CtrlK
BlogDocsLog inGet started
Tessl Logo

metagenomic-krona-chart

Analyze data with `metagenomic-krona-chart` using a reproducible workflow, explicit validation, and structured outputs for review-ready interpretation.

40

Quality

39%

Does it follow best practices?

Run evals on this skill

Adds up to 20 points to the overall score

View guide

SecuritybySnyk

Passed

No findings from the security scan

Fix and improve this skill with Tessl

tessl review fix ./scientific-skills/Data Analysis/metagenomic-krona-chart/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

50%Weight 40%Scale 1-5

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

The body contains genuinely useful, executable guidance (CLI params, input formats, a working script) but is weighed down by heavy redundancy, contradictory dependency statements, and large swaths of generic process boilerplate. Consolidating and trimming would materially improve it.

Suggestions

Merge the duplicate dependency/prerequisite sections into one accurate block (resolve the Python 3.8 vs 3.10+ and pandas/plotly version contradictions) and remove the contradictory 'No additional Python packages required'.

Delete generic boilerplate sections that add no skill-specific knowledge (Risk Assessment, Security Checklist, Evaluation Criteria, Lifecycle Status, Response Template) or move them to a reference file.

Replace the placeholder 'Audit validation sample with explicit symptoms, history...' example with a real metagenomic invocation, and add a verify-output checkpoint to the main workflow.

DimensionReasoningScore

Conciseness

The ~272-line body carries substantial redundancy and generic boilerplate — duplicate/contradictory dependency sections (3.10+ vs 3.8+, 'Prerequisites: No additional Python packages required'), cross-references like 'See ## Usage above', and generic process sections (Risk Assessment, Security Checklist, Evaluation Criteria, Lifecycle Status, Response Template) that add no skill-specific knowledge.

2 / 5

Actionability

It provides executable commands (py_compile, --help, -i input.tsv -o krona_chart.html), a concrete parameter table, and input-format examples backed by a real argparse CLI in scripts/main.py; the main gap is a copy-pasted placeholder example invocation mentioning 'symptoms, history, assessment'.

4 / 5

Workflow Clarity

A 5-step workflow and a run plan exist, but they describe generic process hygiene rather than the actual technical sequence (parse report -> build taxonomy tree -> render sunburst -> write HTML), and validation checkpoints are only implicit (a separate py_compile smoke check, no verify-output step in the main flow).

3 / 5

Progressive Disclosure

Structure exists with a real scripts/main.py and references/ directory, but almost all content is inlined into one large SKILL.md, the single reference file runtime_checklist.md is not linked by name, and requirements.txt is referenced but absent from the bundle.

3 / 5

Total

12

/

20

Passed

Description

28%Weight 40%Scale 1-5

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 names the tool but relies on generic process language instead of concrete capabilities, and it lacks any explicit 'when to use' trigger guidance. It is recognizable but underspecified for routing.

Suggestions

State the concrete capability, e.g. 'Generate interactive Krona/sunburst charts of taxonomic abundance from Kraken2, Bracken, or Centrifuge output.'

Add an explicit trigger clause such as 'Use when the user asks for Krona charts, sunburst plots, or metagenomic taxonomic abundance visualization.'

Replace generic phrasing like 'Analyze data' with the specific actions (parse classification reports, build taxonomy hierarchy, render interactive HTML).

DimensionReasoningScore

Specificity

The only action verb is the generic 'Analyze data'; the rest ('reproducible workflow', 'explicit validation', 'structured outputs', 'review-ready interpretation') describes a process posture rather than concrete actions like generating Krona/sunburst charts or parsing Kraken2/Bracken output.

2 / 5

Completeness

The 'what' is vague ('Analyze data with X') and there is no 'Use when...' clause or equivalent trigger guidance, matching the anchor for a vague what with no when.

2 / 5

Trigger Term Quality

It supplies the tool name 'metagenomic-krona-chart' plus the generic phrase 'Analyze data', but omits the natural terms a user would actually say such as 'Krona chart', 'sunburst plot', 'metagenomic samples', or 'Kraken2'.

2 / 5

Distinctiveness Conflict Risk

The specific tool name 'metagenomic-krona-chart' gives it a niche, but the surrounding boilerplate about reproducible workflows and structured outputs is generic enough to overlap with other data-analysis skills.

3 / 5

Total

9

/

20

Passed

Validation

93%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

15

/

16

Passed

Repository
aipoch/medical-research-skills
Reviewed

Table of Contents

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.