meta-baseline-generator
Generates a meta-analysis baseline characteristics section (text + table) from raw data. Supports Chinese and English. Use when the user provides baseline data and wants a formatted results section.
53
60%
Does it follow best practices?
Impact
—
No eval scenarios have been run
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./scientific-skills/Academic Writing/meta-baseline-generator/SKILL.mdQuality
Discovery
85%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 a well-crafted description that clearly defines a narrow, specialized task with explicit trigger guidance. Its main weakness is that trigger terms could be expanded to include more natural variations that researchers might use (e.g., 'systematic review', 'patient demographics', 'Table 1'). Overall, it effectively communicates both what the skill does and when to use it.
Suggestions
Add more natural trigger terms users might say, such as 'systematic review', 'patient demographics', 'study characteristics', 'participant characteristics', or 'Table 1'.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: generates a meta-analysis baseline characteristics section, produces both text and table, supports Chinese and English. These are concrete, well-defined outputs. | 3 / 3 |
Completeness | Clearly answers both what ('Generates a meta-analysis baseline characteristics section (text + table) from raw data. Supports Chinese and English.') and when ('Use when the user provides baseline data and wants a formatted results section.'). | 3 / 3 |
Trigger Term Quality | Includes relevant terms like 'meta-analysis', 'baseline characteristics', 'baseline data', and 'results section', but misses common variations users might say such as 'patient demographics', 'study characteristics', 'systematic review', 'participant characteristics', or 'Table 1'. | 2 / 3 |
Distinctiveness Conflict Risk | Very specific niche: meta-analysis baseline characteristics sections from raw data. This is unlikely to conflict with other skills due to the highly specialized domain of systematic review/meta-analysis writing. | 3 / 3 |
Total | 11 / 12 Passed |
Implementation
35%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 significant verbosity and repetition—the description is restated at least 3 times across different sections, and multiple sections ('Key Features', 'Implementation Details') add little actionable value. The core Workflow section is the strongest part, providing a clear multi-step process with a concrete code example, but it lacks validation checkpoints. The skill would benefit greatly from removing redundant sections and tightening the content to focus on the actual workflow and rules.
Suggestions
Remove or drastically condense the 'When to Use', 'Key Features', and 'Implementation Details' sections—they repeat the same information and add no actionable guidance Claude doesn't already know.
Consolidate the two competing workflows (the 'Example run plan' in Example Usage and the main Workflow) into a single, authoritative sequence.
Add explicit validation checkpoints to the workflow, e.g., verify the LLM-generated text contains proper punctuation before passing to the script, and validate the table output has correct markdown structure before presenting to the user.
Remove generic boilerplate phrases like 'validate the request, choose the packaged workflow, and produce a bounded deliverable' that provide no skill-specific guidance.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Extremely verbose and repetitive. The 'When to Use' section restates the description 5 different ways. 'Key Features' repeats the description verbatim. 'Implementation Details' says 'See Workflow above' then restates generic platitudes. Multiple sections (Example Usage, Implementation Details) add no real information and pad the token budget significantly. | 1 / 3 |
Actionability | The Workflow section provides a reasonably concrete sequence with a Python code snippet showing how to call process_content(). However, much of the skill is vague boilerplate ('validate the request, choose the packaged workflow, produce a bounded deliverable'). The actual executable guidance is buried among generic filler, and the script's actual behavior/API is only partially documented. | 2 / 3 |
Workflow Clarity | The Workflow section has a clear 5-step sequence with inputs/outputs for each step, which is good. However, there are no explicit validation checkpoints or error recovery steps. The 'Example run plan' in Example Usage is a separate, vaguer workflow that conflicts with the main Workflow section, creating confusion about which to follow. | 2 / 3 |
Progressive Disclosure | References to `references/prompts.md` and `scripts/text_processor.py` are appropriately signaled in the Workflow section. However, no bundle files were provided to verify these exist, and the overall document has too much inline content that is repetitive rather than well-organized. The 'Implementation Details' section pointing to 'Workflow above' is circular and poorly structured. | 2 / 3 |
Total | 7 / 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
d3c2c0f
- Reviewed
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