microbiome-diversity-reporter

Interpret Alpha and Beta diversity metrics from 16S rRNA sequencing results.

Quality

61%

Does it follow best practices?

Impact

—

No eval scenarios have been run

Securityby

Passed

No known issues

Fix and improve this skill with Tessl

tessl review fix ./scientific-skills/Academic Writing/microbiome-diversity-reporter/SKILL.md

Quality

Content

65%

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

The body is highly actionable with concrete CLI examples and a thorough parameter table, but it is padded with generic process boilerplate and keeps too much reference-grade content inline instead of splitting it across the bundle.

Suggestions

Move the parameter table, input-format examples, and security/risk checklist into separate reference files and link to them from SKILL.md to reduce inline bulk.

Tighten or remove generic scaffolding (Key Features, Implementation Details, Response Template, 'See ## Usage above' cross-references) that restates the description or adds no domain-specific value.

Integrate the py_compile validation step explicitly into the Workflow sequence as a checkpoint before running main.py, so validation is part of the ordered steps rather than a separate section.

Dimension	Reasoning	Score
Conciseness	The domain-specific sections (CLI usage, parameter table, input formats, example output) are lean, but the body carries generic process boilerplate and redundancy such as "When to Use" restating the description and "See `## Usage` above" cross-references.	2 / 3
Actionability	It provides copy-paste-ready executable commands with realistic flags and values (e.g., '--input otu_table.tsv --metric shannon'), a complete parameter table, TSV input examples, and concrete example JSON output.	3 / 3
Workflow Clarity	A 5-step Workflow, a py_compile Quick Check, and a fallback path exist, but the workflow steps read as generic process language with validation checkpoints implicit and separated rather than tightly integrated into the sequence.	2 / 3
Progressive Disclosure	Real one-level-deep bundles are referenced and verified (scripts/main.py, references/audit-reference.md), but the body is long and monolithic with parameter docs, input formats, the security checklist, and example output all kept inline rather than split into reference files.	2 / 3
	Total	9 / 12 Passed

Description

57%

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

The description is domain-specific and clearly distinguishable, but it answers only the "what" and omits explicit when-to-use trigger guidance, capping its strongest-weighted dimension at 2.

Suggestions

Add a 'Use when...' clause naming natural triggers (e.g., analyzing 16S rRNA surveys, comparing sample composition, computing Shannon/Simpson/Chao1 or UniFrac/Bray-Curtis diversity).

Expand the action list beyond the single verb 'Interpret' to concrete operations like computing alpha indices, generating PCoA/NMDS plots, and running PERMANOVA tests.

Include common term variations users would actually say (e.g., 'microbiome', 'OTU/ASV tables', 'beta-diversity ordination').

Dimension	Reasoning	Score
Specificity	The description names a concrete domain ("Alpha and Beta diversity metrics from 16S rRNA sequencing") and the action "Interpret", but relies on a single verb over two metric types rather than listing multiple distinct concrete actions.	2 / 3
Completeness	It clearly answers "what" the skill does but provides no "Use when..." clause or equivalent explicit when-to-use guidance, so completeness is capped at 2 per the rubric guidelines.	2 / 3
Trigger Term Quality	"Alpha and Beta diversity" and "16S rRNA sequencing" are relevant niche keywords a researcher might say, but coverage is thin with no common variations or natural trigger phrasing.	2 / 3
Distinctiveness Conflict Risk	16S rRNA microbiome diversity interpretation is a narrow, specialized niche with distinct triggers that is very unlikely to overlap with unrelated skills.	3 / 3
	Total	9 / 12 Passed

Validation

93%

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 — 15 / 16 Passed

Validation for skill structure

Criteria	Description	Result
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	15 / 16 Passed

Repository: aipoch/medical-research-skills
Commit: 7cc5680

Reviewed: 3 days 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.