---
id: daily-autoresearchclaw-autonomous-research
name: "autoresearchclaw-autonomous-research"
url: https://skills.yangsir.net/skill/daily-autoresearchclaw-autonomous-research
author: aradotso
domain: science
tags: ["research", "academic-writing", "automation", "literature-review", "citations"]
install_count: 1400
rating: 4.30 (26 reviews)
github: https://github.com/aradotso/trending-skills
---

# autoresearchclaw-autonomous-research

> 全自动 23 阶段研究管道，从自然语言主题到完整学术论文，包含真实引用和数据分析

**Stats**: 1,400 installs · 4.3/5 (26 reviews)

## Before / After 对比

### 学术研究效率

**Before**:

手动搜索文献、阅读论文、整理引用、撰写草稿，一篇论文需要数月甚至更长时间

**After**:

全自动研究流程，从选题到完整论文只需数天，引用真实文献且格式规范

| Metric | Before | After | Change |
|---|---|---|---|
| 研究周期 | 90days | 5days | -94% |
| 引用准确性 | 70% | 95% | +36% |

## Readme

# autoresearchclaw-autonomous-research

# AutoResearchClaw — Autonomous Research Pipeline

Skill by [ara.so](https://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-<hash>/
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-<hash>/`:

```
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.

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---
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