green-pt/honey-for-devs/honey-hive

Claude Code and agent skill by green-pt/honey-for-devs

Trust tier 2
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Checking that the SKILL.md file is still in the repository.

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.

Install

npx vskill@latest install green-pt/honey-for-devs/honey-hive

SKILL.md

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

AgentDoesReturns
hive-scoutlocate symbols / callers / configs / patternscompact id-keyed JSON map
hive-reviewerreview diff/files for bugs + over-engineering + verbositycolumnar 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.

Security

  • Security scanPassed, 93 of 100Pattern checks on the skill's files.
  • Model reviewNot runA model review is scheduled for the next rescan.

Quality evaluation

  • Task evaluationNot runNo evaluation yet. Run one in Skill Studio.

Source

Popularity

Repository stars
303
Repository forks
17
7-day trend score
29

Stars and forks are for the whole GitHub repository, not this skill alone. Not refreshed yet.

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