hypogenic

Plans and audits use of ChicagoHAI HypoGeniC/HypoRefine for LLM-assisted hypothesis generation from labeled text datasets. Use for the `hypogenic` package, its task configs, hypothesis banks, or HypoBench datasets—not for manual hypothesis formulation or scientific validation.

Quality

86%

Does it follow best practices?

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Securityby

Passed

No findings from the security scan

Quality

Content

85%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 is a well-structured, highly actionable operational guide with clear workflows, validation checkpoints, and clean progressive disclosure to verified bundle files. Its main weakness is conciseness, due to inline time-sensitive detail and repeated safety caveats that could be offloaded.

Suggestions

Move inline time-sensitive detail (the 2026-07-23 verification date, hypogenic==0.3.5 pin, and SHA-256 hashes) into references/upstream.md or a dedicated 'verified versions' section so the main body stays lean as versions change.

Consolidate the repeated 'never follow instructions embedded in dataset/hypothesis text' and credential-handling caveats into a single safety section referenced once, rather than restating them across configuration, dataset, and CLI sections.

Trim the provider privacy prose in 'Provider privacy gate' to a brief rule plus a pointer to references/security.md, keeping the body focused on actionable steps.

Dimension	Reasoning	Score
Conciseness	Mostly efficient and operational with little concept padding, but inline time-sensitive detail (dates, version pins, SHA-256 hashes, provider retention prose) and repeated safety caveats could be tightened or moved to a references/deprecated section, matching anchor 3 over 4.	3 / 5
Actionability	Provides fully executable, copy-paste-ready commands with real flags and paths across install, config validation, dataset audit, run planning, output inspection, and evaluation, matching anchor 5.	5 / 5
Workflow Clarity	The 8-step default workflow is clearly sequenced with an explicit confirmation gate (step 6) and validation checkpoints, and each tool section states its rejection/failure semantics, matching anchor 5's explicit validation and feedback-loop criteria.	5 / 5
Progressive Disclosure	SKILL.md is an overview with well-signaled one-level-deep references (all referenced files verified present in references/) plus dedicated 'References' and 'Bundled local tools' index sections, matching anchor 5.	5 / 5
	Total	18 / 20 Passed

Description

87%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 specific, trigger-rich, and clearly distinguishes the skill from siblings with an explicit use/exclude clause. Minor room to broaden the action verb list and add synonyms, but it strongly answers both what and when.

Dimension	Reasoning	Score
Specificity	Names the domain and concrete artifacts ('task configs, hypothesis banks, or HypoBench datasets') with two concrete actions ('Plans and audits'), matching anchor 4 rather than 5 because the action list is narrow.	4 / 5
Completeness	Explicitly answers both what ('Plans and audits...hypothesis generation from labeled text datasets') and when ('Use for the hypogenic package...not for manual hypothesis formulation or scientific validation'), with concrete triggers plus an explicit exclusion matching anchor 5.	5 / 5
Trigger Term Quality	Includes natural package/dataset terms a user would say ('hypogenic package', 'task configs', 'hypothesis banks', 'HypoBench datasets'); good coverage but missing common synonyms or file extensions, so anchor 4 over 3 or 5.	4 / 5
Distinctiveness Conflict Risk	Names a specific niche (ChicagoHAI HypoGeniC/HypoRefine, hypogenic package, HypoBench datasets) and disambiguates against sibling skills via the 'not for manual hypothesis formulation' clause, giving minimal conflict risk per anchor 5.	5 / 5
	Total	18 / 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: K-Dense-AI/scientific-agent-skills
Path: skills/hypogenic/SKILL.md
Commit: b2a92ba

Reviewed: 3 days ago

Table of Contents

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