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 agentmods add commands/airmcp-com/mcp-standards/neural-patternsgit clone --depth 1 https://github.com/airmcp-com/mcp-standardsWrote 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/commands/airmcp-com/mcp-standards/neural-patterns)<a href="https://agentmods.dev/commands/airmcp-com/mcp-standards/neural-patterns"><img src="https://agentmods.dev/badge/commands/airmcp-com/mcp-standards/neural-patterns.svg" alt="Measured on agentmods" 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 | $0.00000 | $0.00399 |
| Opus 5 | $0.00000 | $0.00199 |
| Sonnet 5 | $0.00000 | $0.00080 |
| Haiku 4.5 | $0.00000 | $0.00040 |
Grade A, and why
neural-patterns 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 2d 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
92% identical to neural-patterns — 31 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 Pattern Training
Purpose
Continuously improve coordination through neural network learning.
How Training Works
1. Automatic Learning
Every successful operation trains the neural networks:
- Edit patterns for different file types
- Search strategies that find results faster
- Task decomposition approaches
- Agent coordination patterns
2. Manual Training
Tool: mcp__claude-flow__neural_train
Parameters: {
"pattern_type": "coordination",
"training_data": "successful task patterns",
"epochs": 50
}
3. Pattern Types
Cognitive Patterns:
- Convergent: Focused problem-solving
- Divergent: Creative exploration
- Lateral: Alternative approaches
- Systems: Holistic thinking
- Critical: Analytical evaluation
- Abstract: High-level design
4. Improvement Tracking
Tool: mcp__claude-flow__neural_status
Result: {
"patterns": {
"convergent": 0.92,
"divergent": 0.87,
"lateral": 0.85
},
"improvement": "5.3% since last session",
"confidence": 0.89
}
Pattern Analysis
Tool: mcp__claude-flow__neural_patterns
Parameters: {
"action": "analyze",
"operation": "recent_edits"
}
Benefits
- 🧠 Learns your coding style
- 📈 Improves with each use
- 🎯 Better task predictions
- ⚡ Faster coordination
CLI Usage
# Train neural patterns via CLI
npx claude-flow neural train --type coordination --epochs 50
# Check neural status
npx claude-flow neural status
# Analyze patterns
npx claude-flow neural patterns --analyze
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.
- 2d ago First seen · 74 lines · 0 tokens per session scan A 80ea2d4261c4
neural-patterns is a command published in the GitHub repository airmcp-com/mcp-standards (3 stars, last pushed 9mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 399 tokens. A static security scan graded it A with 0 findings. It is 92% identical to neural-patterns, differing in 31 lines, and is treated as a copy.
Other commands, from other repositories
auto
계획부터 PR까지 원버튼 자동 실행. 중간에 멈추지 않습니다.
sync
Command "sync" from sangrokjung/claude-forge, covering /sync - git pull + 문서 동기화, 플래그, phase 1: git pull (--no-pull 시 스킵), 1-1. 현재 상태 확인 and 1-2. pull 실행.
next-task
Command "next-task" from sangrokjung/claude-forge, covering /next-task - 다음 task 시작 준비 (v6), 0단계: 현재 상태 확인, 1단계: 진행률 계산, 2단계: 다음 task 식별 and 3단계: 복잡도 분석.
suggest-automation
반복 패턴 분석하여 자동화 기회 제안 (주기적 실행 권장).
verify-loop
자동 재검증 루프 (최대 3회 재시도, 실패 시 자동 수정).
evolve
Cluster related instincts into skills, commands, or agents.