Name: flow-nexus-neural
Rating: 68.60000000000001 (1 reviews)
Author: ruvnet

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flow-nexus-neural

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

2.07x

Quality

59%

Does it follow best practices?

Impact

79%

2.07x

Average score across 3 eval scenarios

Securityby

Low

Low-risk findings worth noting

Fix and improve this skill with Tessl

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

The canonical home for this skill is flow-nexus-neural in ruvnet/claude-flow

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 skill is highly actionable, dominated by concrete copy-paste MCP calls, but it is a monolithic 738-line document with redundant example sections and no external reference files. Workflow sequencing is clear yet lacks explicit embedded validation checkpoints for expensive distributed-training operations.

Suggestions

Move the bulk of the API reference, full JSON response schemas, and Architecture Patterns into separate reference files (e.g. references/api.md, references/architectures.md) and link to them one level deep from SKILL.md.

Remove the redundant "Architecture Patterns" and "Common Use Cases" sections that re-show feedforward/LSTM/transformer/federated examples already present in Core Capabilities, or consolidate them.

Embed explicit validation checkpoints in the distributed-training workflow (e.g. "Only proceed to train_distributed when cluster_status returns all nodes active; re-check after each epoch milestone").

Dimension	Reasoning	Score
Conciseness	The body is mostly efficient MCP API examples rather than concept explanation, but the "Architecture Patterns" and "Common Use Cases" sections re-demonstrate calls already shown in Core Capabilities, adding redundancy that could be trimmed.	3 / 5
Actionability	Nearly every section is a copy-paste-ready MCP tool call with full parameter objects and example responses, covering the common training, inference, cluster, and marketplace cases.	5 / 5
Workflow Clarity	The distributed-training flow (init → deploy → connect → train → status → terminate) is clearly sequenced and validation/benchmark tools exist, but validation is only mentioned as a best practice rather than embedded as explicit validate-then-proceed checkpoints in the batch training workflows.	3 / 5
Progressive Disclosure	Headers organize the content, but with no bundle files the entire ~738-line API reference, response schemas, architecture patterns, and troubleshooting are inlined in SKILL.md rather than split into one-level-deep reference files.	3 / 5
	Total	14 / 20 Passed

Description

53%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 names a clear niche, but it lacks an explicit "Use when..." trigger clause and broader keyword coverage, capping completeness and trigger-term quality at the midpoint. It is distinguishable from sibling skills but not maximally comprehensive.

Suggestions

Add an explicit trigger clause, e.g. "Use when training, deploying, or running inference on neural networks, or when the user mentions distributed training, federated learning, or E2B sandboxes."

Expand the action list beyond train/deploy to reflect inference, marketplace templates, and distributed clusters for fuller coverage.

Include natural synonyms users say ("deep learning", "train models", "machine learning models") alongside "neural networks".

Dimension	Reasoning	Score
Specificity	"Train and deploy neural networks in distributed E2B sandboxes with Flow Nexus" names the domain plus two concrete actions (train, deploy), but omits other capabilities the body covers (inference, marketplace, clusters), so it is not comprehensive.	3 / 5
Completeness	The description gives a clear "what" but no "Use when..." trigger clause, and per the rubric a missing explicit trigger guidance caps completeness at 3.	3 / 5
Trigger Term Quality	"neural networks" and "train and deploy" are natural terms, but "distributed E2B sandboxes" is jargon and common synonyms like "deep learning", "train models", or file/extension triggers are absent.	3 / 5
Distinctiveness Conflict Risk	The product (Flow Nexus), infrastructure (E2B sandboxes), and task (neural networks) form a mostly distinct niche with only minor overlap risk against the sibling flow-nexus skills listed in the body.	4 / 5
	Total	13 / 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 (739 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/ruflo
Path: .agents/skills/flow-nexus-neural/SKILL.md
Commit: fa13ee4

Reviewed: about 4 hours ago

Table of Contents

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