Stat Research Orchestrator logo

Stat Research Orchestrator

OrganizationPopular
aiming-lab
stat-research-orchestrator

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

Overview

Publisheraiming-lab
RepositoryAutoResearchClaw
Skill namestat-research-orchestrator
Stars
14.4K
Forks
1.7K
Bundled files
Instructions only
LicenseMIT
Links
Skill sourceRepository
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by aiming-lab on GitHub. Read the source before you install it.

Installation

Install the Stat Research Orchestrator AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/aiming-lab/AutoResearchClaw.git /tmp/AutoResearchClaw
mkdir -p .claude/skills
cp -r /tmp/AutoResearchClaw/external/agents/stat_research_agent/skills/stat-research-orchestrator .claude/skills/stat-research-orchestrator
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Stat Research Orchestrator in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Stat Research Orchestrator on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Stat Research Orchestrator is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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

Provide formulation, method, and theory. Wait for:

text
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:

text
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:

text
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:

text
progress/<TOPIC_ID>/step6_quality_audit.md

Progress File Specification

progress/<TOPIC_ID>/step0_problem_formulation.md

markdown
# 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

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

progress/<TOPIC_ID>/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/<TOPIC_ID>/step3_experimental_evaluation.md

markdown
# 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

markdown
# 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

markdown
# 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

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.

Frequently asked questions

What does the Stat Research Orchestrator AI skill do?

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

Why use Stat Research Orchestrator on TypingMind?

Because you install it once and use it with any model. Stat Research Orchestrator is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Stat Research Orchestrator in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/aiming-lab/AutoResearchClaw/tree/main/external/agents/stat_research_agent/skills/stat-research-orchestrator. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Stat Research Orchestrator?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Stat Research Orchestrator?

As many as you like. As long as a model supports skills, you can use Stat Research Orchestrator with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Stat Research Orchestrator AI skill free?

Yes. It is published on GitHub by aiming-lab under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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