/honey-hive
Decide when to delegate to Honey's read-only subagents (hive-scout, hive-reviewer) instead of working inline, so the expensive tokens — large file reads and review passes injected back into the orchestrator's context — come back compressed (Honey Lever 3). Use when a task is
$ npx -y skills add Green-PT/honey-for-devs --skill honey-hive --agent claude-codeHow 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-hive
Context preview
The summary Claude sees to decide when to auto-load this skill.
Decide when to delegate to Honey's read-only subagents (hive-scout, hive-reviewer) instead of working inline, so the expensive tokens — large file reads and review passes injected back into the orchestrator's context — come back compressed (Honey Lever 3). Use when a task is
SKILL.md
honey-hive.SKILL.mdname: honey-hive
description: >-
Decide when to delegate to Honey's read-only subagents (hive-scout,
hive-reviewer) instead of working inline, so the expensive tokens — large
file reads and review passes injected back into the orchestrator's context —
come back compressed (Honey Lever 3). Use when a task is search-heavy or
review-heavy, spans many files, or you want to keep the orchestrator's context
small. Not for trivial one-file work.
license: MIT
Honey Hive
Delegate the token-heavy reading; keep the thinking. A subagent's return is injected back into your context — Honey's hive returns it as a compressed Lever-3 handoff, so the most expensive tokens in an agentic session shrink ~25–55% with no loss the orchestrator can use.
When to delegate (any one holds)
- **Search-heavy** — "where is X / who calls Y / find all Z" across many files → `hive-scout`.
- **Review-heavy** — review a diff or file set for bugs and bloat → `hive-reviewer`.
- **Context-preserving** — the read would dump many files into your context but you need only the conclusions.
- **Parallel** — independent locate/review jobs that can run at once.
When NOT to (work inline)
- One known file, a trivial edit, or content you already have in context.
- Dispatch + return overhead would exceed just reading it yourself (a file or two).
- You need the full file body, not a map.
The crew is read-only by design — they locate and review; you decide and edit.
The crew
| Agent | Does | Returns | |-------|------|---------| | `hive-scout` | locate symbols / callers / configs / patterns | compact id-keyed JSON map | | `hive-reviewer` | review diff/files for bugs + over-engineering + verbosity | columnar id-keyed JSON findings |
Reading a hive return (Lever 3, in reverse)
Every return is compact/columnar JSON, records addressed by a stable `id`, with an `n` count. Read it as data:
- **Address by `id`**, never "the 3rd finding" — ordinal lookup misparses, frontier models included.
- **Aggregate in code** — to count or filter, do it programmatically; don't eyeball rows.
- **Check `n`** against the rows you received — a dense misparse is silent.
- **Safety carve-out** — auth / money / migration / delete findings come back explicit, not slugged. Treat them verbatim.
ESON is opt-in: ask for it only on a high-volume, cached review pipe you own end-to-end.
Read more
name: honey-hive description: >- Decide when to delegate to Honey's read-only subagents (hive-scout, hive-reviewer) instead of working inline, so the expensive tokens — large file reads and review passes injected back into the orchestrator's context — come back compressed (Honey Lever 3). Use when a task is search-heavy or review-heavy, spans many files, or you want to keep the orchestrator's context small. Not for trivial one-file work. license: MIT
Honey Hive
Delegate the token-heavy reading; keep the thinking. A subagent's return is injected back into your context — Honey's hive returns it as a compressed Lever-3 handoff, so the most expensive tokens in an agentic session shrink ~25–55% with no loss the orchestrator can use.
When to delegate (any one holds)
- **Search-heavy** — "where is X / who calls Y / find all Z" across many files → `hive-scout`.
- **Review-heavy** — review a diff or file set for bugs and bloat → `hive-reviewer`.
- **Context-preserving** — the read would dump many files into your context but you need only the conclusions.
- **Parallel** — independent locate/review jobs that can run at once.
When NOT to (work inline)
- One known file, a trivial edit, or content you already have in context.
- Dispatch + return overhead would exceed just reading it yourself (a file or two).
- You need the full file body, not a map.
The crew is read-only by design — they locate and review; you decide and edit.
The crew
| Agent | Does | Returns | |-------|------|---------| | `hive-scout` | locate symbols / callers / configs / patterns | compact id-keyed JSON map | | `hive-reviewer` | review diff/files for bugs + over-engineering + verbosity | columnar id-keyed JSON findings |
Reading a hive return (Lever 3, in reverse)
Every return is compact/columnar JSON, records addressed by a stable `id`, with an `n` count. Read it as data:
- **Address by `id`**, never "the 3rd finding" — ordinal lookup misparses, frontier models included.
- **Aggregate in code** — to count or filter, do it programmatically; don't eyeball rows.
- **Check `n`** against the rows you received — a dense misparse is silent.
- **Safety carve-out** — auth / money / migration / delete findings come back explicit, not slugged. Treat them verbatim.
ESON is opt-in: ask for it only on a high-volume, cached review pipe you own end-to-end.
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.
Repo: Green-PT/honey-for-devs
Other skills on honey.
- /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
Open skill - /honey-chat
Honey for plain Claude — the terse-prose core with no agent-harness features. Paste into a claude.ai Project's custom instructions, a Style, or an API system prompt. Strips filler, hedging, and pleasantries from every response while keeping facts, names, numbers, steps, and code
Open skill - /honey-compress
Rewrite a memory or context file (CLAUDE.md, AGENTS.md, a todo or notes file) into Honey-terse form so it costs fewer input tokens every session, without losing meaning. Backs up the original first. Use when asked to shrink or compress context files, trim CLAUDE.md, or cut
Open skill - /honey-debt
Harvest every `honey:` comment in the codebase into a debt ledger, so the deliberate shortcuts Lever 1 leaves behind get tracked instead of rotting into "later means never". Flags markers with no revisit trigger — those are the ones that go permanent silently. Use when the user
Open skill - /honey-design
Same pixels, fewer tokens — for user-facing deliverables where visual polish is the spec. Use when building or editing a landing page, marketing site, hero, pricing/feature section, dashboard, or any HTML/CSS UI component. Keeps the full rendered design (layout depth, hierarchy,
Open skill - /honey-eco
Report this session's output tokens and CO₂ by running the repo's committed EcoLogits port, plus the modelled CO₂/$ saved vs a no-Honey baseline — always labelled with the bench stamp it came from. Use when asked how much Honey saved, the session's carbon/token footprint, or to
Open skill

