.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Extremely verbose and repetitive. Multiple sections reference each other circularly ('See ## Prerequisites above', 'See ## Usage above', 'See ## Workflow above'). Contains extensive boilerplate (security checklists, lifecycle status, evaluation criteria, risk assessment) that adds little actionable value. The description of the skill's purpose is repeated in multiple places. Many sections like 'Key Features' restate obvious things Claude already knows. | 1 / 3 |
Actionability | The CLI examples and Python API usage are concrete and executable, with clear parameter tables and output format examples. However, much of the surrounding content is vague procedural boilerplate ('Confirm the user objective', 'Validate that the request matches the documented scope') rather than specific executable guidance. The actual medication message generation logic is well-documented but buried in noise. | 2 / 3 |
Workflow Clarity | There is a numbered workflow (steps 1-5) and an 'Example run plan' (steps 1-4), but both are generic and lack specific validation checkpoints. The workflow steps are abstract ('Validate that the request matches the documented scope') rather than concrete. There's no explicit validation feedback loop for the actual message generation process—just a generic 'if execution fails, switch to fallback' instruction. | 2 / 3 |
Progressive Disclosure | The document is a monolithic wall of text with 20+ sections, many of which are boilerplate or redundant. Content is poorly organized with circular cross-references ('See ## Prerequisites above' when Prerequisites appears below). The single external reference (references/audit-reference.md) is mentioned but the skill dumps everything inline rather than appropriately splitting content across files. | 1 / 3 |
Total | 6 / 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
8277276
- Reviewed
Table of Contents
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