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Honey Loop

Organization
Green-PT
honey-loop

Cost discipline for recurring /loop runs. A loop multiplies per-iteration cost by iteration count, so waste compounds. Encodes cache-aware pacing (avoid the 300s dead zone), event-driven-over-polling, a no-change short-circuit, a compact state handle carried between ticks, and a stop condition. Use when a /loop is running or being set up — especially long-lived or frequently-firing ones.

Overview

PublisherGreen-PT
Repositoryhoney-for-devs
Skill namehoney-loop
Stars
342
Forks
22
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by Green-PT on GitHub. Read the source before you install it.

Installation

Install the Honey Loop AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/Green-PT/honey-for-devs/tree/main/plugins/honey/skills/honey-loop
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Green-PT/honey-for-devs.git /tmp/honey-for-devs
mkdir -p .claude/skills
cp -r /tmp/honey-for-devs/plugins/honey/skills/honey-loop .claude/skills/honey-loop
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Honey Loop in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Honey Loop on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Honey Loop is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Honey Loop

A loop is the one place token waste compounds: cost = per_tick × ticks. The honey levers still apply inside each tick (less code, less prose), but a loop adds two leaks the single-shot levers don't cover — re-paying for context every wake-up, and re-doing work that didn't change. Cut those.

The five levers

  1. Pace to the cache, skip the dead zone. The prompt cache TTL is 5 min.

    • < 270s → context stays warm; right for actively polling external state.
    • ≥ 1200s → one cache miss amortized over a long idle wait; the default for idle ticks (use 1200–1800s).
    • ~300s is the worst choice — pays the cache miss without amortizing it. Never round to "5 minutes." Drop to 270s or commit to 1200s+.
  2. Event-driven over polling. Harness-tracked work (background Bash, Agent, Workflow) re-invokes you automatically on completion. Don't schedule a short tick to poll it — that re-reads full context for nothing. Set a long fallback heartbeat (1200s+) and let the completion notification drive you. Poll only external state the harness can't see (CI, a deploy, a remote queue), at a cadence matched to how fast that state actually changes.

  3. No-change short-circuit. Re-generating output is the bulk of per-tick cost. Check cheaply first (a hash, a timestamp, a git rev-parse, a status field). If nothing changed: emit one line and reschedule. Don't redo the task, don't re-read the world, don't write a report.

  4. Carry a compact state handle, don't re-derive. Keep what the loop has done so far in a small file (.honey-loop/state.json or a <<honey:HASH>> stash via eso stash), and read that each tick — not the full history re-discovered from scratch. Lever 3b: the cheapest input token is the one that never re-enters context.

  5. Define done; then stop. An unbounded loop is unbounded cost. State the exit condition up front (target reached, N empty ticks, deadline). When met, end the loop — omit the wake-up reschedule. "Loop forever" is a bug, not a feature.

Per-tick output: ultra by default

A loop tick is an agent-to-self / agent-to-log message, not a deliverable. Default to ultra: status line only, near-zero prose. One line is enough — tick 4/∞ · no change · next 1800s. Step up to a full report only on the tick where something actually needs the user (state changed, a decision, an error).

Don't cut

  • The stop/exit check — dropping it is how a loop runs all night.
  • Error surfacing — a tick that hit an error reports it; silent failure in a loop hides for hours.
  • The cheap pre-check in lever 3 — skipping it to "just redo it" defeats the point.

Frequently asked questions

What does the Honey Loop AI skill do?

Cost discipline for recurring /loop runs. A loop multiplies per-iteration cost by iteration count, so waste compounds. Encodes cache-aware pacing (avoid the 300s dead zone), event-driven-over-polling, a no-change short-circuit, a compact state handle carried between ticks, and a stop condition. Use when a /loop is running or being set up — especially long-lived or frequently-firing ones.

Why use Honey Loop on TypingMind?

Because you install it once and use it with any model. Honey Loop is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Honey Loop in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Green-PT/honey-for-devs/tree/main/plugins/honey/skills/honey-loop. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Honey Loop?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Honey Loop?

As many as you like. As long as a model supports skills, you can use Honey Loop with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Honey Loop AI skill free?

Yes. It is published on GitHub by Green-PT under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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