loop-designer

A guide for designing AI agents that run repeated work on their own, check results against a goal, use helpers, and record lessons for later runs.

In plain words
What is it for?
Use it for scheduled unattended agents, self-checking workflows, remembered state, and fail-safe automation.
Why use it?
It provides a structured way to automate recurring workflows and avoid agents stopping after their first improvements.

Skill for Claude CodeCodex

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/groktopus/loop-designer/loop-designer
Any agent
npx skills add groktopus/loop-designer --skill loop-designer
Clone the repo
git clone --depth 1 https://github.com/groktopus/loop-designer

Made for: Claude Code, Codex.

Per session 14 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 5,019 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 $0.00014 $0.05019
Opus 5 $0.00007 $0.02509
Sonnet 5 $0.00003 $0.01004
Haiku 4.5 $0.00001 $0.00502

Measured 2d ago against content hash ee0dc40f9d2e, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

loop-designer 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 2d 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.

SKILL.md · 382 lines

How it starts

The opening of the file, as written. The whole thing — 382 lines — stays where its author put it; the contents beside it link to each section on GitHub.

From Prompter to Loop Designer

A loop designer builds a system that prompts itself -- runs on a timer, checks its own work against a goal, spawns helpers when needed, and writes down what it learned so the next run starts smarter. This skill distills Alex (@de1lymoon)'s 10-step roadmap into concrete Hermes Agent invocations.

This is NOT about better prompting. It does NOT cover model selection, fine-tuning, or prompt engineering. The model stays the same throughout -- what improves is the Hermes infrastructure wrapped around it.

When to Use

  • You are building an agent that should run unattended on a schedule
  • You have a working manual workflow and want to automate it
  • You want the system to remember past runs and improve over time
  • You need to build a fail-safe autonomous loop
  • You are debugging why your automated agent stops improving after initial gains

When Not to Use

  • The task takes under 5 minutes and runs less than weekly. The setup cost of a dedicated profile, goal_judge wiring, state log, and cron job exceeds the manual effort for months or years. A simple cron job or calendar reminder is a better fit.
  • The task requires irreversible production writes without a human gate. Deployments, database migrations, and user-facing content changes must have a human approval step. The loop can prepare and verify, but should not execute alone.
  • The task's environment changes faster than the loop's value compounds. If the target (dashboard, API, website) changes layout or contract weekly, the loop spends more time debugging failures than the manual task would take. The compounding benefit of memory and skills only helps when the environment is stable enough for lessons to accumulate.
  • You can't define a clear "done" condition. Without a goal_judge verdict or measurable acceptance criteria, the loop has no way to know when to stop. Loops without a stop condition drift into token-wasting oscillation.
  • You're building it to learn loop design, not to solve a problem. That's valid — build a throwaway loop on a synthetic task first. The Verification section spells out what graduating looks like.

Read the full file on GitHub · 382 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. 2d ago First seen · 382 lines · 14 tokens per session scan A ee0dc40f9d2e

Subscribe to this mod's changes

loop-designer is a skill published in the GitHub repository groktopus/loop-designer (5 stars, last pushed 1mo ago), licensed MIT. It adds 14 tokens to every session and 5,019 once invoked, about $0.0001 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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