Skills when-training-neural-networks-use-flow-nexus-neural
📦
when-training-neural-networks-use-flow-nexus-neural
Content revision r1 Medium Risk 🔑 Env variables⚙️ External commands🌐 Network access
Train Neural Networks with Flow Nexus
Distributed neural network training requires coordinated setup, validation, and deployment. This skill guides Flow Nexus workflows from architecture design to production serving.
Supports: Claude Codex Code(CC)
Install with my Agent
Copy this request to your Agent. It includes the canonical Skill page and manifest.
Agent request
Review the Skillstore skill "when-training-neural-networks-use-flow-nexus-neural" from https://skillstore.io/skills/dnyoussef-when-training-neural-networks-use-flow-nexus-neural.md and its manifest at https://skillstore.io/api/skills/dnyoussef-when-training-neural-networks-use-flow-nexus-neural/manifest. Verify the artifact. You may proceed after verification, subject to the environment's own policy.Your Agent should still show its plan and request any confirmation required by the security policy.
Agent-readable resources
Use these links when an AI agent, crawler, or script needs clean context instead of reading the full page.
Test it
Using "when-training-neural-networks-use-flow-nexus-neural". Train a transformer classifier with Flow Nexus and prepare it for serving.
Expected outcome:
A phased plan covering authentication, cluster setup, architecture selection, distributed training, validation metrics, and deployment readiness.
Using "when-training-neural-networks-use-flow-nexus-neural". Review whether my trained model is ready for production.
Expected outcome:
A validation summary with accuracy, latency, overfitting checks, benchmark status, and recommended deployment actions.
Using "when-training-neural-networks-use-flow-nexus-neural". Create a deployment handoff for a completed neural network project.
Expected outcome:
A handoff outline with model metadata, serving endpoints, monitoring expectations, health checks, and operational cautions.
Security Audit
Medium Riskv8 • 7/5/2026 Open versioned report
Most static detections are Markdown fences, workflow diagrams, local URLs, or illustrative labels rather than malicious code. Confirmed concerns are documented commands that may execute unpinned npx packages and a deployment example that can expose an unauthenticated inference service. No prompt injection or secret exfiltration intent was found.
4
Files scanned
1,664
Lines analyzed
15
Review items
0
False positives ignored
Confirmed security concerns (2)
Medium
Unpinned Remote Package Execution
The SOP asks users or agents to run npx flow-nexus@latest and npx claude-flow@alpha. These dist-tags can execute package code that changes over time without a pinned version.
The cited commands directly invoke npm packages through mutable latest and alpha tags. This is a clear supply-chain risk when automated by an agent.
Medium
Unauthenticated Inference Service Exposure
The deployment example binds Uvicorn to all interfaces and maps port 8000 while exposing prediction and metrics endpoints without authentication.
The service binds to 0.0.0.0 and the deploy script publishes the port. The FastAPI example shows health, predict, and metrics routes with no access control.
Capability review items (15)
These are real local capabilities that may be expected for this skill, so they require review but are not counted as confirmed malicious behavior.
Medium
Shell command substitution
npx claude-flow@alpha hooks session-restore --session-id "neural-training-$(date +%s)"
This documented shell example invokes npx claude-flow@alpha while using command substitution. Running unpinned package commands from a skill can execute changing third-party code in the user environment.
Medium
Shell command substitution
--value "{\"status\": \"complete\", \"cluster_id\": \"[id]\", \"timestamp\": \"$(date -Iseconds)\"}"
This documented shell example invokes npx claude-flow@alpha while using command substitution. Running unpinned package commands from a skill can execute changing third-party code in the user environment.
Medium
Shell command substitution
CLUSTER_ID=$(npx claude-flow@alpha memory retrieve --key "neural/cluster-id" | jq -r '.value')
This documented shell example invokes npx claude-flow@alpha while using command substitution. Running unpinned package commands from a skill can execute changing third-party code in the user environment.
Medium
Shell command substitution
--value "{\"status\": \"complete\", \"nodes\": 4, \"topology\": \"mesh\", \"timestamp\": \"$(date -I
This documented shell example invokes npx claude-flow@alpha while using command substitution. Running unpinned package commands from a skill can execute changing third-party code in the user environment.
Medium
Shell command substitution
CLUSTER_ID=$(npx claude-flow@alpha memory retrieve --key "neural/cluster-id" | jq -r '.value')
This documented shell example invokes npx claude-flow@alpha while using command substitution. Running unpinned package commands from a skill can execute changing third-party code in the user environment.
Medium
Shell command substitution
STATUS=$(npx claude-flow@alpha memory retrieve --key "neural/training-status" | jq -r '.value')
This documented shell example invokes npx claude-flow@alpha while using command substitution. Running unpinned package commands from a skill can execute changing third-party code in the user environment.
Medium
Shell command substitution
--value "{\"status\": \"complete\", \"training_job\": \"$TRAINING_JOB_ID\", \"final_accuracy\": 0.94
This documented shell example invokes npx claude-flow@alpha while using command substitution. Running unpinned package commands from a skill can execute changing third-party code in the user environment.
Medium
Shell command substitution
CLUSTER_ID=$(npx claude-flow@alpha memory retrieve --key "neural/cluster-id" | jq -r '.value')
This documented shell example invokes npx claude-flow@alpha while using command substitution. Running unpinned package commands from a skill can execute changing third-party code in the user environment.
Medium
Shell command substitution
TRAINING_METRICS=$(npx claude-flow@alpha memory retrieve --key "neural/training-metrics" | jq -r '.v
This documented shell example invokes npx claude-flow@alpha while using command substitution. Running unpinned package commands from a skill can execute changing third-party code in the user environment.
Medium
Shell command substitution
--value "{\"inference_latency_ms\": 67, \"throughput_qps\": 1200, \"memory_mb\": 512, \"timestamp\":
This documented shell example invokes npx claude-flow@alpha while using command substitution. Running unpinned package commands from a skill can execute changing third-party code in the user environment.
Medium
Shell command substitution
--value "{\"status\": \"complete\", \"validation_passed\": true, \"accuracy\": 0.94, \"latency_ms\":
This documented shell example invokes npx claude-flow@alpha while using command substitution. Running unpinned package commands from a skill can execute changing third-party code in the user environment.
Medium
Shell command substitution
--value "{\"model_id\": \"$TRAINING_JOB_ID\", \"template_published\": true, \"timestamp\": \"$(date
This documented shell example invokes npx claude-flow@alpha while using command substitution. Running unpinned package commands from a skill can execute changing third-party code in the user environment.
Medium
Shell command substitution
--value "{\"status\": \"complete\", \"deployment\": \"docker\", \"api\": \"fastapi\", \"port\": 8000
This documented shell example invokes npx claude-flow@alpha while using command substitution. Running unpinned package commands from a skill can execute changing third-party code in the user environment.
Medium
Shell command substitution
--value "{\"status\": \"success\", \"model_accuracy\": 0.94, \"latency_ms\": 67, \"deployed\": true,
This documented shell example invokes npx claude-flow@alpha while using command substitution. Running unpinned package commands from a skill can execute changing third-party code in the user environment.
Medium
Hardcoded IP address
CMD ["uvicorn", "serve:app", "--host", "0.0.0.0", "--port", "8000"]
The Dockerfile example binds Uvicorn to 0.0.0.0, and the deployment script maps port 8000. If copied as-is, it can expose an unauthenticated inference API.
Risk Factors
🔑 Env variables (1)
⚙️ External commands (87)
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🌐 Network access (10)
Audited by: codex View Audit History →
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APA citation
DNYoussef. (2026). when-training-neural-networks-use-flow-nexus-neural security audit report (audit version 8) [Author version unspecified]. Skillstore. https://skillstore.io/skills/dnyoussef-when-training-neural-networks-use-flow-nexus-neural/audits/8BibTeX citation
@techreport{dnyoussef-dnyoussef-when-training-neural-networks-use-flow-nexus-neural-2026,
author = {DNYoussef},
title = {when-training-neural-networks-use-flow-nexus-neural security audit report (audit version 8)},
institution = {Skillstore},
year = {2026},
number = {8},
url = {https://skillstore.io/skills/dnyoussef-when-training-neural-networks-use-flow-nexus-neural/audits/8},
note = {Author version unspecified}
}CITATION.cff
cff-version: 1.2.0
message: "If you use this Skill, cite its author and this versioned security audit report."
title: "when-training-neural-networks-use-flow-nexus-neural security audit report (audit version 8)"
version: "unspecified"
type: report
authors:
- name: "DNYoussef"
date-released: "2026-07-05"
url: "https://skillstore.io/skills/dnyoussef-when-training-neural-networks-use-flow-nexus-neural/audits/8"
identifiers:
- type: other
value: "skillstore:dnyoussef-when-training-neural-networks-use-flow-nexus-neural:audit:8"
description: "Skillstore immutable audit report identifier"
Skillstore Score
Why this score Evidence Confidence: High 55
Architecture
85
Maintainability
87
Content
72
Community
74
Spec Compliance
What You Can Build
Plan Distributed Training
Create a structured training plan with architecture, dataset, validation, and benchmark steps.
Coordinate Flow Nexus Resources
Map cluster setup, node deployment, memory coordination, and distributed training operations.
Prepare Model Serving
Draft Docker, FastAPI, health check, metrics, and deployment documentation for a trained model.
Try These Prompts
Plan a Training Run
Use this skill to plan a Flow Nexus neural network training run for my task, dataset type, target accuracy, and deployment goal.
Configure the Architecture
Use this skill to propose the model architecture, hyperparameters, validation checks, and Flow Nexus resources for my neural network.
Validate and Benchmark
Use this skill to design validation tests, performance benchmarks, acceptance thresholds, and troubleshooting steps for my trained model.
Prepare Production Deployment
Use this skill to produce a secure deployment checklist, serving API plan, monitoring plan, and rollout steps for my trained model.
Best Practices
- Replace all placeholder model IDs, credentials, metrics, and dataset values before running commands.
- Pin package versions and review shell commands before allowing an agent to execute them.
- Add authentication, access controls, and observability before exposing an inference service.
Avoid
- Running the sample deployment commands unchanged in a shared or production environment.
- Treating illustrative accuracy, latency, and throughput numbers as real validation evidence.
- Publishing model endpoints without authentication, rate limits, monitoring, or rollback plans.
Frequently Asked Questions
What does this skill help with?
It helps plan and execute a Flow Nexus neural network workflow from setup to deployment.
Does it train a model by itself?
No. It provides workflow instructions and examples that require Flow Nexus tools and user-provided data.
Which tools are referenced?
It references Flow Nexus MCP actions, Claude Flow hooks, E2B sandboxes, Docker, FastAPI, and TensorFlow serving concepts.
Can I use it with Claude Code or Codex?
Yes. It is written as a procedural skill for Claude, Claude Code, and Codex compatible workflows.
What should I review before running commands?
Review package versions, shell scripts, Docker settings, network exposure, credentials, and local file paths.
Is it suitable for production deployment?
It can support production planning, but users must add security controls, governance, testing, and operational review.
Developer Details
Author
DNYoussefLicense
MIT
Skillstore revision
r1
Version notice
The author did not declare a version.
Ref
c579efd02184f1128d6741d16e6c8d951d7d4208
Maintenance freshness
7/23/2026
Usage
7 downloads · 331 views
File structure