subagent-dispatch

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Mandatory protocol for dispatching any built-in and custom agent in this project via the task tool. Use this skill EVERY TIME you are about to call the task tool with a custom agent_type. This skill ensures the agent's intended model (declared in its YAML frontmatter) is respected rather than overridden by a default. Also encodes prompting best practices for subagent context and quality. ALWAYS invoke before any task tool call that targets a custom agent — even if the agent name seems obvious.

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Sourcerakaadi/agent-kit

Added on2026-04-02

NPX Install

npx skill4agent add rakaadi/agent-kit subagent-dispatch

SKILL.md Content

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Subagent Dispatch Protocol

This skill governs how to correctly dispatch custom agents in this project. Its primary purpose is to ensure each agent runs with its intended model — declared in the agent's own YAML frontmatter — rather than a platform default.

Why This Matters

Each agent in

.github/agents/

declares a

model

in its YAML frontmatter. That choice is deliberate: different agents are optimized for different models based on their role (e.g., the UI Composer on Gemini for creative work, the Compliance Reviewer on GPT-5.4 for analytical rigor). Calling the

task

tool without passing

model

ignores that intent entirely.

Model Resolution Protocol

Follow these steps before every custom agent dispatch:

Step 1 — Identify the agent file

Custom agents live at

.github/agents/<agent-name>.agent.md

. The

agent_type

value maps directly to the filename (e.g.,

ui-composer

→

ui-composer.agent.md

).

Step 2 — Look up the model

Use the Agent Registry below for the four known agents. If the agent isn't listed (a new one was added), read its

.agent.md

file and extract the

model

field from the YAML frontmatter at the top.

Step 3 — Map to the task tool's model ID

Agent frontmatter `model` value	`task` tool `model` parameter	Model Fallback
`Gemini 3.1 Pro (Preview) (copilot)`	`gemini-3.1-pro-preview`	Claude Opus 4.6
`Gemini 3 Pro (Preview) (copilot)`	`gemini-3-pro-preview`	Claude Opus 4.6
`Gemini 3 Flash (Preview) (copilot)`	`gemini-3-flash-preview`
`Claude Opus 4.6 (copilot)`	`claude-opus-4.6`	GPT-5.4
`Claude Sonnet 4.6 (copilot)`	`claude-sonnet-4.6`	GPT-5.4
`Claude Haiku 4.5 (copilot)`	`claude-haiku-4.5`
`GPT-5.4 (copilot)`	`gpt-5.4`
`GPT-5.4 mini (copilot)`	`gpt-5.4-mini`	Claude Haiku 4.5
`GPT-5.3-Codex (copilot)`	`gpt-5.3-codex`

Pattern: strip
 (copilot)
, lowercase, replace spaces with hyphens.

Fallbacks: If the intended model isn't available, use the fallback. Otherwise, default to the platform's default for that agent type.

Some built-in agent types (
task
,
code-review
) have no
.agent.md
file. Skip model resolution for these — use platform defaults or override manually based on task complexity.

Important: Prefer

GPT-5.4 mini

and

GPT-5.3-Codex

for

explore

and

general-purpose

built-in agents respectively.

Step 4 — Pass

model

to the task tool

Always include the resolved model ID as the

model

parameter:

task({
  agent_type: "ui-composer",
  model: "gemini-3.1-pro-preview",   // resolved from frontmatter
  description: "Build patient card component",
  prompt: "..."
})

Core Dispatch Principles

One agent per problem domain. Dispatch independent agents concurrently — don't serialize work that can run in parallel.
Subagents are stateless. They have zero memory of the current conversation. The prompt must be entirely self-contained with all the context needed.
Review before accepting. Evaluate subagent output critically and request revisions when quality or relevance falls short.

Prompting Checklist

A subagent prompt should answer all of these before you send it:

Context: What is the project? What stack/conventions are relevant? Which files matter?
Task: What exactly needs to be done? What are the constraints and requirements?
Direction: Where should the agent look first? What references or docs should it consult?
Success criteria: What does "done" look like? What's the expected output format?

Thin prompts produce thin results. If a subagent fails or produces something off-target, the root cause is almost always an underspecified prompt — not the agent's capability.