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stat-research-orchestrator 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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Ocupações relacionadas SOC
Baseado na classificação ocupacional SOC
name stat-research-orchestrator description 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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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
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
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
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
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
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?
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.