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.
npx skills add natea/fitfinder --skill reasoningbank-intelligencegit clone --depth 1 https://github.com/natea/fitfinderWrote 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/natea/fitfinder/reasoningbank-intelligence)<a href="https://agentmods.dev/skills/natea/fitfinder/reasoningbank-intelligence"><img src="https://agentmods.dev/badge/skills/natea/fitfinder/reasoningbank-intelligence/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.
<a href="https://agentmods.dev/skills/natea/fitfinder/reasoningbank-intelligence"><img src="https://agentmods.dev/badge/skills/natea/fitfinder/reasoningbank-intelligence.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00040 | $0.01126 |
| Opus 5 | $0.00020 | $0.00563 |
| Sonnet 5 | $0.00008 | $0.00225 |
| Haiku 4.5 | $0.00004 | $0.00113 |
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 6d 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
91% identical to ReasoningBank Intelligence — 4 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 — 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']
}
});
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.
- 6d ago First seen · 202 lines · 40 tokens per session scan A b62b18984d12
ReasoningBank Intelligence is a skill published in the GitHub repository natea/fitfinder (4 stars, last pushed 10mo ago), licensed MIT. It adds 40 tokens to every session and 1,126 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 4 lines, and is treated as a copy.
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