Individual skill
subagent-brief
Use BEFORE invoking the Task or Agent tool to spawn a subagent. Anthropic does NOT share prefix cache across subagents — every subagent cold-starts and re-tokenizes its full prompt (system prompt + tool definitions + the context you handed it). Spawning N subagents with full context = N× token cost; a single fan-out can burn an entire Max-plan day. This skill enforces a pre-flight discipline: compress every subagent prompt into a ≤200-word brief before spawning. Triggers when the agent is about to call Task / Agent tool, especially with long files, full repo dumps, or N parallel subagents on similar work.
skills/subagent-brief/SKILL.md
Source commit ebbc355b6db90b445649efba9a58022d049c9ee2
What was checked
- Matches the observed repository HEAD
- Yes
- Scanner rules are current
- No
- Repository scan complete
- No
- Skill scan complete
- No
- Observed behavior
- Not checked yet
- Last checked
- Nov 4, 2026
These are observations at the recorded commit, not a guarantee of safety. Review the source before running it.
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Repository context
- Repository stars (not skill ratings)
- 232
- Repository last updated
- Jun 9, 2026
- License
- MIT
Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.
- gemini-cli-extension Repository topic
- engineering Derived category
Files and excerpt
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SKILL.mdcore · 6,296 bytes
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# Subagent Brief Discipline
Pre-flight checklist for spawning subagents in Claude Code. The single most
expensive mistake in multi-agent workflows is treating subagents as free
threads. They are not. Each subagent is an independent LLM call that
re-tokenizes the entire prompt you hand it.
There is **no prefix sharing across subagents** in Anthropic's serving stack
today. The multi-agent topology is a known optimization opportunity (see
arXiv 2604.25899 *Pythia: Predictability-Driven Agent-Native LLM Serving*,
2026-04) but has not yet shipped in production. Until it does, the burden
of compression falls on the orchestrating agent.
The math: 20 subagents × 50K shared context = ~1M tokens per fan-out. One