Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add akhilyad/deployy --skill neural-traininggit clone --depth 1 https://github.com/akhilyad/deployyWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/akhilyad/deployy/neural-training)<a href="https://agentmods.dev/skills/akhilyad/deployy/neural-training"><img src="https://agentmods.dev/badge/skills/akhilyad/deployy/neural-training/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/akhilyad/deployy/neural-training"><img src="https://agentmods.dev/badge/skills/akhilyad/deployy/neural-training.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00064 | $0.00426 |
| Opus 5.5 | $0.00026 | $0.00170 |
| Sonnet 5.5 | $0.00013 | $0.00085 |
| Haiku 4.5 | $0.00006 | $0.00043 |
Grade A, and why
neural-training scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 24d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to neural-training — 10 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
Neural Training Skill
Purpose
Train and optimize neural patterns using SONA, MoE, and EWC++ systems.
When to Trigger
- Training new patterns
- Optimizing agent routing
- Knowledge consolidation
- Pattern recognition tasks
Intelligence Pipeline
- RETRIEVE — Fetch relevant patterns via HNSW (150x-12,500x faster)
- JUDGE — Evaluate with verdicts (success$failure)
- DISTILL — Extract key learnings via LoRA
- CONSOLIDATE — Prevent catastrophic forgetting via EWC++
Components
| Component | Purpose | Performance |
|---|---|---|
| SONA | Self-optimizing adaptation | <0.05ms |
| MoE | Expert routing | 8 experts |
| HNSW | Pattern search | 150x-12,500x |
| EWC++ | Prevent forgetting | Continuous |
| Flash Attention | Speed | 2.49x-7.47x |
Commands
Train Patterns
npx hyrex neural train --model-type moe --epochs 10
Check Status
npx hyrex neural status
View Patterns
npx hyrex neural patterns --type all
Predict
npx hyrex neural predict --input "task description"
Optimize
npx hyrex neural optimize --target latency
Best Practices
- Use pretrain hook for batch learning
- Store successful patterns after completion
- Consolidate regularly to prevent forgetting
- Route based on task complexity
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 24d ago First seen · 69 lines · 64 tokens per session scan A c9f45afcd4c7
neural-training is a skill published in the GitHub repository akhilyad/deployy (0 stars, last pushed 5mo ago), licensed MIT. It adds 64 tokens to every session and 426 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to neural-training, differing in 10 lines, and is treated as a copy.
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