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

name

description

claude-subagents

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 via the Claude CLI

claude is on PATH in your container, and 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 - not a hack.

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 spawn command

Put the task recipe in a file and pass it as a path, with a short -p trigger:

claude --name <memorable> --model <cheaper-than-you> --dangerously-skip-permissions \
       --append-system-prompt-file task-prompt.md \
       -p "Carry out the task described in your instructions."

• --name <memorable> - names the session so you can --resume it by name for follow-ups.
• --model <cheaper-than-you> - think about which model the task actually needs; don't spend a bigger model's tokens than your own on mechanical work a cheaper one handles fine. Never use a bigger model than yourself for a sub-agent - if the task needs that much capability, it's not the "mechanical batch" case this skill is for.
• --dangerously-skip-permissions - required in headless (-p) mode. Without it, tool calls that would normally prompt for approval are auto-denied non-interactively, so the sub-agent silently does nothing. Acceptable here because the sub-instance runs inside your already-sandboxed environment, with your already-scoped credentials, on a bounded task - don't reach for it when the task could touch things outside its intended blast radius.
• --append-system-prompt-file <path> - read the recipe from a file rather than inlining it in -p (which is bounded by ARG_MAX - a long recipe passed as -p "$(cat …)" can blow past it and the exec fails). Keeps Claude Code's default scaffolding and adds your recipe on top.
• -p "<trigger>" - headless print mode. Keep this short: just tell the sub-agent to act on its file-supplied instructions.

Run it in the background, don't babysit

Wrap the launch in a background bash task and end your turn - your bash-task runner already captures stdout/stderr and hands you a completion pointer, and ending the turn keeps you reachable for other work while the sub-agent grinds.

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 in the background and don't babysit any single one — same as the one-subagent case, just N background bash tasks 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

claude --resume <name> -p "Now run the same procedure over the second batch." 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. Spawn 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)

• No --dangerously-skip-permissions → headless tool calls denied, the sub-agent burns a run doing nothing.
• 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.
• Spawning fresh instead of --resume 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.