Honey Superpowers logo

Honey Superpowers

Organization
Green-PT
honey-superpowers

Stack Honey onto Superpowers-style workflows (subagent-driven-development, dispatching-parallel-agents, executing-plans) and any orchestration that dispatches fresh subagents. Dispatched subagents run in isolated context and do NOT inherit the session's Honey hook, so they emit full-fat code and verbose reports. Use when you are about to dispatch implementer/reviewer/fixer/parallel subagents: it gives the exact Honey directive to paste into each dispatch prompt, with a worker variant and a reviewer variant, so the levers apply inside the subagents too — where the multiplied cost actually is.

Overview

PublisherGreen-PT
Repositoryhoney-for-devs
Skill namehoney-superpowers
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 Superpowers 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-superpowers
  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-superpowers .claude/skills/honey-superpowers
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Honey Superpowers 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 Superpowers 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 Superpowers 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 × Superpowers

Honey is turned on by the session's SessionStart hook — which lives in your (the orchestrator's) context. Superpowers dispatches fresh subagents with isolated context; they never inherit the hook. So every implementer, reviewer, and fixer runs Honey-off: full-fat code (Lever 1 dark) and verbose reports (Lever 2 dark) — multiplied per dispatch. That's the leak. Close it by pasting the directive into the dispatch prompt itself.

The two skills already pull cost the same way Honey does — cheap model per task, file handoffs over pasted text, one-line narration. Don't fight them; stack on top. Honey adds what they don't: less code in the worker, terser reports both ways.

When it fires

Any time you are about to dispatch a subagent under a Superpowers-style workflow:

  • subagent-driven-development — implementer / task-reviewer / fixer per task
  • dispatching-parallel-agents — one agent per independent domain
  • executing-plans — parallel-session execution
  • any orchestrator → worker handoff with isolated context

What to inject

On Claude Code with the Honey plugin, skip the paste: the plugin's SubagentStart hook (hooks/honey-subagent.js) injects the matching directive into every spawned subagent automatically (reviewer-shaped agent types get the reviewer variant). The blocks below are for other harnesses, or when the hook isn't installed.

Paste the matching block into the dispatch prompt body — inline text, not a Skill call (invoking burns a round-trip; the subagent only needs the rules).

Worker / implementer / fixer / parallel-domain agent:

Apply Honey: write the minimum code that needs to exist — YAGNI, stdlib/native
before custom; no speculative params, branches, or single-caller abstractions.
Never cut validation, error handling, auth, or anything the task asked for.
Keep code, identifiers, paths, and the brief's exact spec values verbatim.
Report terse: status, commits, one-line test summary, concerns. No narration.

Reviewer:

Report findings tersely: id · severity · file:line · one-line fix. Don't narrate
or restate the diff. Honey governs your prose only — never your verdict or
severity. Flag everything you normally would; raise false positives for the
controller to adjudicate. Do not suppress or downgrade a finding to save words.

Why the reviewer variant is different

Lever 1 (less code) and Lever 2 (less prose) compress what a worker emits. A reviewer emits judgments — compress its prose, never its verdict. This matches Superpowers' hard rule ("never tell a reviewer what not to flag") and Honey's own safety carve-out (keep exact anything asked for; auth/money/migrations/deletes stay explicit). A terser review is fine; a leniter review is a regression.

Carve-outs (inherit Honey's)

Never elide, in any variant: input validation at trust boundaries, error handling that prevents data loss, auth/escaping/secrets, accessibility basics, and anything the task or plan explicitly mandated. Spec values (numbers, magic strings, signatures, test cases) stay verbatim — that's Lever 2's "keep exact," and it's also what the brief is the single source of truth for.

What Honey can't shrink here

The skill prompts Superpowers loads into context are input tokens you've already paid for — Honey can't out-compress them. They're lazy-loaded, so only triggered skills cost anything. The big realized savings are the cheap-model-per-task rule (Superpowers' own — don't skip it) plus Honey inside every dispatched subagent.

Frequently asked questions

What does the Honey Superpowers AI skill do?

Stack Honey onto Superpowers-style workflows (subagent-driven-development, dispatching-parallel-agents, executing-plans) and any orchestration that dispatches fresh subagents. Dispatched subagents run in isolated context and do NOT inherit the session's Honey hook, so they emit full-fat code and verbose reports. Use when you are about to dispatch implementer/reviewer/fixer/parallel subagents: it gives the exact Honey directive to paste into each dispatch prompt, with a worker variant and a reviewer variant, so the levers apply inside the subagents too — where the multiplied cost actually is.

Why use Honey Superpowers on TypingMind?

Because you install it once and use it with any model. Honey Superpowers 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 Superpowers 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-superpowers. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Honey Superpowers?

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 Superpowers?

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

Is the Honey Superpowers 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇