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

76%

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tessl review fix ./bundled/skills/adaptyv/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.

The body is well-organized and provides an executable quick-start path, but it relies on four referenced files that are missing from the bundle and lacks validation checkpoints for a costly submission workflow. These gaps hold it back from a strong score despite good structure.

Suggestions

Provide the referenced bundle files (reference/experiments.md, reference/api_reference.md, reference/protein_optimization.md, reference/examples.md) or remove the dangling references so the signaled navigation actually resolves.

Add validation checkpoints to the submission workflow — check the HTTP response status code, validate the sequence/format before posting, and confirm an experiment_id was returned — since submitting to a wet lab is a costly, hard-to-reverse action.

Inline a minimal status-tracking and result-retrieval example rather than only linking out, so the core submit → track → download loop is self-contained in SKILL.md.

Dimension	Reasoning	Score
Conciseness	Mostly efficient with executable code blocks and tight bullet lists; the only padding is the opening paragraph restating the description and the brief 'Important Notes' that partly repeats earlier facts.	4 / 5
Actionability	The authentication and experiment-submission flow is fully executable, copy-paste-ready Python, but status tracking, result retrieval, and batch processing are only pointed at reference/examples.md with no inline code.	4 / 5
Workflow Clarity	A rough sequence exists (contact support, set env var, install, submit) but there are no validation checkpoints, and the submission code never checks the HTTP response status or confirms the experiment_id — a concern for a costly ~21-day wet-lab operation, which caps this dimension.	3 / 5
Progressive Disclosure	The body cleanly signals one-level-deep references (reference/experiments.md, protein_optimization.md, api_reference.md, examples.md), but none of these files exist in the bundle — the references/ (or reference/) directory is absent — so navigation is broken and the detailed content is unreachable.	2 / 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-structured description that concretely states capabilities and provides explicit 'Use when' / 'Also use for' trigger guidance with minimal conflict risk. The only minor weakness is a slight reliance on tool-name jargon in place of conversational synonyms.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions spanning the domain — 'binding assays, expression testing, thermostability measurements, enzyme activity assays' plus 'submitting experiments via API, tracking experiment status, downloading results, optimizing protein sequences' — giving comprehensive coverage rather than a vague domain label.	5 / 5
Completeness	Explicitly answers both what ('Cloud laboratory platform for automated protein testing and validation') and when ('Use when designing proteins and needing experimental validation...', 'Also use for...') with concrete trigger phrases.	5 / 5
Trigger Term Quality	Natural domain phrases a protein designer would say ('binding assays', 'expression testing', 'protein sequence optimization') are present, but the long enumeration of tool names (NetSolP, SoluProt, SolubleMPNN, ESM) leans toward jargon over user-spoken synonyms, leaving a few natural phrasings uncovered.	4 / 5
Distinctiveness Conflict Risk	Occupies a clear niche — wet-lab validation of designed proteins via API submission — with triggers specific enough to avoid overlap with general protein-design or generic API 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/adaptyv/SKILL.md
Commit: d5ae560

Reviewed: 1 day ago

Table of Contents

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