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Honey Memory

Organization
Green-PT
honey-memory

Create and maintain one committed per-project memory file (PROJECT.md) so agents stop re-discovering the same facts every cold session. Stores only the stable, expensive-to-rediscover, not-in-the-code context — architecture + why, build/test/run commands, decisions and dead-ends — as human-readable markdown, versioned with the code so it can't silently rot. Use when asked to set up project memory/context, stop agents re-grepping the codebase, scaffold or refresh a PROJECT.md / CLAUDE.md memory section, or cut cold-start rediscovery cost. Prose context only — never code, config, or data.

Overview

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

Use it in TypingMind

Enable Honey Memory 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 Memory 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 Memory 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 Memory

Lever 2 applied to discovery cost. Every cold session re-greps the same things — where auth lives, the build command, why X is shaped Y. One committed file replaces N rediscovery round-trips with a single cached read, on every future session. The cheapest token is the one not re-derived.

The win is per-session and recurring. The risk is staleness — a wrong cached fact costs more than no file. So the file lives in git, next to the code that can invalidate it, and is fixed in the same change that breaks it.

Scaffold (create)

  1. One file per repo: PROJECT.md at the root, committed (not ~/.claude). If the repo already has CLAUDE.md/AGENTS.md, add/refresh a ## Memory section there instead — don't add a second file.
  2. Back up before overwriting an existing file: copy → FILE.original.md. If that backup already exists, stop and ask. Never clobber a restore point.
  3. Write only three kinds of fact (derive by reading the repo):
    • Architecture + conventions an agent can't cheaply grep — where things live, and why (the intent, not the file list).
    • Build / test / run commands, env, and gotchas.
    • Decisions and dead-ends — choices made, paths tried and rejected. This is intent; it is never in the code.
  4. Report what was written and the file path.

Never store

  • Anything derivable from source — code structure, symbol locations, past fixes, git history. Re-deriving is cheaper than maintaining, and never stale.
  • Secrets, tokens, credentials. The file is committed.
  • Transient state — open TODOs, this-week status. That rots fastest.

If a fact is stable + expensive-to-rediscover + not-in-the-code, write it. Everything else, let agents derive on demand — caching it is where these systems quietly lose money.

Update

A markdown file does not update itself. Reliability = discipline, not infra:

  • Same-change rule (primary). When code changes invalidate a fact, fix the fact in the same commit. Review catches drift because the file is in git.
  • On-demand refresh. Re-read the repo and rewrite stale entries; show the diff. Don't blind-append — appending is how these files bloat and rot.
  • Lint before and after a refresh. node "${CLAUDE_PLUGIN_ROOT}/bin/check.js" memory (no model) finds paths the file names that no longer exist, @imports that load nothing, the same rule loaded from two files, and a Claude Code MEMORY.md past the 200 lines / 25KB that load at session start. Fix each finding with the user's yes; never delete memory content unasked. (If CLAUDE_PLUGIN_ROOT is unexpanded, bin/check.js lives at the plugin root, two directories above this file.)
  • Treat a fact as superseded, not accumulated: replace the old line, don't stack a new one beside it.

One file vs many

Start with one file. Split only when it crosses ~150–200 lines or covers clearly separable domains — then go to a thin index + topic files loaded on demand. Below that, an index costs more (the always-loaded tax) than it saves.

Keep terse

Apply Honey Lever 2 to the content — fragments over paragraphs, no narration of what the code already says. A bloated memory file is a per-session input tax. Keep it human-readable markdown: you (and reviewers) hand-edit it, so don't use a wire format — staleness costs more than the tokens a dense format would save.

Boundaries

Reversible — FILE.original.md is the restore path when overwriting. Verify every written fact against the current code before reporting done; an unverified fact is worse than an absent one.

Frequently asked questions

What does the Honey Memory AI skill do?

Create and maintain one committed per-project memory file (PROJECT.md) so agents stop re-discovering the same facts every cold session. Stores only the stable, expensive-to-rediscover, not-in-the-code context — architecture + why, build/test/run commands, decisions and dead-ends — as human-readable markdown, versioned with the code so it can't silently rot. Use when asked to set up project memory/context, stop agents re-grepping the codebase, scaffold or refresh a PROJECT.md / CLAUDE.md memory section, or cut cold-start rediscovery cost. Prose context only — never code, config, or data.

Why use Honey Memory on TypingMind?

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

Which AI models can use Honey Memory?

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 Memory?

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

Is the Honey Memory 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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