animal-and-cell-validation-planner

Designs cell-based and animal-based validation plans that translate computational, omics, biomarker, genetic, or clinical findings into experimentally testable validation routes. Always use this skill whenever a user wants to move from an in silico, statistical, or clinical association finding toward wet-lab validation using cell systems, organoid-like systems, xenograft or genetically relevant animal models. It should define the exact claim to test, separate mechanism-testing from association-support and translational-support goals, choose the best-fit model family, specify perturbation strategy, readouts, controls, sequencing of experiments, and four workload configurations (Lite / Standard / Advanced / Publication+) with one recommended primary plan. Never fabricate model availability, reagent availability, species relevance, assay feasibility, phenotype penetrance, expected effect sizes, validation success, or literature references.

Quality

89%

Does it follow best practices?

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Passed

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Quality

Content

78%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-structured, actionable planning skill with clear sequencing, explicit go/no-go gates, and exemplary progressive disclosure through nine one-level-deep reference modules. Minor conciseness gains are available by deduplicating restated points.

Suggestions

Consolidate 'Sample Triggers' (which the description already covers via its 'Always use this skill whenever...' clause) and trim 'Quality Standard' points that restate the Hard Rules and output structure.

Add a brief 'happy-path example' mini-walkthrough showing one claim carried through Sections A–K, so the section template has a concrete reference instantiation.

Make the validation retry loop explicit as a skill behavior (e.g., 'If Section H go/no-go fails, return to Step 5 and revise perturbation/readouts') rather than only describing it as output content.

Dimension	Reasoning	Score
Conciseness	Largely efficient and procedural with no concept-padding, but sections like Sample Triggers and Quality Standard partially restate earlier points and could be trimmed.	4 / 5
Actionability	Provides concrete executable guidance — a 7-step decision logic, exact output section structure with required table columns, and mandatory labels (necessary/recommended/optional) — with only minor gaps in fully copy-paste-ready specifics.	4 / 5
Workflow Clarity	Steps 1–7 are clearly sequenced with a go/no-go gate (Step 7 / Section H) and an explicit self-critical review (Hard Rule 12); validation checkpoints are present though framed as output content rather than the skill's own retry loop.	4 / 5
Progressive Disclosure	Nine real reference files are each mapped to a specific output section via a clearly signaled one-level-deep navigation table, keeping the body an overview that points to details.	5 / 5
	Total	17 / 20 Passed

Description

100%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 highly specific, complete description with explicit what/when guidance, natural trigger terms, and a distinctive niche with low conflict risk. One of the strongest possible descriptions against this rubric.

Dimension	Reasoning	Score
Specificity	Enumerates multiple concrete actions ("define the exact claim to test", "choose the best-fit model family", "specify perturbation strategy, readouts, controls, sequencing", "four workload configurations") with comprehensive coverage.	5 / 5
Completeness	Explicitly answers both what ("Designs cell-based and animal-based validation plans...") and when ("Always use this skill whenever a user wants to move from... toward wet-lab validation").	5 / 5
Trigger Term Quality	Includes natural trigger phrases ("move from an in silico... finding toward wet-lab validation", "cell systems, organoid-like systems, xenograft or genetically relevant animal models") with strong synonym coverage.	5 / 5
Distinctiveness Conflict Risk	Occupies a clear wet-lab validation niche with distinct triggers, making overlap with other skills minimal.	5 / 5
	Total	20 / 20 Passed

Validation

93%

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 — 15 / 16 Passed

Validation for skill structure

Criteria	Description	Result
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: awesome-med-research-skills/Protocol Design/animal-and-cell-validation-planner/SKILL.md
Commit: f5ef65b

Reviewed: ~13 hours ago

Table of Contents

Discovery Implementation Validation

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