denario

Multiagent AI system for scientific research assistance that automates research workflows from data analysis to publication. This skill should be used when generating research ideas from datasets, developing research methodologies, executing computational experiments, performing literature searches, or generating publication-ready papers in LaTeX format. Supports end-to-end research pipelines with customizable agent orchestration.

Quality

76%

Does it follow best practices?

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tessl review fix ./bundled/skills/denario/SKILL.md

Quality

Content

61%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.

A well-organized, actionable skill body with clear staged guidance and real reference files, weakened by redundant pipeline examples and the absence of validation/verification checkpoints for the computational and LaTeX-generation steps.

Suggestions

Add explicit validation checkpoints to the pipeline (e.g., verify experiment results are sensible before paper generation, confirm the generated LaTeX compiles before declaring success) to lift workflow_clarity above the batch-operation cap of 3.

Remove the redundant 'End-to-End Research Pipeline' example or fold it into examples.md, since it duplicates the staged walkthrough and inflates the body.

Tighten obvious restatements like 'This produces a research question or hypothesis based on the described data' to improve token efficiency.

Dimension	Reasoning	Score
Conciseness	Mostly efficient and free of basic-concept padding, but the 'Common Workflows' section repeats the same pipeline already shown in the staged walkthrough, and prose like 'This produces a research question or hypothesis based on the described data' states the obvious. Not below a 3 since no fundamental concepts are over-explained.	3 / 5
Actionability	Provides concrete, copy-paste-ready code and commands (uv add, den.set_data_description, den.get_paper(journal=Journal.APS), denario run) covering the common cases; held back from 5 by placeholder-filled snippets ('[specify domain]', '[research question]') and minor gaps.	4 / 5
Workflow Clarity	The numbered four-stage pipeline is clearly sequenced, but there are no validation or verification checkpoints for batch/destructive operations (computational experiments, LaTeX compilation that can fail), which caps this dimension at 3 per the rubric.	3 / 5
Progressive Disclosure	Good structure with a dedicated 'Detailed References' section and inline pointers to real bundle files (installation.md, llm_configuration.md, research_pipeline.md, examples.md) that are one level deep; not a 5 because some inline code and the redundant end-to-end example could live in examples.md.	4 / 5
	Total	14 / 20 Passed

Description

92%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.

A strong, well-structured description that concretely enumerates capabilities and gives explicit 'use when' trigger guidance in the correct third-person voice. Only minor gap is the absence of synonyms/file extensions for maximum trigger coverage.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions — 'generating research ideas from datasets', 'developing research methodologies', 'executing computational experiments', 'performing literature searches', 'generating publication-ready papers in LaTeX' — giving comprehensive coverage of capabilities.	5 / 5
Completeness	Explicitly answers both what ('automates research workflows from data analysis to publication') and when ('This skill should be used when generating research ideas...') with concrete trigger phrases, in third person.	5 / 5
Trigger Term Quality	Good coverage of natural terms users would say ('research ideas', 'datasets', 'literature searches', 'publication-ready papers', 'LaTeX'), though it lacks synonyms and file-extension variants that would push it to a 5.	4 / 5
Distinctiveness Conflict Risk	Occupies a clear niche — multiagent scientific-research automation ending in LaTeX publication — with distinct triggers and minimal overlap risk with generic skills.	5 / 5
	Total	19 / 20 Passed

Validation

100%

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 — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository: foryourhealth111-pixel/Vibe-Skills
Path: bundled/skills/denario/SKILL.md
Commit: d5ae560

Reviewed: 1 day ago

Table of Contents

Discovery Implementation Validation

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