cldcde: Skill for Claude Code

.claude/skills/reasoningbank-agentdb/SKILL.md

ReasoningBank with AgentDB is a skill for Claude Code from aegntic/cldcde. It costs 56 tokens per session (2,645 once invoked), scanned A, a copy of ReasoningBank with AgentDB, MIT.

A system for helping AI agents learn from previous decisions and outcomes using AgentDB, a database designed for storing and finding patterns. It records task paths, judges results, and distills useful memories.

In plain words
What is it for?
Use it to track agent experiences, evaluate outcomes, store distilled memories, recognize patterns, and build experience-replay or self-learning agents.
Why use it?
It lets agents reuse experience instead of treating every decision as new. This can improve pattern retrieval and decision-making over repeated tasks.

Skill for Claude Code

Written for Claude Code: installed under .claude/. Also seen: mentions Claude Code.

This is aegntic/cldcde's own configuration. It tells Claude Code how to work on cldcde itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything cldcde configures →

Needs its repository: it runs a file that does not travel with it, so clone the repository first. The line is npx agentdb@latest init ./.agentdb/reasoningbank.db --dimension 1536.

Reuse

Borrowing it

Nothing to install: this file belongs to aegntic/cldcde. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/aegntic/cldcde/main/.claude/skills/reasoningbank-agentdb/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/aegntic/cldcde

Made for: Claude Code.

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 with AgentDB

README.md
[![agentmods](https://agentmods.dev/badge/skills/aegntic/cldcde/reasoningbank-agentdb/github.svg)](https://agentmods.dev/skills/aegntic/cldcde/reasoningbank-agentdb)
Your own site
<a href="https://agentmods.dev/skills/aegntic/cldcde/reasoningbank-agentdb"><img src="https://agentmods.dev/badge/skills/aegntic/cldcde/reasoningbank-agentdb/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.

agentmods 80×15 button for ReasoningBank with AgentDB

Your own site · 80×15
<a href="https://agentmods.dev/skills/aegntic/cldcde/reasoningbank-agentdb"><img src="https://agentmods.dev/badge/skills/aegntic/cldcde/reasoningbank-agentdb.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 56 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,645 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin 100% 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 $0.00056 $0.02645
Opus 5 $0.00028 $0.01323
Sonnet 5 $0.00011 $0.00529
Haiku 4.5 $0.00006 $0.00265

Measured 9d ago against content hash d60a616ad0bb, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

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 9d 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

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.

.claude/skills/reasoningbank-agentdb/SKILL.md · 447 lines

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);

Read the full file on GitHub · 447 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. 9d ago First seen · 447 lines · 56 tokens per session scan A d60a616ad0bb

Subscribe to this mod's changes

ReasoningBank with AgentDB is a skill published in the GitHub repository aegntic/cldcde (11 stars, last pushed 12d 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.

Related

Other skills, from other repositories

learnings

Use for repeated-looking troubleshooting, solved-issue recall, adding durable troubleshooting notes, and maintaining the local LEARNINGS archive. Reads LEARNINGS-SUMMARY.md first, follows referenced archive/source files before applying lessons, and verifies against current system evidence.

Firstp1ck/pi-coding-agent-forge · 54 tokens

hono-core

Hono ultrafast web framework fundamentals - routing, context, handlers, and response patterns for multi-runtime deployment.

bobmatnyc/claude-mpm-skills · 26 tokens

titen-memory

Use the Titen MCP server to recall bounded evidence-grounded context, record verified durable signals, submit feedback, and coordinate checkpoints, leases, or handoffs. Use when work may benefit from prior project memory or when a verified outcome should be preserved for another agent; do not use it to capture raw…

RamaAditya49/titen · 77 tokens

nen-contract-mantle

Hold a task's objective, boundaries, protected state, assumptions, and acceptance tests through long or context-shifting work. Use when the user invokes Ten, Ken, 纏, or 堅; work spans several phases, agents, or context windows; requirements may drift; or completion needs a final contract check.

cambridgetcg/agenttool · 70 tokens

durable-session-state

Persist plans, scope decisions, evidence, and reviewer/critic verdicts to durable files during long or multi-phase tasks so work survives context compaction, session resumes, and handoffs. Use for swarm-mode tasks, before context grows large, when recording approval gates, and when resuming after compaction or a…

ZaxbyHub/opencode-swarm · 70 tokens

tauri-django-react

Agents should invoke this skill for Tauri + Django + React desktop apps, especially backend lifecycle, CORS/auth, frontend integration, mandatory light/dark theming, German/English i18n, build packaging, dual desktop/web deployment, Rust commands, and platform-specific gotchas.

Firstp1ck/pi-coding-agent-forge · 63 tokens