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 Soulcynics404/AgentForge --skill agent-v3-memory-specialistgit clone --depth 1 https://github.com/Soulcynics404/AgentForgeWrote 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/soulcynics404/agentforge/agent-v3-memory-specialist)<a href="https://agentmods.dev/skills/soulcynics404/agentforge/agent-v3-memory-specialist"><img src="https://agentmods.dev/badge/skills/soulcynics404/agentforge/agent-v3-memory-specialist/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/soulcynics404/agentforge/agent-v3-memory-specialist"><img src="https://agentmods.dev/badge/skills/soulcynics404/agentforge/agent-v3-memory-specialist.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 | $0.00025 | $0.02192 |
| Opus 5 | $0.00013 | $0.01096 |
| Sonnet 5 | $0.00005 | $0.00438 |
| Haiku 4.5 | $0.00003 | $0.00219 |
Grade A, and why
agent-v3-memory-specialist 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 6d 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 agent-v3-memory-specialist — 0 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.
How it starts
The opening of the file, as written. The whole thing — 323 lines — stays where its author put it; the contents beside it link to each section on GitHub.
name: v3-memory-specialist version: "3.0.0-alpha" updated: "2026-01-04" description: V3 Memory Specialist for unifying 6+ memory systems into AgentDB with HNSW indexing. Implements ADR-006 (Unified Memory Service) and ADR-009 (Hybrid Memory Backend) to achieve 150x-12,500x search improvements. color: cyan metadata: v3_role: "specialist" agent_id: 7 priority: "high" domain: "memory" phase: "core_systems" hooks: pre_execution: | echo "🧠 V3 Memory Specialist starting memory system unification..."
# Check current memory systems
echo "📊 Current memory systems to unify:"
echo " - MemoryManager (legacy)"
echo " - DistributedMemorySystem"
echo " - SwarmMemory"
echo " - AdvancedMemoryManager"
echo " - SQLiteBackend"
echo " - MarkdownBackend"
echo " - HybridBackend"
# Check AgentDB integration status
npx agentic-flow@alpha --version 2>$dev$null | head -1 || echo "⚠️ agentic-flow@alpha not detected"
echo "🎯 Target: 150x-12,500x search improvement via HNSW"
echo "🔄 Strategy: Gradual migration with backward compatibility"
post_execution: | echo "🧠 Memory unification milestone complete"
# Store memory patterns
npx agentic-flow@alpha memory store-pattern \
--session-id "v3-memory-$(date +%s)" \
--task "Memory Unification: $TASK" \
--agent "v3-memory-specialist" \
--performance-improvement "150x-12500x" 2>$dev$null || true
V3 Memory Specialist
🧠 Memory System Unification & AgentDB Integration Expert
Mission: Memory System Convergence
Unify 7 disparate memory systems into a single, high-performance AgentDB-based solution with HNSW indexing, achieving 150x-12,500x search performance improvements while maintaining backward compatibility.
Systems to Unify
Current Memory Landscape
┌─────────────────────────────────────────┐
│ LEGACY SYSTEMS │
├─────────────────────────────────────────┤
│ • MemoryManager (basic operations) │
│ • DistributedMemorySystem (clustering) │
│ • SwarmMemory (agent-specific) │
│ • AdvancedMemoryManager (features) │
│ • SQLiteBackend (structured) │
│ • MarkdownBackend (file-based) │
│ • HybridBackend (combination) │
└─────────────────────────────────────────┘
↓
┌─────────────────────────────────────────┐
│ V3 UNIFIED SYSTEM │
├─────────────────────────────────────────┤
│ 🚀 AgentDB with HNSW │
│ • 150x-12,500x faster search │
│ • Unified query interface │
│ • Cross-agent memory sharing │
│ • SONA integration learning │
│ • Automatic persistence │
└─────────────────────────────────────────┘
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.
- 6d ago First seen · 323 lines · 25 tokens per session scan A a6ab754e61cc
agent-v3-memory-specialist is a skill published in the GitHub repository Soulcynics404/AgentForge (1 stars, last pushed 16d ago), licensed MIT. It adds 25 tokens to every session and 2,192 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to agent-v3-memory-specialist, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
memory-lancedb
LanceDB-backed vector memory for high-volume embedding and retrieval workloads.
pinecone-research
Agent RAG and long-term memory with Pinecone.
browserwing-admin
Manage and operate BrowserWing — an intelligent browser automation platform. Install dependencies, configure LLM, create/manage/execute automation scripts, use AI-driven exploration to generate scripts, browse the script marketplace, and troubleshoot issues.
vector-db
Vector database expert for embeddings, similarity search, RAG patterns, and indexing strategies.
karpathy-llm-wiki
Use when building or maintaining a personal LLM-powered knowledge base. Triggers: ingesting sources into a wiki, querying wiki knowledge, linting wiki quality, 'add to wiki', 'what do I know about', or any mention of 'LLM wiki' or 'Karpathy wiki'.
Vizra ADK Memory System
Implement persistent memory, session context, and vector memory (RAG) for AI agents.