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 agentmods add skills/richfrem/agent-plugins-skills/triple-loop-learningnpx skills add richfrem/agent-plugins-skills --skill triple-loop-learninggit clone --depth 1 https://github.com/richfrem/agent-plugins-skillsWrote 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/richfrem/agent-plugins-skills/triple-loop-learning)<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>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 | $0.00078 | $0.00866 |
| Opus 5 | $0.00039 | $0.00433 |
| Sonnet 5 | $0.00016 | $0.00173 |
| Haiku 4.5 | $0.00008 | $0.00087 |
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.
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)
- The Orchestrator constantly ingests execution logs from existing operations. Look for repeated uncertainties, API errors, test failures, or syntax flaws.
- Group the friction into clustered tasks.
Step 2: Hypothesis Generation (Outer Loop)
- Define a singular thesis: "If we change instruction X, the accuracy score on benchmark Y will improve by N."
- Write a rigid Strategy Packet for the Mid-level Planner.
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.
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.
- today Changed f9f681a94ba2
- 4d ago First seen · 83 lines · 78 tokens per session scan A 0368493e7658
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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