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/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

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honey
22314 skills3 agents3 hooks
Install
$ npx -y skills add Green-PT/honey-for-devs --skill honey-ccr --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/honey-ccr

Context preview

The summary Claude sees to decide when to auto-load this skill.

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

SKILL.md

honey-ccr.SKILL.md
name: honey-ccr
description: >-
  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.
license: MIT

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

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: Claude Code builds affected by anthropics/claude-code#68951 ignore the hook's Bash rewrite, making it inert (a session-start warning tells you when this applies). Manual `some-tool | eson crush` piping is unaffected — prefer it there.

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Ships withhoney

Write less code and say less about it. Honey (I Shrunk the AI) by GreenPT is a cross-tool coding skill that cuts AI coding-agent token usage and LLM API costs — making agents emit less code and less prose without losing correctness.

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