AutoResearchClaw — Autonomous Research Pipeline Skill by ara.so — Daily 2026 Skills collection. AutoResearchClaw is a fully autonomous 23-stage research pipeline that takes a natural language topic and produces a complete academic paper: real arXiv/Semantic Scholar citations, sandboxed experiments, statistical analysis, multi-agent peer review, and conference-ready LaTeX (NeurIPS/ICML/ICLR). No hallucinated references. No human babysitting. Installation

Clone and install

git clone https://github.com/aiming-lab/AutoResearchClaw.git cd AutoResearchClaw python3 -m venv .venv && source .venv/bin/activate pip install -e .

Verify CLI is available

researchclaw --help Requirements: Python 3.11+ Configuration cp config.researchclaw.example.yaml config.arc.yaml Minimum config ( config.arc.yaml ) project : name : "my-research" research : topic : "Your research topic here" llm : provider : "openai" base_url : "https://api.openai.com/v1" api_key_env : "OPENAI_API_KEY" primary_model : "gpt-4o" fallback_models : [ "gpt-4o-mini" ] experiment : mode : "sandbox" sandbox : python_path : ".venv/bin/python" export OPENAI_API_KEY = " $YOUR_OPENAI_KEY " OpenRouter config (200+ models) llm : provider : "openrouter" api_key_env : "OPENROUTER_API_KEY" primary_model : "anthropic/claude-3.5-sonnet" fallback_models : - "google/gemini-pro-1.5" - "meta-llama/llama-3.1-70b-instruct" export OPENROUTER_API_KEY = " $YOUR_OPENROUTER_KEY " ACP (Agent Client Protocol) — no API key needed llm : provider : "acp" acp : agent : "claude"

or: codex, gemini, opencode, kimi

cwd : "." The agent CLI (e.g. claude ) handles its own authentication. OpenClaw bridge (optional advanced capabilities) openclaw_bridge : use_cron : true

Scheduled research runs

use_message : true

Progress notifications

use_memory : true

Cross-session knowledge persistence

use_sessions_spawn : true

Parallel sub-sessions

use_web_fetch : true

Live web search in literature review

use_browser : false

Browser-based paper collection

Key CLI Commands

Basic run — fully autonomous, no prompts

researchclaw run --topic "Your research idea" --auto-approve

Run with explicit config file

researchclaw run --config config.arc.yaml --topic "Mixture-of-experts routing efficiency" --auto-approve

Run with topic defined in config (omit --topic flag)

researchclaw run --config config.arc.yaml --auto-approve

Interactive mode — pauses at gate stages for approval

researchclaw run --config config.arc.yaml --topic "Your topic"

Check pipeline status / resume a run

researchclaw status --run-id rc-20260315-120000-abc123

List past runs

researchclaw list Gate stages (5, 9, 20) pause for human approval in interactive mode. Pass --auto-approve to skip all gates. Python API from researchclaw . pipeline import Runner from researchclaw . config import load_config

Load config and run

config

load_config ( "config.arc.yaml" ) config . research . topic = "Efficient attention mechanisms for long-context LLMs" config . auto_approve = True runner = Runner ( config ) result = runner . run ( )

Access outputs

print ( result . artifact_dir )

artifacts/rc-YYYYMMDD-HHMMSS-/

print ( result . deliverables_dir )

.../deliverables/

print ( result . paper_draft_path )

.../deliverables/paper_draft.md

print ( result . latex_path )

.../deliverables/paper.tex

print ( result . bibtex_path )

.../deliverables/references.bib

print ( result . verification_report )

.../deliverables/verification_report.json

Run specific stages only

from researchclaw . pipeline import Runner , StageRange runner = Runner ( config ) result = runner . run ( stages = StageRange ( start = "LITERATURE_COLLECT" , end = "KNOWLEDGE_EXTRACT" ) )

Access knowledge base after a run

from researchclaw . knowledge import KnowledgeBase kb = KnowledgeBase . load ( result . artifact_dir ) findings = kb . get ( "findings" ) literature = kb . get ( "literature" ) decisions = kb . get ( "decisions" ) Output Structure After a run, all outputs land in artifacts/rc-YYYYMMDD-HHMMSS-/ : artifacts/rc-20260315-120000-abc123/ ├── deliverables/ │ ├── paper_draft.md # Full academic paper (Markdown) │ ├── paper.tex # Conference-ready LaTeX │ ├── references.bib # Real BibTeX — auto-pruned to inline citations │ ├── verification_report.json # 4-layer citation integrity report │ └── reviews.md # Multi-agent peer review ├── experiment_runs/ │ ├── run_001/ │ │ ├── code/ # Generated experiment code │ │ ├── results.json # Structured metrics │ │ └── sandbox_output.txt # Execution logs ├── charts/ │ └── *.png # Auto-generated comparison charts ├── evolution/ │ └── lessons.json # Self-learning lessons for future runs └── knowledge_base/ ├── decisions.json ├── experiments.json ├── findings.json ├── literature.json ├── questions.json └── reviews.json Pipeline Stages Reference Phase Stage # Name Notes A 1 TOPIC_INIT Parse and scope research topic A 2 PROBLEM_DECOMPOSE Break into sub-problems B 3 SEARCH_STRATEGY Build search queries B 4 LITERATURE_COLLECT Real API calls to arXiv + Semantic Scholar B 5 LITERATURE_SCREEN Gate — approve/reject literature B 6 KNOWLEDGE_EXTRACT Extract structured knowledge C 7 SYNTHESIS Synthesize findings C 8 HYPOTHESIS_GEN Multi-agent debate to form hypotheses D 9 EXPERIMENT_DESIGN Gate — approve/reject design D 10 CODE_GENERATION Generate experiment code D 11 RESOURCE_PLANNING GPU/MPS/CPU auto-detection E 12 EXPERIMENT_RUN Sandboxed execution E 13 ITERATIVE_REFINE Self-healing on failure F 14 RESULT_ANALYSIS Multi-agent analysis F 15 RESEARCH_DECISION PROCEED / REFINE / PIVOT G 16 PAPER_OUTLINE Structure paper G 17 PAPER_DRAFT Write full paper G 18 PEER_REVIEW Evidence-consistency check G 19 PAPER_REVISION Incorporate review feedback H 20 QUALITY_GATE Gate — final approval H 21 KNOWLEDGE_ARCHIVE Save lessons to KB H 22 EXPORT_PUBLISH Emit LaTeX + BibTeX H 23 CITATION_VERIFY 4-layer anti-hallucination check Common Patterns Pattern: Quick paper on a topic export OPENAI_API_KEY = " $OPENAI_API_KEY " researchclaw run \ --topic "Self-supervised learning for protein structure prediction" \ --auto-approve Pattern: Reproducible run with full config

config.arc.yaml

project : name : "protein-ssl-research" research : topic : "Self-supervised learning for protein structure prediction" llm : provider : "openai" api_key_env : "OPENAI_API_KEY" primary_model : "gpt-4o" fallback_models : [ "gpt-4o-mini" ] experiment : mode : "sandbox" sandbox : python_path : ".venv/bin/python" max_iterations : 3 timeout_seconds : 300 researchclaw run --config config.arc.yaml --auto-approve Pattern: Use Claude via OpenRouter for best reasoning export OPENROUTER_API_KEY = " $OPENROUTER_API_KEY " cat

config.arc.yaml << 'EOF' project: name: "my-research" llm: provider: "openrouter" api_key_env: "OPENROUTER_API_KEY" primary_model: "anthropic/claude-3.5-sonnet" fallback_models: ["google/gemini-pro-1.5"] experiment: mode: "sandbox" sandbox: python_path: ".venv/bin/python" EOF researchclaw run --config config.arc.yaml \ --topic "Efficient KV cache compression for transformer inference" \ --auto-approve Pattern: Resume after a failed run

List runs to find the run ID

researchclaw list

Resume from last completed stage

researchclaw run --resume rc-20260315-120000-abc123 Pattern: Programmatic batch research import asyncio from researchclaw . pipeline import Runner from researchclaw . config import load_config topics = [ "LoRA fine-tuning on limited hardware" , "Speculative decoding for LLM inference" , "Flash attention variants comparison" , ] config = load_config ( "config.arc.yaml" ) config . auto_approve = True for topic in topics : config . research . topic = topic runner = Runner ( config ) result = runner . run ( ) print ( f"[ { topic } ] → { result . deliverables_dir } " ) Pattern: OpenClaw one-liner (if using OpenClaw agent) Share the repo URL with OpenClaw, then say: "Research mixture-of-experts routing efficiency" OpenClaw auto-reads RESEARCHCLAW_AGENTS.md , clones, installs, configures, and runs the full pipeline. Compile the LaTeX Output

Navigate to deliverables

cd artifacts/rc-*/deliverables/

Compile (requires a LaTeX distribution)

pdflatex paper.tex bibtex paper pdflatex paper.tex pdflatex paper.tex

Or upload paper.tex + references.bib directly to Overleaf

Troubleshooting researchclaw: command not found

Make sure the venv is active and package is installed

source .venv/bin/activate pip install -e . which researchclaw API key errors

Verify env var is set

echo $OPENAI_API_KEY

Should print your key (not empty)

Set it explicitly for the session

export OPENAI_API_KEY = "sk-..." Experiment sandbox failures The pipeline self-heals at Stage 13 (ITERATIVE_REFINE). If it keeps failing:

Increase timeout and iterations in config

experiment : max_iterations : 5 timeout_seconds : 600 sandbox : python_path : ".venv/bin/python" Citation hallucination warnings Stage 23 (CITATION_VERIFY) runs a 4-layer check. If references are pruned: This is expected behaviour — fake citations are removed automatically Check verification_report.json for details on which citations were rejected and why PIVOT loop running indefinitely Stage 15 (RESEARCH_DECISION) may pivot multiple times. To cap iterations: research : max_pivots : 2 max_refines : 3 LaTeX compilation errors

Check for missing packages

pdflatex paper.tex 2

&1 | grep "File.*not found"

Install missing packages (TeX Live)

tlmgr install < package-name

Out of memory during experiments

Force CPU mode in config

experiment

:

sandbox

:

device

:

"cpu"

max_memory_gb

:

4

Key Concepts

PIVOT/REFINE Loop

Stage 15 autonomously decides PROCEED, REFINE (tweak params), or PIVOT (new hypothesis direction). All artifacts are versioned.

Multi-Agent Debate

Stages 8, 14, 18 use structured multi-perspective debate — not a single LLM pass.

Self-Learning

Each run extracts lessons with 30-day time decay. Future runs on similar topics benefit from past mistakes.

Sentinel Watchdog

Background monitor detects NaN/Inf in results, checks paper-evidence consistency, scores citation relevance, and guards against fabrication throughout the run.

4-Layer Citation Verification

arXiv lookup → CrossRef lookup → DataCite lookup → LLM relevance scoring. A citation must pass all layers to survive.