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reasoningbank-intelligence

9856 stars 1303 forks
63
D

"Implement adaptive learning with ReasoningBank for pattern recognition, strategy optimization, and continuous improvement. Use when building self-learning agents, optimizing workflows, or implementing meta-cognitive systems."

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Third-Party Agent Skill: Review the code before installing. Agent skills execute in your AI assistant's environment and can access your files. Learn more about security

Installation for Agentic Skill

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skilz install ruvnet/claude-flow/reasoningbank-intelligence
skilz install ruvnet/claude-flow/reasoningbank-intelligence --agent opencode
skilz install ruvnet/claude-flow/reasoningbank-intelligence --agent codex
skilz install ruvnet/claude-flow/reasoningbank-intelligence --agent gemini

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Works with 22+ AI coding assistants

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Extract and copy to ~/.claude/skills/ then restart Claude Desktop

1. Clone the repository:
git clone https://github.com/ruvnet/claude-flow
2. Copy the agent skill directory:
cp -r claude-flow/.claude/skills/reasoningbank-intelligence ~/.claude/skills/
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Need detailed installation help? Check our platform-specific guides:

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Agentic Skill Details

Owner
ruvnet (GitHub)
Repository
claude-flow
Stars
9856
Forks
1303
Type
Non-Technical
Meta-Domain
development
Primary Domain
github
Market Score
63
Agentic Skill Grades →

Agent Skill Grade

D
Score: 63/100 Click to see breakdown

Score Breakdown

Spec Compliance
10/15
PDA Architecture
15/30
Ease of Use
17/25
Writing Style
6/10
Utility
14/20
Modifiers: +1

Areas to Improve

  • Name convention violations
  • No Reference Files
  • Missing Table of Contents

Recommendations

  • Focus on improving Pda (currently 15/30)
  • Address 1 high-severity issues first
  • Add trigger phrases to description for discoverability

Graded: 2026-01-05

Developer Feedback

I took a look at your reasoningbank-intelligence skill and wanted to share some thoughts.

Links:

The TL;DR

You're at 63/100, which puts you in D territory. The good news? You've got solid utility (14/20) - the core idea of adaptive pattern recognition for AI agents addresses a real gap. The rough spots are in Progressive Disclosure Architecture (15/30) and Spec Compliance (10/15). This evaluation is based on Anthropic's best practices for agent skills.

What's Working Well

  • Practical utility - The ReasoningBank concept solves a real problem; agents learning from their own experiences is genuinely useful
  • Good trigger phrases - Your metadata picks up on "self-learning," "meta-cognitive," and "adaptive" which helps discoverability
  • Configuration flexibility - Exposing learningRate, threshold, and updateFrequency gives users real control
  • Solid examples - You've got concrete TypeScript code showing how to use this, not just abstract descriptions

The Big One: You're Putting Too Much in One File

Right now, all 202 lines are crammed into SKILL.md. That's verbose and burns tokens unnecessarily. Here's what's holding you back: no references/ directory structure at all.

The fix: Split this into layers:

  • Keep SKILL.md to ~50 lines (just intro + minimal Quick Start)
  • Create references/api-reference.md for all those TypeScript code blocks
  • Create references/patterns.md for the best practices section
  • Create references/troubleshooting.md for edge cases

This alone gets you +8 points. Your file structure should look like:

reasoningbank-intelligence/
├── SKILL.md (just overview + quick start)
├── references/
│   ├── api-reference.md
│ ...

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