flow-nexus-neural

Train and deploy neural networks in distributed E2B sandboxes with Flow Nexus

2.63x

Quality

65%

Does it follow best practices?

Impact

95%

2.63x

Average score across 3 eval scenarios

Securityby

Low

Low-risk findings worth noting

Fix and improve this skill with Tessl

tessl review fix ./.claude/skills/flow-nexus-neural/SKILL.md

Quality

Content

65%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 highly actionable with executable examples throughout, but it is verbose with duplicated sections and inlines a large API reference that belongs in separate files. Destructive/batch workflows also lack validation checkpoints.

Suggestions

Move the full API reference and architecture-pattern blocks into separate reference files (e.g. REFERENCE.md, ARCHITECTURES.md) and keep SKILL.md a concise overview with one-level-deep links.

Add explicit validation checkpoints and a validate→fix→retry loop to the distributed-training and cluster-terminate workflows.

Remove duplicated examples between 'Architecture Patterns'/'Common Use Cases' and the capability sections to tighten the document.

Dimension	Reasoning	Score
Conciseness	Mostly efficient API reference, but the 'Architecture Patterns' and 'Common Use Cases' sections duplicate examples already shown, and verbose response JSON plus commentary like the self-learning blurb add padding that could be trimmed.	3 / 5
Actionability	Provides fully executable, copy-paste-ready MCP tool calls with realistic parameters and example responses covering the common cases (train, predict, cluster init, distributed training, publishing).	5 / 5
Workflow Clarity	Multi-step cluster workflows are sequenced (init → deploy → connect → train → monitor → terminate), but these are batch/destructive operations with no validation checkpoints or validate→fix→retry feedback loops, which caps workflow clarity at 3.	3 / 5
Progressive Disclosure	Has clear section headers and numbered capabilities, but the bulk of API reference (full request/response blocks, architecture patterns) is inlined in one ~600-line file with no separate reference files, so content that should be split is inline.	3 / 5
	Total	14 / 20 Passed

Description

66%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 and distinguishable, naming concrete actions and a clear niche, but it omits any explicit 'when to use' trigger guidance. Adding a Use-when clause with natural synonyms would lift completeness and trigger quality.

Suggestions

Add an explicit trigger clause, e.g. 'Use when training or deploying neural networks, running distributed model training, or working with E2B sandbox clusters.'

Broaden trigger term coverage with synonyms like 'deep learning', 'model training', and 'GPU training' to match more natural user phrasing.

Mention additional concrete capabilities (inference, benchmarking, template marketplace) to make coverage more comprehensive.

Dimension	Reasoning	Score
Specificity	Names the domain (neural networks, distributed E2B sandboxes) and lists multiple concrete actions ('Train and deploy'), with minor gaps such as inference, monitoring, and marketplace usage not mentioned.	4 / 5
Completeness	Provides a clear 'what' ('Train and deploy neural networks in distributed E2B sandboxes') but has no 'Use when...' or equivalent trigger clause, which caps completeness at 3 per the rubric guidelines.	3 / 5
Trigger Term Quality	Includes natural terms like 'neural networks', 'distributed', and 'deploy' that users would say, but misses common synonyms such as 'deep learning', 'model training', or 'GPU'.	4 / 5
Distinctiveness Conflict Risk	'distributed E2B sandboxes with Flow Nexus' carves a specific niche with distinct triggers, though there is minor overlap risk with related flow-nexus skills.	4 / 5
	Total	15 / 20 Passed

Validation

87%

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 — 14 / 16 Passed

Validation for skill structure

Criteria	Description	Result
skill_md_line_count	SKILL.md is long (757 lines); consider splitting into references/ and linking	Warning
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	14 / 16 Passed

Repository: ruvnet/RuVector
Path: .claude/skills/flow-nexus-neural/SKILL.md
Commit: 14db7a3

Reviewed: 3 days ago

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