Team Builder

Compose the right team for any job by drawing from three rosters of specialists. The Research Lab uses Karpathy's autoresearch methodology for autonomous experiment loops.

Quick Start — Scripts

1. Browse available agents

bash {baseDir}/scripts/roster.sh                     # all 3 rosters
bash {baseDir}/scripts/roster.sh -r agency            # agency only
bash {baseDir}/scripts/roster.sh -d engineering        # one division
bash {baseDir}/scripts/roster.sh -s "frontend"         # search
bash {baseDir}/scripts/roster.sh -v                    # verbose descriptions
bash {baseDir}/scripts/roster.sh -j                    # JSON output

2. Generate a team proposal

bash {baseDir}/scripts/plan.sh "Build a portfolio dashboard with pie charts"
bash {baseDir}/scripts/plan.sh --mode sprint "Optimize image generation prompts using autoresearch"
bash {baseDir}/scripts/plan.sh -o proposal.md "Analyze astronomy photos for star classification"

The planner auto-detects task domains (engineering, creative, research, marketing, operations, spatial) and proposes the right-sized team (micro/sprint/full).

3. Activate a specialist

bash {baseDir}/scripts/activate.sh --division engineering --agent frontend-developer
bash {baseDir}/scripts/activate.sh --division testing --agent evidence-collector
bash {baseDir}/scripts/activate.sh --division testing --list
bash {baseDir}/scripts/activate.sh --file reference/agency-agents-main/design/design-ui-designer.md
bash {baseDir}/scripts/activate.sh --division engineering --agent ai-engineer --personality-only

Outputs the agent's full personality definition for use in delegation prompts.

4. Run QA review

bash {baseDir}/scripts/review.sh --task "Portfolio dashboard"
bash {baseDir}/scripts/review.sh --task "Image pipeline" --criteria criteria.txt --pass evidence
bash {baseDir}/scripts/review.sh --task "LLM training optimization" --pass reality
bash {baseDir}/scripts/review.sh --task "Full product" --pass both -o review.md

Generates review checklists (Evidence Collector Pass 1 + Reality Checker Pass 2) and logs to ~/.openclaw/team-reviews/.

5. Run a Research Lab experiment

bash {baseDir}/scripts/experiment.sh --setup /path/to/project     # initialize experiment
bash {baseDir}/scripts/experiment.sh --run /path/to/project       # run one experiment cycle
bash {baseDir}/scripts/experiment.sh --status /path/to/project    # show ledger

See references/TEAM-RESEARCH.md for the full autoresearch methodology and working examples.

The Three Rosters

1. Core Team (references/TEAM-CORE.md)

The permanent OpenClaw agents. Always available, always running.

Agent	Role	Workspace
CEO	Leader, orchestrator, final authority	.openclaw/workspace/
Artist	Image generation, visual analysis	.openclaw/workspace-artist/

2. Agency Division (references/TEAM-AGENCY.md)

55+ specialist agents across 9 divisions. Activated on demand from reference/agency-agents-main/.

Division	Agents	Key Specialists
Engineering	7	Frontend Developer, Backend Architect, AI Engineer, DevOps
Design	7	UI Designer, UX Architect, Image Prompt Engineer
Marketing	8	Growth Hacker, Content Creator, Social Media
Product	3	Sprint Prioritizer, Trend Researcher, Feedback Synthesizer
Project Management	5	Senior PM, Studio Producer, Experiment Tracker
Testing	7	Evidence Collector, Reality Checker, API Tester
Support	6	Analytics Reporter, Finance Tracker, Legal Compliance
Spatial Computing	6	XR Architect, visionOS Engineer
Specialized	7	Agents Orchestrator, Data Analytics, LSP Engineer

3. Research Lab (references/TEAM-RESEARCH.md)

Autonomous experiment loops adapted from Karpathy's autoresearch. Set up a measurable experiment, run it in a fixed time budget, keep improvements, discard failures, loop forever.

Source code reference: reference/autoresearch-master/ (program.md, train.py, prepare.py)

Cross-Team Workflow Examples

Image Analysis + Research Loop

Artist (image acquisition) + Research Lab (analysis loop) + AI Engineer (classification)

Visual Content Pipeline

Artist (generation) + Image Prompt Engineer (prompts) + Visual Storyteller (narrative)

Dashboard / UI Feature Build

Senior PM (scope) + Frontend Developer (build) + Evidence Collector (QA)

Autonomous LLM Training (autoresearch)

Research Lab (experiment loop on train.py) + AI Engineer (architecture suggestions)
→ 12 experiments/hour, ~100 overnight, fully autonomous

Full Product Launch

CEO (orchestrate) + Engineering (build) + Design (UX) + Marketing (launch) + Testing (validate)

Handoff Protocol

When passing work between specialists:

## Handoff
| Field | Value |
|-------|-------|
| From | [Agent Name] |
| To | [Agent Name] |
| Task | [What needs to be done] |
| Priority | [Critical / High / Medium / Low] |

## Context
- Current state: [What's been done]
- Relevant files: [File paths]

## Deliverable
- What is needed: [Specific output]
- Acceptance criteria:
  - [ ] [Criterion 1]
  - [ ] [Criterion 2]

## Quality
- Evidence required: [What proof looks like]
- Reviewer: [Who validates]

For complete handoff templates: reference/agency-agents-main/strategy/coordination/handoff-templates.md

NEXUS Pipeline Modes

Mode	Scale	Agents	Timeline
Micro	Single task/fix	1-3	Hours-days
Sprint	Feature or MVP	5-10	1-2 weeks
Full	Complete product	10+	Weeks-months

Reference Files

File	Contents
SKILL.md	This file — overview, scripts, quick start
scripts/roster.sh	Browse and search all agent rosters
scripts/plan.sh	Generate team proposals from task descriptions
scripts/activate.sh	Load agent personality definitions
scripts/review.sh	Generate QA review checklists
scripts/experiment.sh	Run autoresearch experiment loops
references/TEAM-CORE.md	CEO/Artist — roles and interactions
references/TEAM-AGENCY.md	All 55+ Agency specialists indexed by division
references/TEAM-RESEARCH.md	Autonomous experiment methodology (autoresearch)
references/PLANNER.md	Job analysis → team proposal workflow (detailed)
references/REVIEWER.md	QA validation workflow with quality gates
references/PROOF-OF-WORK.md	Example proposals showing cross-roster teams