[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6K0E9ZWFTG44SK6G2ARS39Z":3,"skill-same:01M6K0E9ZWFTG44SK6G2ARS39Z":1271},{"skillNo":4,"name":5,"nameZh":6,"description":6,"descriptionZh":6,"repositoryFullName":7,"ownerLogin":8,"ownerAvatarUrl":9,"ownerAccountNo":10,"ownerDisplayName":6,"repositoryNo":11,"starsCount":12,"licenseSpdxId":13,"licenseFileUrl":6,"sourceHtmlUrl":14,"categories":15,"keywords":27,"publishedAt":28,"linkOnly":29,"markdownContent":30,"markdownBody":30,"frontMatter":31,"refName":32,"commitSha":33,"skillDirectory":34,"forksCount":12,"repositoryHtmlUrl":35,"repositoryHomepageUrl":6,"ownerHtmlUrl":36,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":37,"install":38,"resources":51},"01M6K0E9ZWFTG44SK6G2ARS39Z","autoresearchclaw",null,"sigitpoerwo\u002Frepoworkspace_zahra","sigitpoerwo","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F260207224?v=4","01M6K000PB6NA0N6SQ7VFMEAFZ","01M6K000V4N0CEHNPPQA9TNQNX",0,"MIT","https:\u002F\u002Fgithub.com\u002Fsigitpoerwo\u002Frepoworkspace_zahra\u002Fblob\u002Fmain\u002Fskills\u002F05-CUSTOM-SKILLS\u002Fautoresearchclaw\u002FSKILL.md",[16,21,24],{"categoryNo":17,"categoryName":18,"taxonomyType":19,"taxonomyTypeDesc":20},"01JBSEEDCAT000000000000008","测试与质量","SCENARIO","使用场景",{"categoryNo":22,"categoryName":23,"taxonomyType":19,"taxonomyTypeDesc":20},"01JBSEEDCAT000000000000021","资料检索与研究",{"categoryNo":25,"categoryName":26,"taxonomyType":19,"taxonomyTypeDesc":20},"01JBSEEDCAT000000000000023","Skill与智能体开发",[5],"2026-11-10 10:12:30",false,"# AutoResearchClaw - Autonomous Research Pipeline\n\n**Version:** 0.3.1  \n**Category:** Research & Academic Writing  \n**Source:** https:\u002F\u002Fgithub.com\u002Faiming-lab\u002FAutoResearchClaw  \n**Installed:** 2026-03-23\n\n---\n\n## ? Overview\n\nAutoResearchClaw adalah sistem pipeline riset otomatis yang mengubah ide riset menjadi paper akademik lengkap tanpa intervensi manusia. Sistem ini menggunakan 23 stage dalam 8 fase dengan multi-agent architecture, self-learning, dan hardware-aware execution.\n\n**Key Capabilities:**\n- ✅ 23-stage autonomous pipeline (Topic Init → Citation Verify)\n- ✅ Multi-agent architecture (CodeAgent, BenchmarkAgent, FigureAgent)\n- ✅ Self-learning via MetaClaw (+18.3% robustness)\n- ✅ Hardware-aware execution (CUDA\u002FMPS\u002FCPU auto-detect)\n- ✅ Real literature integration (OpenAlex, Semantic Scholar, arXiv)\n- ✅ Conference-ready LaTeX (NeurIPS\u002FICML\u002FICLR)\n- ✅ 4-layer citation verification\n- ✅ OpenClaw compatible\n\n---\n\n## ? Use Cases\n\n### 1. Academic Research Paper Generation\n```bash\n# Generate full research paper from topic\nresearchclaw run --topic \"Graph neural networks for drug discovery\" --auto-approve\n```\n\n### 2. Literature Review Automation\n```bash\n# Collect and synthesize literature\nresearchclaw run --from-stage SEARCH_STRATEGY --topic \"Transformer attention mechanisms\"\n```\n\n### 3. Experiment Design & Execution\n```bash\n# Design and run experiments\nresearchclaw run --from-stage EXPERIMENT_DESIGN --config config.yaml\n```\n\n### 4. Paper Writing & Review\n```bash\n# Generate paper from existing experiments\nresearchclaw run --from-stage PAPER_OUTLINE --auto-approve\n```\n\n---\n\n## ?️ Architecture\n\n### Pipeline Stages (23 Total)\n\n```\nPhase A: Research Scoping (1-2)\n├── 1. TOPIC_INIT: Formulate SMART goal + hardware detection\n└── 2. PROBLEM_DECOMPOSE: Break into sub-questions\n\nPhase B: Literature Discovery (3-6)\n├── 3. SEARCH_STRATEGY: Plan queries & sources\n├── 4. LITERATURE_COLLECT: Real API calls (arXiv → Semantic Scholar)\n├── 5. LITERATURE_SCREEN: [GATE] Filter by relevance\n└── 6. KNOWLEDGE_EXTRACT: Extract structured knowledge cards\n\nPhase C: Knowledge Synthesis (7-8)\n├── 7. SYNTHESIS: Cluster findings, identify gaps\n└── 8. HYPOTHESIS_GEN: Generate falsifiable hypotheses\n\nPhase D: Experiment Design (9-11)\n├── 9. EXPERIMENT_DESIGN: [GATE] Design plan with baselines\n├── 10. CODE_GENERATION: Hardware-aware Python code\n└── 11. RESOURCE_PLANNING: Estimate GPU\u002Ftime requirements\n\nPhase E: Experiment Execution (12-13)\n├── 12. EXPERIMENT_RUN: Execute in sandbox\u002Fdocker\n└── 13. ITERATIVE_REFINE: Self-healing code repair (max 10 iterations)\n\nPhase F: Analysis & Decision (14-15)\n├── 14. RESULT_ANALYSIS: Statistical analysis\n└── 15. RESEARCH_DECISION: PROCEED\u002FPIVOT\u002FREFINE decision\n\nPhase G: Paper Writing (16-19)\n├── 16. PAPER_OUTLINE: Section-level outline\n├── 17. PAPER_DRAFT: 5,000-6,500 words, anti-fabrication guard\n├── 18. PEER_REVIEW: Multi-agent review with NeurIPS rubric\n└── 19. PAPER_REVISION: Address reviews with length guard\n\nPhase H: Finalization (20-23)\n├── 20. QUALITY_GATE: [GATE] Quality check\n├── 21. KNOWLEDGE_ARCHIVE: Save retrospective\n├── 22. EXPORT_PUBLISH: Generate LaTeX + charts + code\n└── 23. CITATION_VERIFY: 4-layer verification\n```\n\n### Multi-Agent Subsystems\n\n**CodeAgent** (`researchclaw\u002Fpipeline\u002Fcode_agent.py`)\n- Generate & repair experiment code\n- AST validation & import whitelist\n- Hardware-aware package selection\n- Self-healing repair (max 5 attempts)\n- OpenCode Beast Mode integration\n\n**BenchmarkAgent** (`researchclaw\u002Fagents\u002Fbenchmark_agent\u002F`)\n- Surveyor: Identify relevant benchmarks\n- Acquirer: Fetch benchmark data\u002Fcode\n- Selector: Choose appropriate benchmarks\n- Validator: Verify benchmark integrity\n- Orchestrator: Coordinate workflow\n\n**FigureAgent** (`researchclaw\u002Fagents\u002Ffigure_agent\u002F`)\n- Decision: Analyze paper → decide figures needed\n- Planner: Design figure specifications\n- CodeGen: Generate matplotlib\u002Fseaborn code\n- Renderer: Execute code → produce images\n- Critic: Quality check (3 iteration max)\n- NanoBanana: Gemini-based image generation\n- Integrator: Combine into manifest\n\n---\n\n## ? Quick Start\n\n### Installation\n\n```bash\n# Clone repository\ngit clone https:\u002F\u002Fgithub.com\u002Faiming-lab\u002FAutoResearchClaw.git\ncd AutoResearchClaw\n\n# Create virtual environment\npython3 -m venv .venv\nsource .venv\u002Fbin\u002Factivate  # Linux\u002FMac\n# .venv\\Scripts\\activate   # Windows\n\n# Install\npip install -e .\n\n# Setup (interactive)\nresearchclaw setup\n\n# Initialize config\nresearchclaw init\n```\n\n### Basic Configuration\n\n```yaml\n# config.arc.yaml (minimum required)\nproject:\n  name: \"my-research\"\n\nresearch:\n  topic: \"Your research topic here\"\n\nllm:\n  base_url: \"https:\u002F\u002Fapi.openai.com\u002Fv1\"\n  api_key_env: \"OPENAI_API_KEY\"\n  primary_model: \"gpt-4o\"\n  fallback_models: [\"gpt-4o-mini\"]\n\nexperiment:\n  mode: \"sandbox\"\n  sandbox:\n    python_path: \".venv\u002Fbin\u002Fpython\"\n```\n\n### Run Pipeline\n\n```bash\n# Set API key\nexport OPENAI_API_KEY=\"sk-...\"\n\n# Run full pipeline\nresearchclaw run --config config.arc.yaml --topic \"Your research idea\" --auto-approve\n\n# Resume from checkpoint\nresearchclaw run --config config.arc.yaml --resume --auto-approve\n\n# Start from specific stage\nresearchclaw run --config config.arc.yaml --from-stage PAPER_OUTLINE --auto-approve\n```\n\n---\n\n## ? Key Features\n\n### 1. MetaClaw Integration (Self-Learning)\n\n**Concept:** Pipeline learns from every run — failures → lessons → skills\n\n```yaml\n# Enable in config.arc.yaml\nmetaclaw_bridge:\n  enabled: true\n  proxy_url: \"http:\u002F\u002Flocalhost:30000\"\n  skills_dir: \"~\u002F.metaclaw\u002Fskills\"\n  lesson_to_skill:\n    enabled: true\n    min_severity: \"warning\"\n    max_skills_per_run: 3\n```\n\n**Results:**\n- Stage retry rate: -24.8%\n- Refine cycle count: -40.0%\n- Pipeline completion: +5.3%\n- Overall robustness: +18.3%\n\n### 2. Hardware-Aware Execution\n\n**Auto-detection:**\n```python\n# Stage 1 detects GPU hardware\nif torch.cuda.is_available():\n    tier = \"high\"  # Full PyTorch with GPU\nelif torch.backends.mps.is_available():\n    tier = \"limited\"  # Lightweight experiments\nelse:\n    tier = \"none\"  # NumPy\u002Fsklearn only\n```\n\n**Adaptive Code Generation:**\n- High-tier GPU: Full PyTorch with GPU acceleration\n- Limited GPU: Lightweight (\u003C1M params, \u003C=20 epochs)\n- CPU-only: NumPy\u002Fsklearn, no deep learning\n\n### 3. OpenCode Beast Mode\n\n**Complex experiments auto-routed to OpenCode:**\n\n```yaml\n# config.arc.yaml\nexperiment:\n  opencode:\n    enabled: true\n    auto: true\n    complexity_threshold: 0.2\n    timeout_sec: 600\n```\n\n**Triggers:**\n- Multi-model comparison\n- Custom architectures\n- Ablation studies\n- Large-scale experiments\n\n### 4. Real Literature Integration\n\n**Multi-source search:**\n1. arXiv (primary)\n2. Semantic Scholar (fallback)\n3. OpenAlex (metadata enrichment)\n\n**4-layer citation verification:**\n1. arXiv ID check\n2. CrossRef\u002FDataCite DOI\n3. Semantic Scholar title match\n4. LLM relevance scoring\n\n### 5. Self-Healing Execution\n\n**Sandbox safety:**\n- AST validation (no eval\u002Fexec\u002Fsubprocess)\n- Import whitelist\n- Memory limit & timeout\n- NaN\u002FInf fast-fail\n- Partial result capture\n\n**Auto-repair:**\n```python\nfor attempt in range(max_retries):\n    result = run_experiment(code)\n    if result.success:\n        return result\n    issues = validate_code(code)\n    code = llm.repair(code, issues)\n```\n\n---\n\n## ? Output Artifacts\n\n**Deliverables:** `artifacts\u002Frc-YYYYMMDD-HHMMSS-\u003Chash>\u002Fdeliverables\u002F`\n\n| File | Description |\n|------|-------------|\n| `paper.tex` | Conference-ready LaTeX |\n| `paper_final.md` | Markdown version |\n| `references.bib` | Verified BibTeX |\n| `verification_report.json` | Citation fact-check |\n| `charts\u002F` | Auto-generated figures |\n| `code\u002Fexperiment.py` | Final experiment code |\n| `code\u002Frequirements.txt` | Dependencies |\n| `code\u002FREADME.md` | Reproduction guide |\n\n---\n\n## ? Experiment Modes\n\n### 1. Simulated (Default)\n```yaml\nexperiment:\n  mode: \"simulated\"\n```\n- LLM generates synthetic results\n- Fast, no setup required\n- Not real experiments\n\n### 2. Sandbox\n```yaml\nexperiment:\n  mode: \"sandbox\"\n  sandbox:\n    python_path: \".venv\u002Fbin\u002Fpython\"\n    gpu_required: false\n    max_memory_mb: 4096\n```\n- Real Python execution\n- Hardware-aware (GPU\u002FMPS\u002FCPU)\n- AST validation + import whitelist\n\n### 3. Docker\n```yaml\nexperiment:\n  mode: \"docker\"\n  docker:\n    image: \"researchclaw\u002Fexperiment:latest\"\n    gpu_enabled: true\n    network_policy: \"setup_only\"\n```\n- Isolated container execution\n- GPU passthrough (NVIDIA)\n- Network policies (none\u002Fsetup_only\u002Fpip_only\u002Ffull)\n- Pre-cached datasets\n\n### 4. SSH Remote\n```yaml\nexperiment:\n  mode: \"ssh_remote\"\n  ssh_remote:\n    host: \"gpu-server.example.com\"\n    gpu_ids: [0, 1]\n```\n- Execute on remote GPU server\n- Multi-GPU support\n\n---\n\n## ? Conference Templates\n\n**Supported:**\n- `neurips_2024`, `neurips_2025`\n- `iclr_2025`, `iclr_2026`\n- `icml_2025`, `icml_2026`\n\n```yaml\nexport:\n  target_conference: \"neurips_2025\"\n  authors: \"Anonymous\"\n  bib_file: \"references\"\n```\n\n---\n\n## ? Integration\n\n### OpenClaw Integration\n\n**Natural language interface:**\n```\nUser: \"Research graph neural networks for drug discovery\"\nOpenClaw: → clones repo → installs → runs pipeline → returns paper\n```\n\n**Bridge adapters:**\n```yaml\nopenclaw_bridge:\n  use_cron: true              # Scheduled runs\n  use_message: true           # Progress notifications\n  use_memory: true            # Cross-session persistence\n  use_sessions_spawn: true    # Parallel sub-sessions\n  use_web_fetch: true         # Live web search\n```\n\n### ACP (Agent Client Protocol)\n\n**Supported agents:**\n- Claude Code (`claude`)\n- Codex CLI (`codex`)\n- Copilot CLI (`gh`)\n- Gemini CLI (`gemini`)\n- OpenCode (`opencode`)\n\n```yaml\nllm:\n  provider: \"acp\"\n  acp:\n    agent: \"claude\"\n    cwd: \".\"\n```\n\n### Python API\n\n```python\nfrom researchclaw.pipeline import Runner\nfrom researchclaw.config import RCConfig\n\nconfig = RCConfig.from_yaml(\"config.yaml\")\nrunner = Runner(config)\nresult = runner.run()\n```\n\n---\n\n## ? Adoption Patterns for Zahra Workspace\n\n### 1. Multi-Agent Architecture\n\n**Break monolithic skills into specialized agents:**\n\n```\nResearch Agent (AutoResearchClaw-inspired)\n├── Literature Agent: Search + screen papers\n├── Experiment Agent: Design + execute + analyze\n├── Writing Agent: Outline + draft + revise\n└── Review Agent: Quality check + citation verify\n```\n\n### 2. Self-Learning System\n\n**Implement MetaClaw pattern:**\n\n```\n.ai\u002Fmemory\u002Flessons.json → extract failures\n                       ↓\n          LLM converts → skills\u002Farc-*\u002FSKILL.md\n                       ↓\n          Next run → inject skills into prompts\n```\n\n### 3. Stage-Based Workflow\n\n**Clear progression with rollback:**\n\n```python\n# Define stages\nstages = [\n    Stage.REQUIREMENTS,\n    Stage.ARCHITECTURE,\n    Stage.IMPLEMENTATION,\n    Stage.TESTING,\n    Stage.DEPLOYMENT,\n]\n\n# Each stage has:\n# - Clear input\u002Foutput artifacts\n# - Validation gates\n# - Rollback targets\n# - Evolution overlay\n```\n\n### 4. Hardware-Aware Execution\n\n**Adapt to available resources:**\n\n```python\nhw = detect_hardware()\n\nif hw.has_gpu:\n    prompt += \"Use PyTorch with GPU acceleration\"\nelse:\n    prompt += \"Use NumPy\u002Fsklearn only (CPU-only)\"\n```\n\n### 5. Quality Gates & Checkpoints\n\n**HITL gates for critical decisions:**\n\n```yaml\nsecurity:\n  hitl_required_stages: [3, 5, 6]  # Architecture, Testing, Deployment\n  allow_publish_without_approval: false\n```\n\n### 6. Prompt Externalization\n\n**Move prompts to YAML:**\n\n```yaml\n# .ai\u002Fprompts.yaml\nstages:\n  architecture_design:\n    system: \"You are a senior software architect...\"\n    user: \"Design architecture for: {requirements}\"\n    json_mode: true\n    max_tokens: 4000\n```\n\n---\n\n## ? Key Files & Modules\n\n### Core Pipeline\n- [`researchclaw\u002Fpipeline\u002Frunner.py`](researchclaw\u002Fpipeline\u002Frunner.py) - Main pipeline orchestrator\n- [`researchclaw\u002Fpipeline\u002Fstages.py`](researchclaw\u002Fpipeline\u002Fstages.py) - Stage definitions & state machine\n- [`researchclaw\u002Fpipeline\u002Fexecutor.py`](researchclaw\u002Fpipeline\u002Fexecutor.py) - Stage execution logic\n\n### Multi-Agent Systems\n- [`researchclaw\u002Fpipeline\u002Fcode_agent.py`](researchclaw\u002Fpipeline\u002Fcode_agent.py) - Code generation & repair\n- [`researchclaw\u002Fagents\u002Fbenchmark_agent\u002F`](researchclaw\u002Fagents\u002Fbenchmark_agent\u002F) - Benchmark selection\n- [`researchclaw\u002Fagents\u002Ffigure_agent\u002F`](researchclaw\u002Fagents\u002Ffigure_agent\u002F) - Figure generation\n\n### MetaClaw Integration\n- [`researchclaw\u002Fmetaclaw_bridge\u002Flesson_to_skill.py`](researchclaw\u002Fmetaclaw_bridge\u002Flesson_to_skill.py) - Lesson → Skill conversion\n- [`researchclaw\u002Fmetaclaw_bridge\u002Fstage_skill_map.py`](researchclaw\u002Fmetaclaw_bridge\u002Fstage_skill_map.py) - Stage → Skill mapping\n- [`researchclaw\u002Fevolution.py`](researchclaw\u002Fevolution.py) - Evolution store & lesson extraction\n\n### Literature & Citation\n- [`researchclaw\u002Fliterature\u002Fsearch.py`](researchclaw\u002Fliterature\u002Fsearch.py) - Multi-source literature search\n- [`researchclaw\u002Fliterature\u002Fverify.py`](researchclaw\u002Fliterature\u002Fverify.py) - 4-layer citation verification\n- [`researchclaw\u002Fliterature\u002Farxiv_client.py`](researchclaw\u002Fliterature\u002Farxiv_client.py) - arXiv API client\n- [`researchclaw\u002Fliterature\u002Fsemantic_scholar.py`](researchclaw\u002Fliterature\u002Fsemantic_scholar.py) - Semantic Scholar client\n\n### Configuration & Prompts\n- [`researchclaw\u002Fconfig.py`](researchclaw\u002Fconfig.py) - Configuration system\n- [`researchclaw\u002Fprompts.py`](researchclaw\u002Fprompts.py) - Prompt management & rendering\n- [`prompts.default.yaml`](prompts.default.yaml) - Default prompts (customizable)\n\n### Hardware & Execution\n- [`researchclaw\u002Fhardware.py`](researchclaw\u002Fhardware.py) - Hardware detection (CUDA\u002FMPS\u002FCPU)\n- [`researchclaw\u002Fexperiment\u002Fsandbox.py`](researchclaw\u002Fexperiment\u002Fsandbox.py) - Sandbox execution\n- [`researchclaw\u002Fexperiment\u002Fdocker_sandbox.py`](researchclaw\u002Fexperiment\u002Fdocker_sandbox.py) - Docker execution\n\n---\n\n## ? Testing\n\n**Test coverage:** 1,634 tests passed\n\n```bash\n# Run all tests\npytest tests\u002F\n\n# Run specific test suite\npytest tests\u002Ftest_rc_executor.py\npytest tests\u002Ftest_metaclaw_bridge\u002F\n\n# Run E2E tests\npytest tests\u002Fe2e_real_llm.py\n```\n\n---\n\n## ? Documentation\n\n- **Main README:** [`README.md`](README.md)\n- **Integration Guide:** [`docs\u002Fintegration-guide.md`](docs\u002Fintegration-guide.md)\n- **Paper Showcase:** [`docs\u002Fshowcase\u002FSHOWCASE.md`](docs\u002Fshowcase\u002FSHOWCASE.md)\n- **Tester Guide:** [`docs\u002FTESTER_GUIDE.md`](docs\u002FTESTER_GUIDE.md)\n- **Bug Tracker:** [`docs\u002FBUG_TRACKER.md`](docs\u002FBUG_TRACKER.md)\n- **Analysis (Bahasa):** [`ANALISIS_AUTORESEARCHCLAW.md`](ANALISIS_AUTORESEARCHCLAW.md)\n\n---\n\n## ? Resources\n\n- **Repository:** https:\u002F\u002Fgithub.com\u002Faiming-lab\u002FAutoResearchClaw\n- **Discord:** https:\u002F\u002Fdiscord.gg\u002Fu4ksqW5P\n- **License:** MIT\n\n---\n\n## ⚠️ Notes\n\n1. **LLM Dependency:** Requires OpenAI-compatible API (OpenAI, Azure, local proxy)\n2. **Cost:** Heavy LLM usage (23 stages × multiple calls per stage)\n3. **Execution Time:** 40-85 minutes per paper (typical)\n4. **Best For:** ML\u002FAI research (less tested for other domains)\n5. **Python Version:** Requires Python 3.11+\n\n---\n\n## ? Quick Commands\n\n```bash\n# Validate config\nresearchclaw validate --config config.yaml\n\n# Check environment\nresearchclaw doctor --config config.yaml\n\n# Generate report\nresearchclaw report --run-dir artifacts\u002Frc-20260322-161500-abc123\u002F\n\n# Resume from checkpoint\nresearchclaw run --resume --auto-approve\n\n# Start from specific stage\nresearchclaw run --from-stage PAPER_OUTLINE --auto-approve\n```\n\n---\n\n**Installed:** 2026-03-23  \n**Version:** 0.3.1  \n**Status:** ✅ Production Ready\n",{},"main","000d804ba753926ec0eca35be68bdb42da72c672","skills\u002F05-CUSTOM-SKILLS\u002Fautoresearchclaw","https:\u002F\u002Fgithub.com\u002Fsigitpoerwo\u002Frepoworkspace_zahra","https:\u002F\u002Fgithub.com\u002Fsigitpoerwo","2026-04-05 09:27:05",{"source":7,"skillName":5,"commands":39,"prompt":49,"zipAvailable":50},[40,43,46],{"tool":41,"command":42},"npx","npx skills add sigitpoerwo\u002Frepoworkspace_zahra --skill autoresearchclaw",{"tool":44,"command":45},"bunx","bunx skills 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Use this skill to scrape websites, automate web tasks, extract structured data, and perform browser automation using natural language. Works seamlessly with OpenClaw's MCP protocol.","https:\u002F\u002Fgithub.com\u002Fsigitpoerwo\u002Frepoworkspace_zahra\u002Fblob\u002Fmain\u002Fskills\u002F05-CUSTOM-SKILLS\u002Ftinyfish-openclaw\u002FSKILL.md",[1341],{"categoryNo":25,"categoryName":26,"taxonomyType":19,"taxonomyTypeDesc":20},[1343,1311,1344,1326,1345,1346,1334,1320,1324,1327,1328,1347,1348,1349,1302,1330,1332,1350,1351,1352],"tinyfish openclaw","openclaw","browser","for","integration","mcp protocol","protocol","structured data","browser automation","MCP",{"skillNo":1354,"name":1355,"nameZh":6,"description":6,"descriptionZh":6,"repositoryFullName":7,"ownerLogin":8,"ownerAvatarUrl":9,"ownerAccountNo":10,"ownerDisplayName":6,"repositoryNo":11,"starsCount":12,"licenseSpdxId":13,"licenseFileUrl":6,"sourceHtmlUrl":1356,"categories":1357,"keywords":1358,"publishedAt":28},"01M6K0E6ZAEJFYDXM4GANCKZY0","researchclaw","https:\u002F\u002Fgithub.com\u002Fsigitpoerwo\u002Frepoworkspace_zahra\u002Fblob\u002Fmain\u002Fskills\u002F05-CUSTOM-SKILLS\u002Fautoresearchclaw\u002F.claude\u002Fskills\u002Fresearchclaw\u002FSKILL.md",[],[1355],{"skillNo":1360,"name":1361,"nameZh":6,"description":1362,"descriptionZh":6,"repositoryFullName":7,"ownerLogin":8,"ownerAvatarUrl":9,"ownerAccountNo":10,"ownerDisplayName":6,"repositoryNo":11,"starsCount":12,"licenseSpdxId":13,"licenseFileUrl":6,"sourceHtmlUrl":1363,"categories":1364,"keywords":1366,"publishedAt":28},"01M6K0E1B6RZ8T2S7RMWQ6BVWW","academic-research-assistant","Research assistant for management & digital business academics - literature review, paper analysis, citation management, research methodology","https:\u002F\u002Fgithub.com\u002Fsigitpoerwo\u002Frepoworkspace_zahra\u002Fblob\u002Fmain\u002Fskills\u002F05-CUSTOM-SKILLS\u002Facademic-research-assistant\u002FSKILL.md",[1365],{"categoryNo":22,"categoryName":23,"taxonomyType":19,"taxonomyTypeDesc":20},[1367,1368,1369,1370,1371,1372,1373,1374,1375,1376,1377,1378,1379,1380,1381,1382,1346,1383],"Academic","academic research assistant","research assistant","research","assistant","review","Citation","paper analysis","research methodology","business","management","literature review","analysis","citation management","methodology","paper","literature",{"skillNo":1385,"name":1386,"nameZh":6,"description":1387,"descriptionZh":6,"repositoryFullName":7,"ownerLogin":8,"ownerAvatarUrl":9,"ownerAccountNo":10,"ownerDisplayName":6,"repositoryNo":11,"starsCount":12,"licenseSpdxId":13,"licenseFileUrl":6,"sourceHtmlUrl":1388,"categories":1389,"keywords":1390,"publishedAt":28},"01M6K0DX0JY6FXJWK0HT5PA277","zig-system-calls","Guides using bun.sys for system calls and file I\u002FO in Zig. Use when implementing file operations instead of std.fs or std.posix.","https:\u002F\u002Fgithub.com\u002Fsigitpoerwo\u002Frepoworkspace_zahra\u002Fblob\u002Fmain\u002Fskills\u002F03-BELUM-DICOBA\u002Fdari-clawhub\u002Fzig-system-calls\u002FSKILL.md",[],[1391,1392,1393,1394,1346,1395,1396,1397,1398,1324,1399,1400,1401,1402],"zig system calls","calls","system","zig","implementing","posix","guides","bun","operations","file operations","fs","file"]