Agent skill

Launch Sub Agent

by NeoLabHQ in NeoLabHQ/context-engineering-kit

Launch an intelligent sub-agent with automatic model selection based on task complexity, specialized agent matching, Zero-shot CoT reasoning, and mandatory self-critique verification

GPL-3.0Auto-check passedAgent Workflows

Install Launch Sub Agent

skills CLI
$ npx skills add NeoLabHQ/context-engineering-kit --skill launch-sub-agent -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install NeoLabHQ/context-engineering-kit launch-sub-agent --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/NeoLabHQ/context-engineering-kit.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/launch-sub-agent .claude/skills/launch-sub-agent && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
launch-sub-agent
GitHub stars
1.7k
Token cost
~3k tokens
SKILL.md length
721 words
Files
1
Skills in repo
57
Repo updated
First seen
Licence
GPL-3.0

At a glance

Launch an intelligent sub-agent with automatic model selection based on task complexity, specialized agent matching, Zero-shot CoT reasoning, and mandatory self-critique verification

  • Works in 5 steps: Task Analysis with Zero-shot CoT → Model Selection → Specialized Agent Matching → …
  • Tasks that involve Subagents
  • SKILL.md covers Process, Examples and Best Practices
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Launch Sub Agent is an agent skill from NeoLabHQ/context-engineering-kit. Launch an intelligent sub-agent with automatic model selection based on task complexity, specialized agent matching, Zero-shot CoT reasoning, and mandatory self-critique verification

Its SKILL.md is about 3k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Agent Workflows, covering Subagents. The repository describes itself as: Hand-crafted Claude Code Skills focused on improving agent results quality. Compatible with OpenCode, Cursor, Antigravity, Gemini CLI, and others. Includes CodeRabbit open-source… The licence is GPL-3.0.

When your agent uses it

  • Tasks that involve Subagents

Example prompts

  • “/launch-sub-agent”

Workflow steps

5 steps, taken from the step headings in SKILL.md.

  1. Task Analysis with Zero-shot CoT
  2. Model Selection
  3. Specialized Agent Matching
  4. Construct Sub-Agent Prompt
  5. Dispatch Sub-Agent

What it can do on your machine

Read from SKILL.md and the folder at commit 23e2428. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are markdown).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Launch Sub Agent loads about 3k tokens when it runs. Until then it costs about 50 tokens; SKILL.md has 721 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~50
When it runs · the whole SKILL.md, loaded when a task matches
~3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from NeoLabHQ/context-engineering-kit at commit 23e2428, republished under its GPL-3.0 licence (© NeoLabHQ). 721 words, ~2,982 tokens.

Download SKILL.mdSave it as .claude/skills/launch-sub-agent/SKILL.md (or your agent's skills folder).
name
launch-sub-agent
description
Launch an intelligent sub-agent with automatic model selection based on task complexity, specialized agent matching, Zero-shot CoT reasoning, and mandatory self-critique verification

launch-sub-agent

<task>
Launch a focused sub-agent to execute the provided task. Analyze the task to intelligently select the optimal model and agent configuration, then dispatch a sub-agent with Zero-shot Chain-of-Thought reasoning at the beginning and mandatory self-critique verification at the end.
</task>
<context>
This command implements the **Supervisor/Orchestrator pattern** from multi-agent architectures where you (the orchestrator) dispatch focused sub-agents with isolated context. The primary benefit is **context isolation** - each sub-agent operates in a clean context window focused on its specific task without accumulated context pollution.
</context>

Process

Phase 1: Task Analysis with Zero-shot CoT

Before dispatching, analyze the task systematically. Think through step by step:

Let me analyze this task step by step to determine the optimal configuration:

1. **Task Type Identification**
   "What type of work is being requested?"
   - Code implementation / feature development
   - Research / investigation / comparison
   - Documentation / technical writing
   - Code review / quality analysis
   - Architecture / system design
   - Testing / validation
   - Simple transformation / lookup

2. **Complexity Assessment**
   "How complex is the reasoning required?"
   - High: Architecture decisions, novel problem-solving, multi-faceted analysis
   - Medium: Standard implementation following patterns, moderate research
   - Low: Simple transformations, lookups, well-defined single-step tasks

3. **Output Size Estimation**
   "How extensive is the expected output?"
   - Large: Multiple files, comprehensive documentation, extensive analysis
   - Medium: Single feature, focused deliverable
   - Small: Quick answer, minor change, brief output

4. **Domain Expertise Check**
   "Does this task match a specialized agent profile?"
   - Development: code, implement, feature, endpoint, TDD, tests
   - Research: investigate, compare, evaluate, options, library
   - Documentation: document, README, guide, explain, tutorial
   - Architecture: design, system, structure, scalability
   - Exploration: understand, navigate, find, codebase patterns
Phase 2: Model Selection

Select the optimal model based on task analysis:

Task ProfileRecommended ModelRationale
Complex reasoning (architecture, design, critical decisions)opusMaximum reasoning capability
Specialized domain (matches agent profile)Opus + Specialized AgentDomain expertise + reasoning power
Non-complex but long (extensive docs, verbose output)sonnet[1m]Good capability, cost-efficient for length
Simple and short (trivial tasks, quick lookups)haikuFast, cost-effective for easy tasks
Default (when uncertain)opusOptimize for quality over cost

Decision Tree:

Is task COMPLEX (architecture, design, novel problem, critical decision)?
|
+-- YES --> Use Opus (highest capability)
|           |
|           +-- Does it match a specialized domain?
|               +-- YES --> Include specialized agent prompt
|               +-- NO --> Use Opus alone
|
+-- NO --> Is task SIMPLE and SHORT?
           |
           +-- YES --> Use Haiku (fast, cheap)
           |
           +-- NO --> Is output LONG but task not complex?
                      |
                      +-- YES --> Use Sonnet (balanced)
                      |
                      +-- NO --> Use Opus (default)
Phase 3: Specialized Agent Matching

If the task matches a specialized domain, incorporate the relevant agent prompt. Specialized agents provide domain-specific best practices, quality standards, and structured approaches that improve output quality.

Decision: Use specialized agent when task clearly benefits from domain expertise. Skip for trivial tasks where specialization adds unnecessary overhead.

Agents: Available specialized agents depends on project and plugins installed. Common agents from the sdd plugin include: sdd:developer, sdd:researcher, sdd:software-architect, sdd:tech-lead, sdd:code-explorer, sdd:business-analyst, sdd:code-reviewer, sdd:tech-writer. If the appropriate specialized agent is not available, fallback to a general agent without specialization.

Integration with Model Selection:

  • Specialized agents are combined WITH model selection, not instead of
  • Complex task + specialized domain = Opus + Specialized Agent
  • Simple task matching domain = Haiku without specialization (overhead not justified)

Usage:

  1. Read the agent definition
  2. Include the agent's instructions in the sub-agent prompt AFTER the CoT prefix
  3. Combine with Zero-shot CoT prefix and Critique suffix
Phase 4: Construct Sub-Agent Prompt

Build the sub-agent prompt with these mandatory components:

4.1 Zero-shot Chain-of-Thought Prefix (REQUIRED - MUST BE FIRST)
markdown
## Reasoning Approach

Before taking any action, you MUST think through the problem systematically.

Let's approach this step by step:

1. "Let me first understand what is being asked..."
   - What is the core objective?
   - What are the explicit requirements?
   - What constraints must I respect?

2. "Let me break this down into concrete steps..."
   - What are the major components of this task?
   - What order should I tackle them?
   - What dependencies exist between steps?

3. "Let me consider what could go wrong..."
   - What assumptions am I making?
   - What edge cases might exist?
   - What could cause this to fail?

4. "Let me verify my approach before proceeding..."
   - Does my plan address all requirements?
   - Is there a simpler approach?
   - Am I following existing patterns?

Work through each step explicitly before implementing.
4.2 Task Body
markdown
<task>
{Task description from $ARGUMENTS}
</task>

<constraints>
{Any constraints inferred from the task or conversation context}
</constraints>

<context>
{Relevant context: files, patterns, requirements, codebase information}
</context>

<output>
{Expected deliverable: format, location, structure}
</output>
4.3 Self-Critique Suffix (REQUIRED - MUST BE LAST)
markdown
## Self-Critique Loop (MANDATORY)

Before completing, you MUST verify your work. Submitting unverified work is UNACCEPTABLE.

### 1. Generate 5 Verification Questions

Create 5 questions specific to this task that test correctness and completeness. There example questions:

| # | Verification Question | Why This Matters |
|---|----------------------|------------------|
| 1 | Does my solution fully address ALL stated requirements? | Partial solutions = failed task |
| 2 | Have I verified every assumption against available evidence? | Unverified assumptions = potential failures |
| 3 | Are there edge cases or error scenarios I haven't handled? | Edge cases cause production issues |
| 4 | Does my solution follow existing patterns in the codebase? | Pattern violations create maintenance debt |
| 5 | Is my solution clear enough for someone else to understand and use? | Unclear output reduces value |

### 2. Answer Each Question with Evidence

For each question, examine your solution and provide specific evidence:

[Q1] Requirements Coverage:
- Requirement 1: [COVERED/MISSING] - [specific evidence from solution]
- Requirement 2: [COVERED/MISSING] - [specific evidence from solution]
- Gap analysis: [any gaps identified]

[Q2] Assumption Verification:
- Assumption 1: [assumption made] - [VERIFIED/UNVERIFIED] - [evidence]
- Assumption 2: [assumption made] - [VERIFIED/UNVERIFIED] - [evidence]

[Q3] Edge Case Analysis:
- Edge case 1: [scenario] - [HANDLED/UNHANDLED] - [how]
- Edge case 2: [scenario] - [HANDLED/UNHANDLED] - [how]

[Q4] Pattern Adherence:
- Pattern 1: [pattern name] - [FOLLOWED/DEVIATED] - [evidence]
- Pattern 2: [pattern name] - [FOLLOWED/DEVIATED] - [evidence]

[Q5] Clarity Assessment:
- Is the solution well-organized? [YES/NO]
- Are complex parts explained? [YES/NO]
- Could someone else use this immediately? [YES/NO]

### 3. Revise If Needed

If ANY verification question reveals a gap:
1. **STOP** - Do not submit incomplete work
2. **FIX** - Address the specific gap identified
3. **RE-VERIFY** - Confirm the fix resolves the issue
4. **DOCUMENT** - Note what was changed and why

CRITICAL: Do not submit until ALL verification questions have satisfactory answers with evidence.
Phase 5: Dispatch Sub-Agent

Use the Task tool to dispatch with the selected configuration:

Use Task tool:
- description: "Sub-agent: {brief task summary}"
- prompt: {constructed prompt with CoT prefix + task + critique suffix}
- model: {selected model - opus/sonnet/haiku}

Context isolation reminder: Pass only context relevant to this specific task. Do not pass entire conversation history.

Examples

Example 1: Complex Architecture Task (Opus)

Input: /launch-sub-agent Design a caching strategy for our API that handles 10k requests/second

Analysis:

  • Task type: Architecture / design
  • Complexity: High (performance requirements, system design)
  • Output size: Medium (design document)
  • Domain match: sdd:software-architect

Selection: Opus + sdd:software-architect agent

Dispatch: Task tool with Opus model, sdd:software-architect prompt, CoT prefix, critique suffix


Show full SKILL.md (272 more words)Show less
Example 2: Simple Documentation Update (Haiku)

Input: /launch-sub-agent Update the README to add --verbose flag to CLI options

Analysis:

  • Task type: Documentation (simple edit)
  • Complexity: Low (single file, well-defined)
  • Output size: Small (one section)
  • Domain match: None needed (too simple)

Selection: Haiku (fast, cheap, sufficient for task)

Dispatch: Task tool with Haiku model, basic CoT prefix, basic critique suffix


Example 3: Moderate Implementation (Sonnet + Developer)

Input: /launch-sub-agent Implement pagination for /users endpoint following patterns in /products

Analysis:

  • Task type: Code implementation
  • Complexity: Medium (follow existing patterns)
  • Output size: Medium (implementation + tests)
  • Domain match: sdd:developer

Selection: Sonnet + sdd:developer agent (non-complex but needs domain expertise)

Dispatch: Task tool with Sonnet model, sdd:developer prompt, CoT prefix, critique suffix


Example 4: Research Task (Opus + Researcher)

Input: /launch-sub-agent Research authentication options for mobile app - evaluate OAuth2, SAML, passwordless

Analysis:

  • Task type: Research / comparison
  • Complexity: High (comparative analysis, recommendations)
  • Output size: Large (comprehensive research)
  • Domain match: sdd:researcher

Selection: Opus + sdd:researcher agent

Dispatch: Task tool with Opus model, sdd:researcher prompt, CoT prefix, critique suffix

Best Practices

Context Isolation
  • Pass only context relevant to the specific task
  • Avoid passing entire conversation history
  • Let sub-agent discover codebase patterns through tools
  • Use file paths and references rather than embedding large content
Model Selection
  • When in doubt, use Opus (quality over cost)
  • Use Haiku only for truly trivial tasks
  • Use Sonnet for "grunt work" - needs capability but not genius
  • Production code always deserves Opus
Specialized Agents
  • Use when domain expertise clearly improves quality
  • Combine with CoT and critique patterns
  • Don't force specialization on general tasks
Quality Gates
  • Self-critique loop is non-negotiable
  • Sub-agents must answer verification questions before completing
  • Review sub-agent output before accepting

© NeoLabHQ, GPL-3.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/launch-sub-agent of NeoLabHQ/context-engineering-kit.

Open the folder on GitHubat commit 23e2428

Compare with similar skills

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Task Observerrebelytics/one-skill-to-rule-them-all3.2k1 repos~12kAutomated safety check: PassCC-BY-4.0

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Categories

Questions about Launch Sub Agent

What does Launch Sub Agent do?

Launch an intelligent sub-agent with automatic model selection based on task complexity, specialized agent matching, Zero-shot CoT reasoning, and mandatory self-critique verification. Launch Sub Agent is an agent skill from NeoLabHQ/context-engineering-kit.

When should I use Launch Sub Agent?

Launch Sub Agent fits situations like: tasks that involve Subagents.

How do I install Launch Sub Agent in Claude Code?

Run `npx skills add NeoLabHQ/context-engineering-kit --skill launch-sub-agent -a claude-code`. Or copy the skill folder (skills/launch-sub-agent in NeoLabHQ/context-engineering-kit) into .claude/skills/launch-sub-agent in your project. Claude Code loads it when a task matches its description.

How do I install Launch Sub Agent in Codex?

Run `npx skills add NeoLabHQ/context-engineering-kit --skill launch-sub-agent -a codex`. Or copy the skill folder (skills/launch-sub-agent in NeoLabHQ/context-engineering-kit) into .agents/skills/launch-sub-agent in your project. Codex loads it when a task matches its description.

Can I use Launch Sub Agent in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add NeoLabHQ/context-engineering-kit --skill launch-sub-agent -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/launch-sub-agent, .gemini/skills/launch-sub-agent, .github/skills/launch-sub-agent and .opencode/skills/launch-sub-agent in your project.

What does Launch Sub Agent need to run?

SKILL.md names no scripts, command-line tools or credentials: Launch Sub Agent is instructions for the agent only.

Does Launch Sub Agent access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Launch Sub Agent safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Launch Sub Agent use?

Launch Sub Agent is published under the GPL-3.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Launch Sub Agent use?

About 3k tokens (SKILL.md is roughly 12k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Launch Sub Agent?

Skills that share tags, products or a category with Launch Sub Agent: Claude Code Agent Development (anthropics/claude-plugins-official, 38k stars), Subagent Driven Development (Asvarox/allkaraoke, 261 stars), Dispatching Parallel Agents (ultralisp/ultralisp, 258 stars) and Paseo Advisor Second Opinion (getpaseo/paseo, 20k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Launch Sub Agent?

NeoLabHQ (a GitHub organization) maintains it in NeoLabHQ/context-engineering-kit, which has 1,749 GitHub stars. The repository holds 57 skills in this directory. The repository was last updated on August 26, 2026.

Source: NeoLabHQ/context-engineering-kit on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.