| name | super-lab-lite |
| description | Lightweight multi-agent research orchestration using one coordinator, three domain leads, and three lightweight research agents. Use for medium-size research, market scans, competitor comparisons, prior-art style exploration, report planning, and any task that benefits from parallel domain decomposition without running a full high-cost research lab. |
Super Lab Lite
Super Lab Lite is a small public orchestration pattern for splitting a research request into three domains, gathering evidence in parallel, and synthesizing the result into one useful answer.
It is designed for practical research tasks where a single model pass is too shallow, but a large multi-agent workflow is too expensive.
When To Use
- Medium-size research questions with 3 to 5 natural subtopics.
- Market scans, competitor comparisons, product research, and technical surveys.
- Report drafts where evidence gathering and synthesis should be separated.
- Public-safe exploratory analysis with a clear audit trail.
- Cost-sensitive work that does not need a full premium-agent stack.
Do not use it for:
- one-off simple questions,
- high-security work with sensitive data,
- tasks requiring strict legal, medical, or financial conclusions,
- research that cannot be decomposed into clear domains.
Public Model Pattern
Use the following roles. The exact model names can be adapted to the user's provider.
Coordinator:
- breaks the request into three domains
- assigns domain prompts
- merges final results
- resolves conflicts
Domain leads:
- analyze one domain each
- request supporting facts or examples
- write domain reports
Research agents:
- gather facts, links, snippets, or local-file evidence
- avoid final judgment
- return structured notes to domain leads
Workflow
1. Plan
Ask the coordinator to produce:
- the main research question,
- three non-overlapping domains,
- a domain prompt for each lead,
- an evidence request for each research agent,
- the final output format.
2. Gather
Run the three research agents in parallel where possible. Keep their job narrow:
- collect facts,
- inspect files,
- summarize sources,
- list uncertainties,
- return structured notes.
3. Analyze
Each domain lead turns one research packet into:
- domain summary,
- key evidence,
- risks or gaps,
- recommended follow-up.
4. Synthesize
The coordinator merges the domain reports:
- remove duplicates,
- surface disagreements,
- identify missing evidence,
- write the final answer,
- include a short audit note.
Output Structure
Prefer this final shape:
## Executive Summary
## Domain Findings
### Domain 1
### Domain 2
### Domain 3
## Cross-Domain Synthesis
## Gaps and Uncertainties
## Recommended Next Actions
Scripts
scripts/lite_orchestrator.py: standalone Python skeleton using the Anthropic SDK.scripts/claude_code_runner.md: Claude Code / agent-tool usage pattern.
The Python script expects an API key in ANTHROPIC_API_KEY when run directly. Do not hard-code keys in the repo.
Quality Checks
Before returning the final result:
- confirm that all three domain reports were included,
- mark missing or weak evidence,
- separate facts from judgment,
- avoid unsupported certainty,
- include the most useful next action.
Safety Boundary
This public version is a lightweight orchestration recipe. It does not include private scoring rules, protected workflows, tuned internal prompts, private datasets, or proprietary research benchmarks.
