01 super lab

Public AI skills: small, useful agent workflows you can try today.

Install

npx -y skills add animaresearch/skills --skill 01-super-lab

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What its author says it does

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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.

SKILL.md

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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.