ReasoningBank Intelligence

ReasoningBank Intelligence is a skill for Claude Code from ruvnet/ruflo. It costs 40 tokens per session (1,127 once invoked), scanned A, a copy of ReasoningBank Intelligence, MIT.

An adaptive-learning system for AI agents that records experiences, recognizes patterns, recommends strategies, and improves its approach over time. It can persist what it learns for later use.

In plain words
What is it for?
Use it to build self-learning agents, optimize workflows, recommend strategies, and add higher-level reasoning about past decisions.
Why use it?
It helps an agent learn which approaches work best for different tasks and contexts, reducing repeated trial and error.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin. Also seen: installed under .agents/ (shared by several agents); $skill-name invocation.

Part of the claude-flow plugin — 134 skills, 52 commands, 11 agents, 4 hooks shipped together

About the project

Ruflo is an execution and coordination layer for Claude Code and Codex that equips AI coding agents with tools, memory, control loops, sandboxes, and collaboration mechanisms. Developers use it to organize specialized agents into swarms, coordinate workflows, retain knowledge across sessions, and communicate across machines. The catalogue entries are Ruflo’s skills, commands, agents, hooks, and plugin components.

ruvnet/ruflo · 70,734 stars · on GitHub · cognitum.one

Install

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.

agentmods
npx agentmods add skills/ruvnet/ruflo/reasoningbank-intelligence
Any agent
npx skills add ruvnet/ruflo --skill reasoningbank-intelligence
Clone the repo
git clone --depth 1 https://github.com/ruvnet/ruflo

Made for: Claude Code.

Or install claude-flow, the plugin that ships this one along with the rest of its 134 skills, 52 commands, 11 agents, 4 hooks.

Wrote 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.

agentmods badge for ReasoningBank Intelligence

README.md
[![agentmods](https://agentmods.dev/badge/skills/ruvnet/ruflo/reasoningbank-intelligence.svg)](https://agentmods.dev/skills/ruvnet/ruflo/reasoningbank-intelligence)
Your own site
<a href="https://agentmods.dev/skills/ruvnet/ruflo/reasoningbank-intelligence"><img src="https://agentmods.dev/badge/skills/ruvnet/ruflo/reasoningbank-intelligence.svg" alt="Measured on agentmods" height="20"></a>
Per session 40 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,127 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin 91% copy Near-identical to another mod in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00040 $0.01127
Opus 5 $0.00020 $0.00563
Sonnet 5 $0.00008 $0.00225
Haiku 4.5 $0.00004 $0.00113

Measured 2d ago against content hash c8403e9ed276, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-06, from the pricing page.

Security

Grade A, and why

ReasoningBank Intelligence 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.

Origin

This is a copy

91% identical to ReasoningBank Intelligence — 14 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.

.agents/skills/reasoningbank-intelligence/SKILL.md · 202 lines

How it starts

The opening of the file, as written. The whole thing — 202 lines — stays where its author put it; the contents beside it link to each section on GitHub.

ReasoningBank Intelligence

What This Skill Does

Implements ReasoningBank's adaptive learning system for AI agents to learn from experience, recognize patterns, and optimize strategies over time. Enables meta-cognitive capabilities and continuous improvement.

Prerequisites

  • agentic-flow v1.5.11+
  • AgentDB v1.0.4+ (for persistence)
  • Node.js 18+

Quick Start

import { ReasoningBank } from 'agentic-flow$reasoningbank';

// Initialize ReasoningBank
const rb = new ReasoningBank({
  persist: true,
  learningRate: 0.1,
  adapter: 'agentdb' // Use AgentDB for storage
});

// Record task outcome
await rb.recordExperience({
  task: 'code_review',
  approach: 'static_analysis_first',
  outcome: {
    success: true,
    metrics: {
      bugs_found: 5,
      time_taken: 120,
      false_positives: 1
    }
  },
  context: {
    language: 'typescript',
    complexity: 'medium'
  }
});

// Get optimal strategy
const strategy = await rb.recommendStrategy('code_review', {
  language: 'typescript',
  complexity: 'high'
});

Core Features

1. Pattern Recognition

// Learn patterns from data
await rb.learnPattern({
  pattern: 'api_errors_increase_after_deploy',
  triggers: ['deployment', 'traffic_spike'],
  actions: ['rollback', 'scale_up'],
  confidence: 0.85
});

// Match patterns
const matches = await rb.matchPatterns(currentSituation);

2. Strategy Optimization

// Compare strategies
const comparison = await rb.compareStrategies('bug_fixing', [
  'tdd_approach',
  'debug_first',
  'reproduce_then_fix'
]);

// Get best strategy
const best = comparison.strategies[0];
console.log(`Best: ${best.name} (score: ${best.score})`);

3. Continuous Learning

// Enable auto-learning from all tasks
await rb.enableAutoLearning({
  threshold: 0.7,        // Only learn from high-confidence outcomes
  updateFrequency: 100   // Update models every 100 experiences
});

Advanced Usage

Meta-Learning

// Learn about learning
await rb.metaLearn({
  observation: 'parallel_execution_faster_for_independent_tasks',
  confidence: 0.95,
  applicability: {
    task_types: ['batch_processing', 'data_transformation'],
    conditions: ['tasks_independent', 'io_bound']
  }
});

Read the full file on GitHub · 202 lines

Changes

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.

  1. 2d ago First seen · 202 lines · 40 tokens per session scan A c8403e9ed276

Subscribe to this mod's changes

ReasoningBank Intelligence is a skill published in the GitHub repository ruvnet/ruflo (70,734 stars, last pushed today), licensed MIT. It adds 40 tokens to every session and 1,127 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 91% identical to ReasoningBank Intelligence, differing in 14 lines, and is treated as a copy.

Related

Other skills, from other repositories

note

Save learnings and discoveries to NOTES.md for persistence across sessions.

blake-simpson/belmont · 14 tokens

bootstrap

Generate a personalized SOUL.md through a warm, adaptive onboarding conversation. Trigger when the user wants to create, set up, or initialize their AI partner's identity — e.g., "create my SOUL.md", "bootstrap my agent", "set up my AI partner", "define who you are", "let's do onboarding", "personalize this AI", "make…

fullstack455/deer-flow · 0 tokens

mem0-integration

Mem0 memory layer integration for AI agents. Implement persistent, semantic memory for long-term context retention and personalization.

a5c-ai/babysitter · 27 tokens

compare-harnesses

Diff two scaffolded harnesses (ADR-031). Reports manifest meta drift + host list + per-file fingerprint changes (added/removed/changed). Exits 0 IDENTICAL, 1 DRIFT, 2 missing manifest. Use --bundle for the ADR-031 schema-1 JSON envelope.

ruvnet/metaharness · 66 tokens

diag-harness

Kernel-version skew check (ADR-027). Reports manifest surface + manifest kernel + installed kernel + verdict (match/patch-diff/minor-diff/major-diff). Exits 1 on minor/major skew with a copy-pasteable npm install @metaharness/[email protected] next step. Exits 2 if no .harness/manifest.json at path.

ruvnet/metaharness · 85 tokens

repo-genome

7-section readiness scorecard for a LOCAL repo. Reports repo type + agent topology + MCP risk + test confidence + release readiness + recommended harness plan + scorecard. Exit 0 ready, 1 needs-work, 2 blocked. --json for the 6-field scorecard, --bundle for the ADR-031 schema-1 envelope.

ruvnet/metaharness · 73 tokens