Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add ag2ai/ag2-skills --skill ag2-subagent-delegationgit clone --depth 1 https://github.com/ag2ai/ag2-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/ag2ai/ag2-skills/ag2-subagent-delegation)<a href="https://agentmods.dev/skills/ag2ai/ag2-skills/ag2-subagent-delegation"><img src="https://agentmods.dev/badge/skills/ag2ai/ag2-skills/ag2-subagent-delegation/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/ag2ai/ag2-skills/ag2-subagent-delegation"><img src="https://agentmods.dev/badge/skills/ag2ai/ag2-skills/ag2-subagent-delegation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00173 | $0.02248 |
| Opus 5 | $0.00086 | $0.01124 |
| Sonnet 5 | $0.00035 | $0.00450 |
| Haiku 4.5 | $0.00017 | $0.00225 |
Grade A, and why
ag2-subagent-delegation scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 210 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Subagent delegation
When to use
- "Coordinator + specialists" — a parent agent should hand parts of a task to a research agent, math agent, etc.
- "Fan out then collect" — multi-part questions where each part is independent and parallel execution saves wall time.
- "Self-delegation" — one agent breaks complex work into focused sub-tasks for itself.
Two patterns
| Pattern | Reach for it when | API |
|---|---|---|
Auto-injected run_subtask / run_subtasks |
Lightweight self-delegation, dynamic fan-out, parallel sub-questions | tasks=TaskConfig(...) on the parent |
Agent.as_tool() |
Distinct named delegates the LLM should reason about ("call the researcher", "call the writer") | Wrap a child Agent as a tool on the parent |
The two compose — a coordinator can have both.
Pattern 1 — auto-injected run_subtasks
Subtask tools are off by default (tasks=False). Opt in with tasks=TaskConfig(...) and the agent gains:
run_subtask(task: str)— one isolated sub-task agent.run_subtasks(tasks: list[str], parallel: bool = True)— fan out multiple in one tool call (default concurrent).
from ag2 import Agent, TaskConfig
from ag2.config import GeminiConfig
config = GeminiConfig(model="gemini-3-flash-preview")
coordinator = Agent(
"coordinator",
prompt=(
"You answer multi-part questions by dispatching run_subtasks "
"with parallel=True. Use one tool call with every sub-question "
"packed into the 'tasks' list."
),
config=config,
tasks=TaskConfig(), # opt in
)
reply = await coordinator.ask(
"In one run_subtasks call, answer: "
"(a) tallest waterfall, (b) Eiffel Tower year, (c) boiling point of nitrogen."
)
TaskConfig controls how the sub-task agents are built:
@dataclass
class TaskConfig:
config: ModelConfig | None = None # falls back to parent's config
prompt: str = "You are a task agent..."
include_tools: Iterable[str] | None = None # None = inherit all parent tools
exclude_tools: Iterable[str] = ()
extra_tools: Iterable[Callable | Tool] = ()
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 210 lines · 173 tokens per session scan A 7393772b841f
ag2-subagent-delegation is a skill published in the GitHub repository ag2ai/ag2-skills (10 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 173 tokens to every session and 2,248 once invoked, about $0.0009 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…
next-cache-components-optimizer
Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…
next-partial-prefetching-adoption
Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…