Video Template Frame Pentagram Stat
nexu-io/open-design
Use this plugin when the user wants a "Pentagram Stat Frame" HyperFrames motion video — Swiss-grid statistic anchor — giant number, red accent, growing bars, black data bar.
Orchestrate a statistical research pipeline centered on formal problem formulation, method proposal, theoretical analysis, experimental evaluation, comparison, and final result synthesis.
MITAuto-check passed
Install Stat Research Orchestrator
$ npx skills add aiming-lab/AutoResearchClaw --skill stat-research-orchestrator -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install aiming-lab/AutoResearchClaw stat-research-orchestrator --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/aiming-lab/AutoResearchClaw.git skills-src && mkdir -p .claude/skills && cp -r skills-src/external/agents/stat_research_agent/skills/stat-research-orchestrator .claude/skills/stat-research-orchestrator && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "stat-research-orchestrator" agent skill from https://github.com/aiming-lab/AutoResearchClaw/tree/main/external/agents/stat_research_agent/skills/stat-research-orchestrator into .claude/skills/stat-research-orchestrator/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "stat-research-orchestrator", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/aiming-lab/AutoResearchClaw/tree/main/external/agents/stat_research_agent/skills/stat-research-orchestratorType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add aiming-lab/AutoResearchClaw --skill stat-research-orchestrator -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install aiming-lab/AutoResearchClaw stat-research-orchestrator --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/aiming-lab/AutoResearchClaw.git skills-src && mkdir -p .agents/skills && cp -r skills-src/external/agents/stat_research_agent/skills/stat-research-orchestrator .agents/skills/stat-research-orchestrator && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "stat-research-orchestrator" agent skill from https://github.com/aiming-lab/AutoResearchClaw/tree/main/external/agents/stat_research_agent/skills/stat-research-orchestrator into .agents/skills/stat-research-orchestrator/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "stat-research-orchestrator", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add aiming-lab/AutoResearchClaw --skill stat-research-orchestrator -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install aiming-lab/AutoResearchClaw stat-research-orchestrator --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/aiming-lab/AutoResearchClaw.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/external/agents/stat_research_agent/skills/stat-research-orchestrator .cursor/skills/stat-research-orchestrator && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "stat-research-orchestrator" agent skill from https://github.com/aiming-lab/AutoResearchClaw/tree/main/external/agents/stat_research_agent/skills/stat-research-orchestrator into .cursor/skills/stat-research-orchestrator/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "stat-research-orchestrator", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/aiming-lab/AutoResearchClaw.git --path external/agents/stat_research_agent/skills/stat-research-orchestrator--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add aiming-lab/AutoResearchClaw --skill stat-research-orchestrator -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install aiming-lab/AutoResearchClaw stat-research-orchestrator --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/aiming-lab/AutoResearchClaw.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/external/agents/stat_research_agent/skills/stat-research-orchestrator .gemini/skills/stat-research-orchestrator && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "stat-research-orchestrator" agent skill from https://github.com/aiming-lab/AutoResearchClaw/tree/main/external/agents/stat_research_agent/skills/stat-research-orchestrator into .gemini/skills/stat-research-orchestrator/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "stat-research-orchestrator", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install aiming-lab/AutoResearchClaw stat-research-orchestratorInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add aiming-lab/AutoResearchClaw --skill stat-research-orchestrator -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/aiming-lab/AutoResearchClaw.git skills-src && mkdir -p .github/skills && cp -r skills-src/external/agents/stat_research_agent/skills/stat-research-orchestrator .github/skills/stat-research-orchestrator && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "stat-research-orchestrator" agent skill from https://github.com/aiming-lab/AutoResearchClaw/tree/main/external/agents/stat_research_agent/skills/stat-research-orchestrator into .github/skills/stat-research-orchestrator/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "stat-research-orchestrator", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add aiming-lab/AutoResearchClaw --skill stat-research-orchestrator -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install aiming-lab/AutoResearchClaw stat-research-orchestrator --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/aiming-lab/AutoResearchClaw.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/external/agents/stat_research_agent/skills/stat-research-orchestrator .opencode/skills/stat-research-orchestrator && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "stat-research-orchestrator" agent skill from https://github.com/aiming-lab/AutoResearchClaw/tree/main/external/agents/stat_research_agent/skills/stat-research-orchestrator into .opencode/skills/stat-research-orchestrator/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "stat-research-orchestrator", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
stat-research-orchestrator- GitHub stars
- 15k
- Token cost
- ~1.5k tokens
- SKILL.md length
- 325 words
- Files
- 1
- Skills in repo
- 34
- Repo updated
- First seen
- Licence
- MIT
At a glance
Orchestrate a statistical research pipeline centered on formal problem formulation, method proposal, theoretical analysis, experimental evaluation, comparison, and final result synthesis.
- Works in 7 steps: Invoke stat-problem-formulator → Invoke stat-method-proposer → Invoke stat-theory-analyzer → …
- SKILL.md covers Overview, Full Pipeline, Workflow and Progress File Specification, plus 1 more section
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Stat Research Orchestrator is an agent skill from aiming-lab/AutoResearchClaw. Orchestrate a statistical research pipeline centered on formal problem formulation, method proposal, theoretical analysis, experimental evaluation, comparison, and final result synthesis.
Its SKILL.md is about 1.5k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
The repository describes itself as: Fully autonomous & self-evolving research from idea to paper. Chat an Idea. Get a Paper. 🦞. The licence is MIT.
Example prompts
- “/stat-research-orchestrator”
Workflow steps
7 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit be4ba47. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md (its code samples are markdown).
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Stat Research Orchestrator loads about 1.5k tokens when it runs. Until then it costs about 54 tokens; SKILL.md has 325 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from aiming-lab/AutoResearchClaw at commit be4ba47, republished under its MIT licence (© aiming-lab). 325 words, ~1,518 tokens.
Download SKILL.mdSave it as
.claude/skills/stat-research-orchestrator/SKILL.md (or your agent's skills folder).- 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.
- metadata.category
- domain
- metadata.trigger-keywords
- statistics,statistical research,problem formulation,method proposal,theory,experiments,comparison,results
- metadata.applicable-stages
- 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,20
- metadata.priority
- 1
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 auditWorkflow
Step 0: Invoke stat-problem-formulator
Provide the topic source and any requirements. Wait for:
progress/<TOPIC_ID>/step0_problem_formulation.mdRead:
- 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.mdRead:
- 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.mdRead:
- 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.mdRead:
- 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.mdRead:
- 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.mdRead:
- 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.mdProgress 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.jsonprogress/<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.
© aiming-lab, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in external/agents/stat_research_agent/skills/stat-research-orchestrator of aiming-lab/AutoResearchClaw.
Open the folder on GitHubat commit be4ba47
Compare with similar skills
Stat Research Orchestrator next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Stat Research Orchestrator this skillaiming-lab/AutoResearchClaw | 15k | — | ~1.5k | Automated safety check: Pass | MIT | |
| Video Template Frame Pentagram Statnexu-io/open-design | 100k | — | ~381 | Automated safety check: Pass | Apache-2.0 | |
| Statistical PowerK-Dense-AI/scientific-agent-skills | 48k | 1 repos | ~4.4k | Automated safety check: Notes | MIT | |
| Team Agent Orchestrationaffaan-m/ECC | 276k | 1 repos | ~1.2k | Automated safety check: Pass | MIT | |
| Orca Orchestrationstablyai/orca | 88k | — | ~916 | Automated safety check: Pass | MIT | |
| Agent Orchestrator Taskruvnet/ruflo | 74k | 2 repos | ~1k | Automated safety check: Pass | MIT |
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Questions about Stat Research Orchestrator
What does Stat Research Orchestrator do?
Orchestrate a statistical research pipeline centered on formal problem formulation, method proposal, theoretical analysis, experimental evaluation, comparison, and final result synthesis. Stat Research Orchestrator is an agent skill from aiming-lab/AutoResearchClaw. Orchestrate a statistical research pipeline centered on formal problem formulation, method proposal, theoretical analysis, experimental evaluation, comparison, and final result synthesis.
How do I install Stat Research Orchestrator in Claude Code?
Run `npx skills add aiming-lab/AutoResearchClaw --skill stat-research-orchestrator -a claude-code`. Or copy the skill folder (external/agents/stat_research_agent/skills/stat-research-orchestrator in aiming-lab/AutoResearchClaw) into .claude/skills/stat-research-orchestrator in your project. Claude Code loads it when a task matches its description.
How do I install Stat Research Orchestrator in Codex?
Run `npx skills add aiming-lab/AutoResearchClaw --skill stat-research-orchestrator -a codex`. Or copy the skill folder (external/agents/stat_research_agent/skills/stat-research-orchestrator in aiming-lab/AutoResearchClaw) into .agents/skills/stat-research-orchestrator in your project. Codex loads it when a task matches its description.
Can I use Stat Research Orchestrator in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add aiming-lab/AutoResearchClaw --skill stat-research-orchestrator -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/stat-research-orchestrator, .gemini/skills/stat-research-orchestrator, .github/skills/stat-research-orchestrator and .opencode/skills/stat-research-orchestrator in your project.
What does Stat Research Orchestrator need to run?
SKILL.md names no scripts, command-line tools or credentials: Stat Research Orchestrator is instructions for the agent only.
Does Stat Research Orchestrator access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Stat Research Orchestrator safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Stat Research Orchestrator use?
Stat Research Orchestrator is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Stat Research Orchestrator use?
About 1.5k tokens (SKILL.md is roughly 6.1k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Stat Research Orchestrator?
Skills that share tags, products or a category with Stat Research Orchestrator: Video Template Frame Pentagram Stat (nexu-io/open-design, 100k stars), Statistical Power (K-Dense-AI/scientific-agent-skills, 48k stars), Team Agent Orchestration (affaan-m/ECC, 276k stars) and Orca Orchestration (stablyai/orca, 88k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Stat Research Orchestrator?
aiming-lab (a GitHub organization) maintains it in aiming-lab/AutoResearchClaw, which has 14,602 GitHub stars. The repository holds 34 skills in this directory. The repository was last updated on August 19, 2026.
Source: aiming-lab/AutoResearchClaw on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.