hyperhive/claude-plugins/plugins/base/skills/claude-subagents/SKILL.md

---
name: claude-subagents
description: Spin up a short-lived headless `claude` sub-instance to grind through a well-scoped, mechanical batch (bulk relabeling, a repetitive find/replace, a mechanical migration) instead of burning your own context on it inline. Use this when a task has a clear, describable recipe and is either big enough to eat your context or long enough that you'd rather not babysit it. Not for judgement-heavy work, anything needing operator back-and-forth, or a task whose blast radius you can't bound up front.
---

# Ephemeral Sub-Agents

A sub-instance you spawn inherits the same filesystem and credentials you
have. This is a first-class tool for offloading a bounded, mechanical
batch.

## When to use it

- **Yes:** mechanical batches with a clear recipe (relabel N issues,
  rewrite a call-site pattern, migrate a config field), especially when
  the batch would eat your context or take long enough that you'd
  rather not babysit it turn-by-turn.
- **No:** judgement-heavy work, anything needing back-and-forth with a
  human, or a task whose blast radius you can't bound up front.

## The `subagent` MCP tools

Your container's `subagent` MCP server (`hive-subagent-daemon`) runs this
skill's recipe for you:

```
start(name, prompt_file, model?, effort?, trigger?)
continue(name, prompt, model?, effort?)
status(name)
interrupt(name)
```

`start` and `continue` return once the turn is under way, not once it
finishes — a completion lands as a todo (`get_loose_ends`), same as any
other producer. `continue` takes the extra moment to confirm the resume
actually attached, so a `continue` naming a session that isn't there
fails the tool call outright instead of looking like it worked; the error
says which directory it searched, which is usually the fix (pass `dir`).
`dir` defaults to your work dir, `/agents/<you>/work`, which has no
`AGENTS.md` above it, so a subagent started there does not load yours
(until that directory exists, the daemon's cwd is used).
Use `status` for a
zero-cost "is it still going" check — for a running subagent it also
reports how long since that turn last produced output, so a few seconds
means it's working and an age climbing into the minutes means it's
wedged and worth an `interrupt`. Reach for `continue` only once you
actually have a new instruction for it, since that spends a turn.
`interrupt` genuinely stops a running turn, with SIGINT so claude stops
the commands it started; `status` says `stopping` until claude has exited.

Model choice, prompt hygiene, splitting big batches, verify-then-report —
everything else in this skill — applies exactly the same whether you're
calling the tool or thinking through the recipe by hand.

## Effort

An omitted `effort` defaults to `medium` here — cheaper than claude's own
model default (`high` on most models), a deliberate cost-conscious choice
for subagent work specifically, same spirit as "cheaper-than-you" model
choice above. Raise it explicitly when the task is complex enough to
actually need deeper reasoning, not as a reflex.

Anthropic's own levels (per current Claude Code docs — names/availability
are model-dependent, check before relying on an exact list): `low`,
`medium`, `high`, `xhigh`, `max`. Each trades token spend for capability:

- **`low`** — short, scoped, latency-sensitive tasks that aren't
  intelligence-sensitive.
- **`medium`** — cost-sensitive work that can trade off some intelligence.
  This skill's default for subagent work.
- **`high`** — balances token usage and intelligence; Anthropic's own
  recommended default for most _interactive_ coding tasks (not what this
  skill defaults subagents to — see above).
- **`xhigh`** — deeper reasoning at higher token spend.
- **`max`** — demanding tasks only; diminishing returns and overthinking
  are a real risk, per Anthropic's own guidance — don't reach for it as a
  default.

Anthropic's guidance: treat effort as a general preference, not a
task-by-task dial — raise it if a subagent keeps skipping files, not
running tests, or not double-checking its own work; lower it for routine
work where quality hasn't suffered. Changing effort between turns on the
_same_ session invalidates prompt caching, so pick a level for the whole
session rather than flipping it turn-to-turn.

## Prompt hygiene - this is where batches succeed or fail

- **Concrete constants, not "figure it out":** exact ids, field names,
  option values, endpoints.
- **Critical ordering rules, spelled out** - the sub-agent won't infer
  invariants you don't state.
- **Explicit scope + exemptions**, plus a fallback rule for ambiguous
  cases: "leave it as-is where genuinely unclear; do not guess."
- **Ask for a report file** - per-item results + anything skipped and
  why, so you can verify without re-deriving.
- **Tune on ONE item first**, eyeball the result, fix the prompt, _then_
  turn it loose on the full batch. A prompt bug replicated across 100
  items is 100 cleanups.

## Split a big independent batch across parallel subagents

One subagent is a sequential worker — a 50-item batch takes roughly 50
items' worth of wall-clock even though nothing about the recipe forces
serialization. If the items are independent (each one touches its own
file/issue/row, nothing depends on another item's result) and the batch
is big enough that wall-clock matters, chunk it across N subagents
running at once instead of handing the whole thing to one.

- **Split by natural boundaries**, not an arbitrary item count — one
  subagent per file, per directory, per module, per label, whatever
  grouping keeps each worker's slice self-contained. A worker that has
  to coordinate with another worker mid-task isn't actually independent
  work; re-scope the split until it is.
- **Isolate each worker's writes.** For a git-based batch, give each
  worker its own `git worktree` (own working directory, own branch, same
  underlying repo — cheap, no full reclone) so N workers editing
  different files never race on the same working tree or index. For a
  non-git batch (issue relabeling, API calls), independent items don't
  need filesystem isolation at all — just launch N in parallel.
- **Launch all N and don't babysit any single one** — same as the
  one-subagent case, just N `start` calls instead of one. Check on them
  as a batch, not by polling each individually in a loop.
- **Mind the container's memory cap before picking N.** Your whole
  container shares one `MemoryMax` (a few GB by default) with every
  subagent you spawn _and_ your own process. A `claude` process plus its
  MCP servers can hold several hundred MB to ~1GB depending on the task;
  spawning a dozen at once on a small container doesn't just slow
  things down, it can OOM the whole container — taking your own
  in-flight turn down with it, not just the subagents. Rule of thumb:
  **2-4 concurrent workers** on a default-sized container; check
  `free -h` (or ask whoever owns the container's config for its
  `MemoryMax`) before going higher, and chunk a bigger batch into
  successive waves of that size rather than firing everything at once.
- **You own the merge.** Once all N report done, review + verify each
  worker's slice (same "don't trust the self-report blind" rule as
  below), then combine — for a git-based split, that's you merging N
  branches (or cherry-picking) into one, not each worker pushing/PRing
  its own slice.

Skip this for a batch small enough to finish in a couple minutes single-
threaded — the coordination overhead isn't worth it below that size.

## Resume for follow-ups

`continue` reuses the same session, so the sub-agent keeps every constant
and gotcha it already discovered instead of re-learning the surface from
a cold prompt. Reach for `start` under a new name only for a genuinely
unrelated task.

## Verify, then report

On completion: read the report file, spot-check a handful of results
yourself (don't trust the self-report blind), then summarize. Surface
anything the sub-agent left ambiguous or exempted for a human call.

## Pitfalls (all observed in practice)

- Using a model bigger than yourself → paying premium cost for
  mechanical work that didn't need it.
- Skipping the one-item tuning pass → a systematic mistake smeared
  across the whole batch.
- `start`-ing fresh instead of `continue`-ing for a follow-up → throws
  away all the context the first pass earned.
- Baking an unverified assumption into the recipe - if a quick check
  suggests something "isn't possible" or "doesn't exist," confirm it
  before writing that conclusion into the prompt; a wrong assumption
  gets replicated across the whole batch.
- Handing a big independent batch to one subagent instead of splitting
  it across several in parallel - a 50-item sequential run burns wall-
  clock the split-by-worktree approach above would've avoided for free.