Honey Ccr logo

Honey Ccr

Organization
Green-PT
honey-ccr

Compress-Cache-Retrieve for huge, repetitive array tool output (logs, scan results, time series, event streams) before it enters context. Keeps an informative sample — endpoints, anomalies/change-points, head/tail — drops the redundant rest to a local cache, and leaves a retrievable hash. Use when a tool returns a long uniform JSON array you must read but mostly skim, and the full set is one command away if needed. Lossy-but-recoverable.

Overview

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

Use it in TypingMind

Enable Honey Ccr 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 Ccr 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 Ccr 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 CCR

The cheapest token is the one not sent. A 500-row log read for one error line costs 500 rows of context. CCR keeps the rows that carry signal and caches the rest — recoverable by hash, so nothing is lost.

Borrowed from headroom's SmartCrusher. Lossy by design — only for data you skim, never for payloads where every row matters (e.g. a code-review handoff where the implementer needs all findings; use eson encode losslessly there).

When to use

  • A tool returns a uniform JSON array of many records (≥ ~5 items, redundant).
  • You need to read it but mostly skim — logs, search hits, metrics, events.
  • The full data stays reachable, so dropping the boring middle is safe.

Don't use

  • Small arrays (passes through untouched anyway) or non-array data.
  • Any array where every element is load-bearing — encode it, don't crush it.

How

sh
tool-that-spews-json | eson crush          # → sampled view + sentinel; originals cached
eson retrieve <hash>                        # → the full original array, verbatim

crush prints the kept sample plus a sentinel {"_ccr":"<<ccr:HASH N_rows_offloaded>>"}. When you need a dropped row, read the hash from the sentinel and run eson retrieve <hash>.

  • Cache dir: .honey-ccr/ (override with HONEY_CCR_DIR).
  • Below the size gate, crush returns the array unchanged with no sentinel — safe to pipe anything.

Always-on hook (entry-time GC)

While Honey is active, a registered PostToolUse hook applies this automatically to Bash output — no opt-in per call. Big uniform JSON arrays (≥20 items, ≥2k chars) are crushed; repetitive text (≥25 lines) gets consecutive repeats collapsed to ⟨×N⟩. The original is always stashed first, and the rewritten result ends with the exact eson retrieve <hash> command that restores it. /honey off disables the hook; history already in context is immutable — this fires at entry, the only place a hook can act.

Caveat: the hook needs Claude Code 2.1.121+; older builds can't rewrite Bash output (a session-start warning tells you). Manual some-tool | eson crush piping works on any version — prefer it there.

Frequently asked questions

What does the Honey Ccr AI skill do?

Compress-Cache-Retrieve for huge, repetitive array tool output (logs, scan results, time series, event streams) before it enters context. Keeps an informative sample — endpoints, anomalies/change-points, head/tail — drops the redundant rest to a local cache, and leaves a retrievable hash. Use when a tool returns a long uniform JSON array you must read but mostly skim, and the full set is one command away if needed. Lossy-but-recoverable.

Why use Honey Ccr on TypingMind?

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

Which AI models can use Honey Ccr?

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 Ccr?

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

Is the Honey Ccr 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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