triple-loop-learning

triple-loop-learning is a skill for Claude Code, Codex from richfrem/agent-plugins-skills. It costs 78 tokens per session (866 once invoked), scanned A, original, MIT.

A pattern for improving an agent system through repeated cycles of planning, execution, evaluation, and strategy changes across sessions.

In plain words
What is it for?
Use it to design long-running improvement loops that generate hypotheses, test strategies, analyze scores, and accept or reject changes.
Why use it?
It provides a structured way to learn from measured results instead of changing agent instructions without evidence.

Skill for Claude CodeCodex

Part of the agent-loops plugin — 7 skills, 1 agent shipped together

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/richfrem/agent-plugins-skills/triple-loop-learning
Any agent
npx skills add richfrem/agent-plugins-skills --skill triple-loop-learning
Clone the repo
git clone --depth 1 https://github.com/richfrem/agent-plugins-skills

Made for: Claude Code, Codex.

Or install agent-loops, the plugin that ships this one along with the rest of its 7 skills, 1 agent.

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 triple-loop-learning

README.md
[![agentmods](https://agentmods.dev/badge/skills/richfrem/agent-plugins-skills/triple-loop-learning.svg)](https://agentmods.dev/skills/richfrem/agent-plugins-skills/triple-loop-learning)
Your own site
<a href="https://agentmods.dev/skills/richfrem/agent-plugins-skills/triple-loop-learning"><img src="https://agentmods.dev/badge/skills/richfrem/agent-plugins-skills/triple-loop-learning.svg" alt="Measured on agentmods" height="20"></a>
Per session 78 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 866 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found 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 $0.00078 $0.00866
Opus 5 $0.00039 $0.00433
Sonnet 5 $0.00016 $0.00173
Haiku 4.5 $0.00008 $0.00087

Measured today against content hash f9f681a94ba2, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

triple-loop-learning 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 today.

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.

plugins/agent-orchestration/skills/triple-loop-learning/SKILL.md · 83 lines

How it starts

The opening of the file, as written. The whole thing — 83 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Dependencies

This skill requires Python 3.8+ and standard library only.

Evaluation gate: NOT included in this primitive. The calling system (e.g., agent-agentic-os os-improvement-loop) is responsible for wrapping this skill with an eval gate and experiment log.


Triple-Loop Learning (Meta-Learning System)

This skill defines the orchestration pattern for the Triple-Loop Architecture. Pattern 5 is a robust, autonomous feedback loop where an independent Meta-Learning Orchestrator governs a long-horizon pipeline of execution, planning, and tactical problem-solving.

This architecture is entirely framework-agnostic. While originally developed for agent-agentic-os, it models the core loop defined by Meta-Harness research where autonomous systems evolve their own operating instructions based strictly on headless evaluators.

Architecture Overview

flowchart TD
    subgraph Outer["Outer Loop (Meta-Learning & Orchestration)"]
        Hypothesize[Hypothesis Generation] --> StrategyBridge[Strategy Packet]
        Report --> EvalBridge[Score Analysis]
        EvalBridge --> Conclude[Accept / Reject Hypothesis]
    end

    subgraph Mid["Strategic Planner (Dual-Loop Integration)"]
        Plan[Define Sub-tasks] --> TacticalBridge[Handoff Packet]
        Result[Aggregate Results] --> Report[Generate Report]
    end

    subgraph Inner["Tactical Executor (Single-Loop Integration)"]
        Execute[Code Mutation] --> Test[Headless Evaluation]
        Test --> ResultBridge[Pass/Fail Signal]
    end

    StrategyBridge --> Plan
    TacticalBridge --> Execute
    ResultBridge --> Result

The Workflow Protocol

Step 1: Friction Aggregation (Outer Loop)

  1. The Orchestrator constantly ingests execution logs from existing operations. Look for repeated uncertainties, API errors, test failures, or syntax flaws.
  2. Group the friction into clustered tasks.

Step 2: Hypothesis Generation (Outer Loop)

  1. Define a singular thesis: "If we change instruction X, the accuracy score on benchmark Y will improve by N."
  2. Write a rigid Strategy Packet for the Mid-level Planner.

Read the full file on GitHub · 83 lines

Files

What ships with it

3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

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. today Changed f9f681a94ba2
  2. 4d ago First seen · 83 lines · 78 tokens per session scan A 0368493e7658

Subscribe to this mod's changes

triple-loop-learning is a skill published in the GitHub repository richfrem/agent-plugins-skills (6 stars, last pushed today), licensed MIT. It adds 78 tokens to every session and 866 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

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