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phylogenetic-tree-styler

Analyze data with `phylogenetic-tree-styler` using a reproducible workflow, explicit validation, and structured outputs for review-ready interpretation.

Quality

49%

Does it follow best practices?

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SecuritybySnyk

Passed

No findings from the security scan

Fix and improve this skill with Tessl

tessl review fix ./scientific-skills/Data Analysis/phylogenetic-tree-styler/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 a genuinely solid core — executable usage commands, a complete parameter table, and clear input format documentation — but it is buried in generic auto-generated governance boilerplate that nearly doubles its length, includes self-contradictory dependency information, and inlines content that belongs in the existing references/ file which is never actually named. The description/body mismatch (a phylogenetics tool described as generic 'data analysis') compounds the noise.

Suggestions

Cut the template boilerplate (Key Features, Implementation Details, Output Requirements, Response Template, Inputs to Collect, Output Contract, Risk Assessment, Security Checklist, Lifecycle Status, Evaluation Criteria) and keep only tree-specific guidance, resolving the Prerequisites/Dependencies contradiction.

Name and link the actual reference file: 'Before running, follow [runtime_checklist.md](references/runtime_checklist.md)' instead of the generic 'references/ contains supporting rules' gesture.

Fix the 'Audit-Ready Commands' section: the --input example passes a prose sentence and --format json, which contradicts the documented Newick-file input and png/pdf/svg output; replace with a real invocation like 'python scripts/main.py -i tree.nwk -o tree_audit.png'.

DimensionReasoningScore

Conciseness

Roughly half the body is template padding unrelated to tree styling: circular filler sections ('See `## Features` above', 'See `## Usage` above', 'See `## Workflow` above'), generic governance boilerplate (Response Template, Output Contract, Inputs to Collect, Lifecycle Status, Risk Assessment), and a direct contradiction ('## Prerequisites: No additional Python packages required' vs. '## Dependencies: ete3, matplotlib, numpy, pandas'). It is noticeably verbose with several padded sections (anchor 2) rather than only minor trimmable excess (anchor 3).

2 / 5

Actionability

The Usage, Parameters, Examples, taxonomy CSV format, and Newick input sections are concrete and copy-paste ready ('python3 scripts/main.py -i tree.nwk -o tree_bootstrap.png --show-bootstrap --bootstrap-threshold 70'). Not score 5 because the 'Audit-Ready Commands' example passes a prose string as --input with --format json, contradicting the documented Newick-input/png-pdf-svg-output interface, and the Quick Check assumes an undocumented working directory ('cd "20260318/scientific-skills/..."').

4 / 5

Workflow Clarity

The 5-step 'Workflow' is a generic template (validate the request, choose the packaged workflow, produce a bounded deliverable) rather than tree-specific operations, and the 'Example run plan' is likewise abstract. The py_compile Quick Check supplies one real checkpoint, but there is no fix-and-retry feedback loop and no validation of the actual output image, matching anchor 3's 'sequence present but checkpoints missing or implicit'.

3 / 5

Progressive Disclosure

The bundle has scripts/main.py and references/runtime_checklist.md, but the body only gestures generically ('Reference material available in `references/`', '`references/` contains supporting rules, prompts, or checklists') — it never names runtime_checklist.md or signals what is inside it, and substantial inline boilerplate (security checklist, lifecycle status) belongs in a separate reference. This matches anchor 3: references present but not clearly signaled and content that should be separate is inline.

3 / 5

Total

12

/

20

Passed

Description

25%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 is template boilerplate: it describes a generic data-analysis discipline rather than the skill's actual purpose (beautifying phylogenetic trees with taxonomy color blocks, bootstrap values, and timelines). It lacks any 'use when' trigger guidance and no natural user-facing keywords, so it will neither attract the right requests nor distinguish itself from other analysis skills.

Suggestions

State the actual capability in the description, e.g. 'Beautify phylogenetic trees with taxonomy color blocks, bootstrap values, and timelines from Newick files' — the body already contains this text in '## Features'.

Add an explicit trigger clause: 'Use when working with phylogenetic trees, Newick (.nwk/.newick) files, or when the user asks to style/annotate a tree with bootstrap values or taxonomy colors.'

Remove generic filler phrases ('reproducible workflow', 'review-ready interpretation', 'structured outputs') that add no information and conflict with every other data-analysis skill.

DimensionReasoningScore

Specificity

The description names the domain ('Analyze data with `phylogenetic-tree-styler`') but every action is generic process language ('reproducible workflow, explicit validation, and structured outputs') with no concrete capability such as beautifying trees, adding bootstrap values, or taxonomy color blocks. It sits between the vague anchor 1 and the 1-2-concrete-actions anchor 3, closer to anchor 2's 'actions are minimal or generic'.

2 / 5

Completeness

The 'what' is vague ('Analyze data... for review-ready interpretation') and there is no 'when' / 'Use when...' clause at all, which per the judging guidelines caps completeness at 3; with a vague what and a fully missing when it matches anchor 2. It is not score 1 because a discernible what is present.

2 / 5

Trigger Term Quality

The only keyword is the generic phrase 'Analyze data' plus the backticked skill name; natural terms a user would actually say (phylogenetic tree, Newick, .nwk, bootstrap values, taxonomy colors) are entirely absent. Not score 1 because 'analyze data' and 'phylogenetic' do function as weak keywords, but it misses the natural phrases of anchor 4.

2 / 5

Distinctiveness Conflict Risk

'Analyze data... reproducible workflow... structured outputs' describes virtually every data-analysis skill and would trigger alongside many of them; only the backticked skill name provides a weak differentiator. It is above anchor 1 ('Helps with code and documents'-level genericity) but squarely in anchor 2's 'very broad; high overlap risk'.

2 / 5

Total

8

/

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.

Validation — 15 / 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

Path

scientific-skills/Data Analysis/phylogenetic-tree-styler/SKILL.md

Reviewed

Table of Contents

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