adaptyv

Cloud laboratory platform for automated protein testing and validation; use when you have designed protein sequences and need wet-lab experimental validation (e.g., binding, expression, thermostability, enzyme activity) and API-based submission/status/result retrieval.

Quality

74%

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Securityby

Passed

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tessl review fix ./scientific-skills/Other/adaptyv/SKILL.md

Quality

Content

56%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 Adaptyv-specific portion is concrete and actionable, but the skill is weighed down by a duplicated generic-template header block and references to missing bundle files. Trimming the boilerplate and ensuring referenced files exist would materially improve it.

Suggestions

Remove the generic templated header block (the first When to Use / Key Features / Dependencies / Example Usage / Implementation Details / Validation Shortcut sections) that merely restates the description and duplicates the real Adaptyv sections below.

Create the referenced reference/*.md files (experiments, protein_optimization, api_reference, examples) or remove the dangling citations so navigation resolves against the actual bundle.

Collapse the redundant generic sections (Recommended Workflow, Output Contract, Deterministic Output Rules, Completion Checklist) into the specific workflow so the single execution path is unambiguous.

Dimension	Reasoning	Score
Conciseness	The body opens with a generic templated section ('Use this skill when the request matches its documented task boundary', 'Execution model: validate the request, choose the packaged workflow') and duplicates the When to Use / Key Features / Dependencies / Example Usage / Implementation Details headers, adding several padded sections Claude does not need.	2 / 5
Actionability	Provides a mostly executable Python workflow (auth header, POST /experiments, poll loop, GET results) plus concrete pre-submission checks (unpaired cysteines, hydrophobicity, solubility), with minor gaps such as endpoint paths flagged as 'confirm in reference/api_reference.md'.	4 / 5
Workflow Clarity	The 4.1/4.2/4.3 sequence (set credentials → install deps → submit → poll → download) is clear with validation checkpoints (missing-key check, raise_for_status, terminal-status check), though the parallel generic 'Recommended Workflow' list is vaguer and somewhat duplicates it.	4 / 5
Progressive Disclosure	References are clearly signaled and one level deep, but the four cited files (reference/experiments.md, protein_optimization.md, api_reference.md, examples.md) do not exist in the bundle (only scripts/validate_skill.py is present), and the generic templated content that belongs elsewhere is inlined.	3 / 5
	Total	13 / 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-targeted description that concretely states both capability and trigger conditions in third person. Minor room to add a few more natural synonyms, but it cleanly answers what and when.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions — 'protein testing and validation', 'API-based submission', 'status' retrieval, 'result retrieval' — alongside the concrete assay categories (binding, expression, thermostability, enzyme activity), giving comprehensive coverage.	5 / 5
Completeness	Explicitly answers both 'what' ('Cloud laboratory platform for automated protein testing and validation') and 'when' via a concrete 'use when you have designed protein sequences and need wet-lab experimental validation' trigger clause.	5 / 5
Trigger Term Quality	Strong natural terms a protein-design user would say ('protein sequences', 'wet-lab experimental validation', 'binding, expression, thermostability, enzyme activity'), though a few common synonyms or file-format cues are absent.	4 / 5
Distinctiveness Conflict Risk	Occupies a clear niche (cloud wet-lab protein validation with API submission) with distinct triggers, making overlap with unrelated skills minimal.	5 / 5
	Total	19 / 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: scientific-skills/Other/adaptyv/SKILL.md
Commit: 63c61d3

Reviewed: 3 hours ago

Table of Contents

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