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/spencermarx/open-code-reviewnpx agentmods add skills/spencermarx/open-code-review/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/spencermarx/open-code-review/reasoningbank-agentdb)<a href="https://agentmods.dev/skills/spencermarx/open-code-review/reasoningbank-agentdb"><img src="https://agentmods.dev/badge/skills/spencermarx/open-code-review/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 spencermarx/open-code-review (355 stars, last pushed 1mo ago), licensed Apache-2.0. 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.
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