CtrlK
BlogDocsLog inGet started
Tessl Logo
SkillPublic

forest-plot-styler

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

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/forest-plot-styler/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

38%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 core usage documentation (parameters, data format, examples) is solid, but it is buried in generic process boilerplate, duplicated sections, dead references to nonexistent bundle files, and a wrong-domain audit command. The skill reads as template-padded rather than purpose-built for its forest-plot task.

Suggestions

Cut the boilerplate sections (Risk Assessment, Security Checklist, Lifecycle Status, Evaluation Criteria, Response Template, Output Contract, Validation and Safety Rules) — they are generic ceremony that consumes context without teaching the forest-plot task — and merge the duplicated Key Features/Features and Example Usage/Usage sections.

Remove or fix dead references: `references/` and `scripts/main.py` do not exist in this bundle, so either ship them or delete every section that points to them; also remove the nonsensical audit command that passes a clinical-note string as --input.

Consolidate the three overlapping workflow/run-plan sections into one validated sequence (compile check → run with confirmed inputs → review generated plot → fallback on failure) so the actual execution path is unambiguous.

DimensionReasoningScore

Conciseness

The body is noticeably padded: boilerplate sections ("Risk Assessment", "Security Checklist", "Lifecycle Status" with a "Next Review Date", "Evaluation Criteria", "Response Template") plus duplicated content ("Key Features" vs "Features", "Example Usage" vs "Usage") and broken pointers ("See `## Features` above" points to a section that appears later). The core usage docs are buried in process ceremony.

2 / 5

Actionability

The parameter table, input-data-format table, sample CSV, and example invocations are genuinely concrete, but the documented entry point `scripts/main.py` does not exist in the bundle and one "Audit-Ready Command" is nonsensical (passes a clinical-note string, "Audit validation sample with explicit symptoms, history...", as --input where a CSV path is required), leaving the guidance incomplete rather than copy-paste ready.

3 / 5

Workflow Clarity

A sequence with some checkpoints exists (Quick Check `py_compile`, scope validation, error-handling fallbacks), but it is scattered across three overlapping sections ("Example run plan", "Workflow", "Error Handling") with no verify-then-fix loop on the generated output itself, matching the 'steps listed but checkpoints implicit/scattered' anchor.

3 / 5

Progressive Disclosure

The body claims "Reference material available in `references/`" and "Reference guidance: `references/` contains supporting rules", but no references/ directory or any bundle file exists — dead references. Meanwhile content that belongs in separate files (preset styles, security checklist, risk assessment) is inlined into a ~350-line monolith.

2 / 5

Total

10

/

20

Passed

Description

13%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 generic boilerplate that omits the skill's actual purpose (beautifying meta-analysis forest plots with customizable odds ratio points and CI line styles). It has no trigger terms and no use-when guidance, so it would rarely be selected and would conflict with any general analytics skill. The skill's real capabilities, documented in the body, are entirely invisible here.

Suggestions

Rewrite the description to state the actual capability in third person, e.g. "Beautifies meta-analysis and subgroup-analysis forest plots with customizable odds ratio point sizes, colors, and confidence-interval line styles."

Add a trigger clause naming the natural terms users would say, e.g. "Use when creating or styling forest plots for meta-analyses, odds-ratio visualizations, or subgroup analyses from CSV/Excel data."

Remove buzzword padding ("reproducible workflow", "explicit validation", "structured outputs for review-ready interpretation") — these add tokens without distinguishing the skill.

DimensionReasoningScore

Specificity

The only named action is "Analyze data", with buzzword phrases like "reproducible workflow, explicit validation, and structured outputs for review-ready interpretation" naming no concrete capability. No domain-specific action (forest plots, odds ratios, CI styling) is stated, matching the 'names an action but actions are minimal or generic' anchor.

2 / 5

Completeness

The 'what' is vague ("Analyze data with `forest-plot-styler`" explains nothing about what the skill produces) and there is no 'when' clause at all — no 'Use when...' trigger guidance, matching the 'vague what and no when' anchor.

2 / 5

Trigger Term Quality

Natural trigger terms a user would say — "forest plot", "meta-analysis", "odds ratio", "subgroup analysis" — are entirely absent; the description contains only generic language ("Analyze data", "structured outputs"). It reads like "Handles document object model manipulation"-level jargon, not user vocabulary.

1 / 5

Distinctiveness Conflict Risk

"Analyze data ... structured outputs" is entirely generic and would trigger for virtually any data-analysis skill; nothing distinguishes it from competitors, matching anchor 1.

1 / 5

Total

6

/

20

Passed

Validation

87%

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

Validation — 14 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

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

Warning

referenced_paths_exist

Referenced path issues: 16 missing

Warning

Total

14

/

16

Passed

Repository

aipoch/medical-research-skills

Path

scientific-skills/Data Analysis/forest-plot-styler/SKILL.md

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.