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Stat Research Orchestrator

Research
stat-research-orchestrator

Stat Research Orchestrator is an Agent Skill for research published in the aiming-lab/AutoResearchClaw repository on GitHub: a SKILL.md instruction file an AI agent can load to do this task.

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

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How to use this skill

Bring this guide into your coding agent with a prompt tailored to the tool you use.

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  2. Copy and paste the prompt below.
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Prompt to paste
Inspect the Agent Skill at https://github.com/aiming-lab/AutoResearchClaw/blob/HEAD/external/agents/stat_research_agent/skills/stat-research-orchestrator/SKILL.md and all supporting files. Report provenance, permissions, scripts, dependencies, and security risks. Propose an exact installation into .agents/skills/stat-research-orchestrator, but wait for my approval before writing files or running scripts.

Copying this prompt does not install or run the skill. Review third-party files before use.

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

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:

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:

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:

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

Provide formulation, method, and theory. Wait for:

progress/<TOPIC_ID>/step3_experimental_evaluation.md

Read:

  • Config path
  • Code paths
  • Metrics
  • Manifest
  • Raw results
  • Runtime deviations

Step 4: Invoke stat-comparison-analyst

Provide theory predictions and experiment outputs. Wait for:

progress/<TOPIC_ID>/step4_comparison.md

Read:

  • Comparison summary
  • Figures and tables
  • Claim verdicts
  • Theory-experiment agreements and disagreements

Step 5: Invoke stat-result-synthesizer

Provide all previous artifacts. Wait for:

progress/<TOPIC_ID>/step5_result_synthesis.md

Read:

  • Paper path
  • README path
  • Final claims
  • Limitations

Step 6: Invoke stat-quality-auditor

Audit the whole research chain:

  • Was the problem formulated formally?
  • Does the method address that formulation?
  • Is there theory or an explicit reason theory is limited?
  • Do experiments test theoretical predictions?
  • Are comparisons fair?
  • Are final conclusions supported?

Wait for:

progress/<TOPIC_ID>/step6_quality_audit.md

Progress File Specification

progress/<TOPIC_ID>/step0_problem_formulation.md

# Step 0: Problem Formulation
## Status: PASS / FAIL
## Topic ID: <TOPIC_ID>
## Research Question
...
## Formal Data Model
...
## Target / Estimand
...
## Assumptions
- ...
## Claims / Hypotheses
- ...
## Evaluation Criteria
- ...
## Theory Targets
- ...
## Blocking Ambiguities
- ...

progress/<TOPIC_ID>/step1_method_proposal.md

# Step 1: Method Proposal
## Status: PASS / FAIL
## Proposed Method
...
## Baselines
- ...
## Diagnostics
- ...
## Ablations
- ...
## Method-to-Claim Map
- ...

progress/<TOPIC_ID>/step2_theory_analysis.md

# Step 2: Theoretical Analysis
## Status: PASS / PARTIAL / FAIL
## Definitions
...
## Main Claims
- ...
## Proof Sketches
- ...
## Assumptions Required
- ...
## Predicted Empirical Patterns
- ...
## Limitations
- ...

progress/<TOPIC_ID>/step3_experimental_evaluation.md

# Step 3: Experimental Evaluation
## Status: PASS / FAIL
## Config
experiments/<TOPIC_ID>/config.yaml
## Code
- ...
## Experiments
- ...
## Metrics
experiments/<TOPIC_ID>/results/metrics.json
## Manifest
experiments/<TOPIC_ID>/results/run_manifest.json
## Warnings
- ...

progress/<TOPIC_ID>/step4_comparison.md

# Step 4: Comparison
## Status: PASS / FAIL
## Baseline Comparisons
- ...
## Ablation Findings
- ...
## Theory vs Experiment
- ...
## Claim Verdicts
experiments/<TOPIC_ID>/results/claim_verdicts.json

progress/<TOPIC_ID>/step5_result_synthesis.md

# Step 5: Result Synthesis
## Status: PASS / FAIL
## Paper
experiments/<TOPIC_ID>/report/paper.md
## README
experiments/<TOPIC_ID>/README.md
## Final Claims
- ...
## Limitations
- ...

progress/<TOPIC_ID>/step6_quality_audit.md

# Step 6: Quality Audit
## Status: PASS / WARN / FAIL
## Formulation Check
- ...
## Theory Check
- ...
## Experiment Check
- ...
## Comparison Check
- ...
## Blocking Issues
- ...

Key Conventions

  • Formulation is the gatekeeper. Do not write code before the target, assumptions, and evaluation criteria are explicit.
  • Theory is required as a pipeline stage. If no theorem is possible, write a clear heuristic or negative analysis and explain why.
  • Experiments should test theoretical predictions, not merely produce numbers.
  • Comparisons must include meaningful baselines or ablations.
  • Final results must connect formulation, method, theory, experiments, and comparison.