# Stat Research Orchestrator

Orchestrate a statistical research pipeline centered on formal problem formulation...

- Repository: https://github.com/aiming-lab/AutoResearchClaw (14798 stars)
- Category: Writing
- License: MIT
- Last updated: 2026-08-19
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add aiming-lab/AutoResearchClaw --skill stat-research-orchestrator -g -a claude-code -y`
Codex: `npx skills add aiming-lab/AutoResearchClaw --skill stat-research-orchestrator -g -a codex -y`
Cursor: `npx skills add aiming-lab/AutoResearchClaw --skill stat-research-orchestrator -g -a cursor -y`
Gemini CLI: `npx skills add aiming-lab/AutoResearchClaw --skill stat-research-orchestrator -g -a gemini-cli -y`

## Description

Orchestrate a statistical research pipeline centered on formal problem formulation, method proposal, theoretical analysis, experimental evaluation, comparison, and final result synthesis.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# Statistical Research Orchestrator

## Overview

Coordinates the full statistical research pipeline. This is not a code-first
benchmark workflow. The pipeline begins with formal problem formulation and
requires theory before final comparisons and conclusions.

## Full Pipeline

```text
Topic prompt / topic file / dataset description
  -> [stat-problem-formulator]   formal problem, notation, assumptions, targets
  -> [stat-method-proposer]      proposed method, baselines, diagnostics, ablations
  -> [stat-theory-analyzer]      theoretical properties, proof sketches, predictions
  -> [stat-experiment-designer]  experiments, code, metrics, manifest
  -> [stat-comparison-analyst]   method comparison, theory-vs-experiment check
  -> [stat-result-synthesizer]   final report, conclusions, limitations
  -> [stat-quality-auditor]      formulation/theory/evidence audit
```

## Workflow

### Step 0: Invoke stat-problem-formulator

Provide the topic source and any requirements. Wait for:

```text
progress/<TOPIC_ID>/step0_problem_formulation.md
```

Read:

- Formal data model
- Target parameter or decision target
- Assumptions
- Hypotheses or claims
- Evaluation criteria
- Theory targets

Do not proceed if the target or assumptions are undefined.

### Step 1: Invoke stat-method-proposer

Provide the problem formulation. Wait for:

```text
progress/<TOPIC_ID>/step1_method_proposal.md
```

Read:

- Proposed method
- Baselines
- Oracle references, if any
- Ablations
- Diagnostics
- Implementation requirements

### Step 2: Invoke stat-theory-analyzer

Provide the formulation and method proposal. Wait for:

```text
progress/<TOPIC_ID>/step2_theory_analysis.md
```

Read:

- Theoretical claims
- Required assumptions
- Proof sketches or derivations
- Predicted empirical patterns
- Limitations

Theory can be partial, but the report must honestly label what is proven,
heuristic, or only experimentally supported.

### Step 3: Invoke stat-experiment-designer

The rest: https://github.com/aiming-lab/AutoResearchClaw/blob/HEAD/external/agents/stat_research_agent/skills/stat-research-orchestrator/SKILL.md