# Stat Research Orchestrator > Orchestrate a statistical research pipeline centered on formal problem formulation, method proposal, theoretical analysis, experimental evaluation, comparison, and final result synthesis. - Skill: `gabrielmoreira/stat-research-orchestrator` (Agent Skill) - Install (CLI): `npx skillmds@latest add gabrielmoreira/stat-research-orchestrator` - Raw SKILL.md: https://api.skillmd.com/api/skills/gabrielmoreira/stat-research-orchestrator/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: DevOps & Infra - Author: gabrielmoreira (https://skillmd.com/u/gabrielmoreira) - Updated: 2026-09-09 - Page: https://skillmd.com/skills/gabrielmoreira/stat-research-orchestrator --- # 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//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//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//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: ```text progress//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: ```text progress//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: ```text progress//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: ```text progress//step6_quality_audit.md ``` ## Progress File Specification ### `progress//step0_problem_formulation.md` ```markdown # Step 0: Problem Formulation ## Status: PASS / FAIL ## Topic ID: ## Research Question ... ## Formal Data Model ... ## Target / Estimand ... ## Assumptions - ... ## Claims / Hypotheses - ... ## Evaluation Criteria - ... ## Theory Targets - ... ## Blocking Ambiguities - ... ``` ### `progress//step1_method_proposal.md` ```markdown # Step 1: Method Proposal ## Status: PASS / FAIL ## Proposed Method ... ## Baselines - ... ## Diagnostics - ... ## Ablations - ... ## Method-to-Claim Map - ... ``` ### `progress//step2_theory_analysis.md` ```markdown # Step 2: Theoretical Analysis ## Status: PASS / PARTIAL / FAIL ## Definitions ... ## Main Claims - ... ## Proof Sketches - ... ## Assumptions Required - ... ## Predicted Empirical Patterns - ... ## Limitations - ... ``` ### `progress//step3_experimental_evaluation.md` ```markdown # Step 3: Experimental Evaluation ## Status: PASS / FAIL ## Config experiments//config.yaml ## Code - ... ## Experiments - ... ## Metrics experiments//results/metrics.json ## Manifest experiments//results/run_manifest.json ## Warnings - ... ``` ### `progress//step4_comparison.md` ```markdown # Step 4: Comparison ## Status: PASS / FAIL ## Baseline Comparisons - ... ## Ablation Findings - ... ## Theory vs Experiment - ... ## Claim Verdicts experiments//results/claim_verdicts.json ``` ### `progress//step5_result_synthesis.md` ```markdown # Step 5: Result Synthesis ## Status: PASS / FAIL ## Paper experiments//report/paper.md ## README experiments//README.md ## Final Claims - ... ## Limitations - ... ``` ### `progress//step6_quality_audit.md` ```markdown # 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.