adaptyv

Cloud laboratory platform for automated protein testing and validation. Use when designing proteins and needing experimental validation including binding assays, expression testing, thermostability measurements, enzyme activity assays, or protein sequence optimization. Also use for submitting experiments via API, tracking experiment status, downloading results, optimizing protein sequences for better expression using computational tools (NetSolP, SoluProt, SolubleMPNN, ESM), or managing protein design workflows with wet-lab validation.

Quality

82%

Does it follow best practices?

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Securityby

Passed

No findings from the security scan

Quality

Content

65%

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The body is actionable with a solid executable quick start and a clean section structure that points outward to references. Its main weaknesses are missing response-validation and status-tracking feedback loops in the core submission workflow, plus referenced bundle files that are mis-pathed and absent.

Suggestions

Add response status checking and error handling after the API POST, and include a short status-polling/retry loop so the submission workflow has an explicit validation checkpoint (raises workflow_clarity).

Fix the reference paths: the body says 'reference/...' (singular) but the bundle directory convention is 'references/'; either correct the paths or create the referenced files so navigation resolves (raises progressive_disclosure).

Remove the redundancy between the opening paragraph and the frontmatter description, and drop the repeated ~21-day note from Important Notes, to tighten token efficiency (raises conciseness).

Dimension	Reasoning	Score
Conciseness	Mostly efficient but the opening paragraph ('Adaptyv is a cloud laboratory platform...') restates the description and the Important Notes section repeats the ~21-day delivery time, so it could be tightened.	2 / 3
Actionability	Provides fully executable, copy-paste-ready guidance: an export/UV install commands, environment-variable setup, and a complete Python requests submission example with a real base URL and JSON payload.	3 / 3
Workflow Clarity	The Quick Start sequence is present (contact support → token → env var → install → submit) but lacks validation checkpoints: the submission example does no response status/error checking and no status-polling feedback loop is shown for a ~21-day batch operation.	2 / 3
Progressive Disclosure	References are well-signaled and one-level deep (experiments.md, protein_optimization.md, api_reference.md, examples.md), but the body uses the singular path 'reference/' while no such files exist, so the split structure does not resolve to real content.	2 / 3
	Total	9 / 12 Passed

Description

100%

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-constructed description that clearly states the platform's purpose, enumerates specific capabilities, and provides explicit natural-language triggers for when to invoke it. It is concise yet comprehensive with no notable over-claims.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions including 'binding assays, expression testing, thermostability measurements, enzyme activity assays' plus 'submitting experiments via API, tracking experiment status, downloading results, optimizing protein sequences'.	3 / 3
Completeness	Explicitly answers what ('Cloud laboratory platform for automated protein testing and validation') and when with both 'Use when designing proteins...' and 'Also use for...' trigger clauses.	3 / 3
Trigger Term Quality	Uses natural domain terms a user would actually say such as 'designing proteins', 'binding assays', 'thermostability measurements', and 'enzyme activity assays'.	3 / 3
Distinctiveness Conflict Risk	The wet-lab protein validation / cloud laboratory niche with assay-specific triggers is clearly distinguishable and unlikely to fire for unrelated skills.	3 / 3
	Total	12 / 12 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/adaptyv/SKILL.md
Commit: f627ab5

Reviewed: about 9 hours ago

Table of Contents

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