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

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

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

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

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

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.

AgentRoleWorkspaceCEOLeader, orchestrator, final authority.openclaw/workspace/ArtistImage 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/.

DivisionAgentsKey SpecialistsEngineering7Frontend Developer, Backend Architect, AI Engineer, DevOpsDesign7UI Designer, UX Architect, Image Prompt EngineerMarketing8Growth Hacker, Content Creator, Social MediaProduct3Sprint Prioritizer, Trend Researcher, Feedback SynthesizerProject Management5Senior PM, Studio Producer, Experiment TrackerTesting7Evidence Collector, Reality Checker, API TesterSupport6Analytics Reporter, Finance Tracker, Legal ComplianceSpatial Computing6XR Architect, visionOS EngineerSpecialized7Agents 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#

text

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

Visual Content Pipeline#

text

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

Dashboard / UI Feature Build#

text

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

Autonomous LLM Training (autoresearch)#

text

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

Full Product Launch#

text

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

Handoff Protocol#

When passing work between specialists:

text

## 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#

ModeScaleAgentsTimelineMicroSingle task/fix1-3Hours-daysSprintFeature or MVP5-101-2 weeksFullComplete product10+Weeks-months

Reference Files#

FileContentsSKILL.mdThis file — overview, scripts, quick startscripts/roster.shBrowse and search all agent rostersscripts/plan.shGenerate team proposals from task descriptionsscripts/activate.shLoad agent personality definitionsscripts/review.shGenerate QA review checklistsscripts/experiment.shRun autoresearch experiment loopsreferences/TEAM-CORE.mdCEO/Artist — roles and interactionsreferences/TEAM-AGENCY.mdAll 55+ Agency specialists indexed by divisionreferences/TEAM-RESEARCH.mdAutonomous experiment methodology (autoresearch)references/PLANNER.mdJob analysis → team proposal workflow (detailed)references/REVIEWER.mdQA validation workflow with quality gatesreferences/PROOF-OF-WORK.mdExample proposals showing cross-roster teams