Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/summarybotng/summarybot-ngnpx agentmods add skills/summarybotng/summarybot-ng/reasoningbank-agentdbWrote 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/summarybotng/summarybot-ng/reasoningbank-agentdb)<a href="https://agentmods.dev/skills/summarybotng/summarybot-ng/reasoningbank-agentdb"><img src="https://agentmods.dev/badge/skills/summarybotng/summarybot-ng/reasoningbank-agentdb.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 | $0.00056 | $0.02645 |
| Opus 5 | $0.00028 | $0.01323 |
| Sonnet 5 | $0.00011 | $0.00529 |
| Haiku 4.5 | $0.00006 | $0.00265 |
Grade A, and why
ReasoningBank with AgentDB 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 4d 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 ReasoningBank with AgentDB — 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 — 447 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ReasoningBank with AgentDB
What This Skill Does
Provides ReasoningBank adaptive learning patterns using AgentDB's high-performance backend (150x-12,500x faster). Enables agents to learn from experiences, judge outcomes, distill memories, and improve decision-making over time with 100% backward compatibility.
Performance: 150x faster pattern retrieval, 500x faster batch operations, <1ms memory access.
Prerequisites
- Node.js 18+
- AgentDB v1.0.7+ (via agentic-flow)
- Understanding of reinforcement learning concepts (optional)
Quick Start with CLI
Initialize ReasoningBank Database
# Initialize AgentDB for ReasoningBank
npx agentdb@latest init ./.agentdb/reasoningbank.db --dimension 1536
# Start MCP server for Claude Code integration
npx agentdb@latest mcp
claude mcp add agentdb npx agentdb@latest mcp
Migrate from Legacy ReasoningBank
# Automatic migration with validation
npx agentdb@latest migrate --source .swarm/memory.db
# Verify migration
npx agentdb@latest stats ./.agentdb/reasoningbank.db
Quick Start with API
import { createAgentDBAdapter, computeEmbedding } from 'agentic-flow/reasoningbank';
// Initialize ReasoningBank with AgentDB
const rb = await createAgentDBAdapter({
dbPath: '.agentdb/reasoningbank.db',
enableLearning: true, // Enable learning plugins
enableReasoning: true, // Enable reasoning agents
cacheSize: 1000, // 1000 pattern cache
});
// Store successful experience
const query = "How to optimize database queries?";
const embedding = await computeEmbedding(query);
await rb.insertPattern({
id: '',
type: 'experience',
domain: 'database-optimization',
pattern_data: JSON.stringify({
embedding,
pattern: {
query,
approach: 'indexing + query optimization',
outcome: 'success',
metrics: { latency_reduction: 0.85 }
}
}),
confidence: 0.95,
usage_count: 1,
success_count: 1,
created_at: Date.now(),
last_used: Date.now(),
});
// Retrieve similar experiences with reasoning
const result = await rb.retrieveWithReasoning(embedding, {
domain: 'database-optimization',
k: 5,
useMMR: true, // Diverse results
synthesizeContext: true, // Rich context synthesis
});
console.log('Memories:', result.memories);
console.log('Context:', result.context);
console.log('Patterns:', result.patterns);
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.
- 4d ago First seen · 447 lines · 56 tokens per session scan A d60a616ad0bb
ReasoningBank with AgentDB is a skill published in the GitHub repository summarybotng/summarybot-ng (2 stars, last pushed 3mo ago), licensed MIT. It adds 56 tokens to every session and 2,645 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to ReasoningBank with AgentDB, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
embeddings
Vector embeddings with HNSW indexing, sql.js persistence, and hyperbolic support. 75x faster with agentic-flow integration. Use when: semantic search, pattern matching, similarity queries, knowledge retrieval. Skip when: exact text matching, simple lookups, no semantic understanding needed.
agent-platform-rag-engine-management
Manage and query Agent Platform RAG Engine Corpora and retrieve grounded contexts using the Google GenAI SDK. Use when listing RAG corpora or files, inspecting a corpus, retrieving contexts, or generating content grounded in a RAG corpus. Do not use for standard database queries (use SQL/Spanner skills), Google…
llm-app-patterns
Production-ready patterns for building LLM applications. Covers RAG pipelines, agent architectures, prompt IDEs, and LLMOps monitoring. Use when designing AI applications, implementing RAG, building agents, or setting up LLM observability.
9router-embeddings
Generate vector embeddings via 9Router /v1/embeddings using OpenAI / Gemini / Mistral / Voyage / Nvidia / GitHub embedding models for RAG, semantic search, similarity. Use when the user wants embeddings, vectors, RAG, semantic search, or to embed text.
azure-search-documents-dotnet
Azure AI Search SDK for .NET (Azure.Search.Documents). Use for building search applications with full-text, vector, semantic, and hybrid search. Covers SearchClient (queries, document CRUD), SearchIndexClient (index management), and SearchIndexerClient (indexers, skillsets). Triggers: "Azure Search .NET"…
similarity-search-patterns
Implement efficient similarity search with vector databases. Use when building semantic search, implementing nearest neighbor queries, or optimizing retrieval performance.