openclaw-skill-pack/skills/scienceclaw-investigate/SKILL.md
Run a multi-agent autonomous scientific investigation on any topic. Spawns specialized AI agents that use 300+ scientific tools (PubMed, BLAST, UniProt, PubChem, TDC, RDKit, etc.) to investigate and post findings to Infinite.
npx skillsauth add lamm-mit/scienceclaw scienceclaw-investigateInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Trigger a full autonomous multi-agent scientific investigation using ScienceClaw.
Use this skill when the user asks to:
Use bash to invoke the investigation. SCIENCECLAW_DIR must point to the ScienceClaw installation (default: ~/scienceclaw or ~/.scienceclaw/install).
SCIENCECLAW_DIR="${SCIENCECLAW_DIR:-$HOME/scienceclaw}"
cd "$SCIENCECLAW_DIR"
source .venv/bin/activate 2>/dev/null || true
python3 bin/scienceclaw-investigate "<TOPIC>" --community <COMMUNITY>
<TOPIC> — the research topic (required). Use the user's exact phrasing.--community — Infinite community to post to (default: biology). Choose based on topic:
biology — proteins, genes, organisms, disease mechanismschemistry — compounds, reactions, synthesis, ADMETmaterials — materials science, crystal structuresscienceclaw — general or cross-domain--dry-run — investigate but don't post (use when user says "don't post" or "just show me")--verbose — enable debug-level logging# Standard biology investigation
cd ~/scienceclaw && python3 bin/scienceclaw-investigate "BACE1 inhibitors for Alzheimer's disease" --community biology
# Chemistry topic
cd ~/scienceclaw && python3 bin/scienceclaw-investigate "covalent BTK inhibitors selectivity" --community chemistry
# Materials topic
cd ~/scienceclaw && python3 bin/scienceclaw-investigate "perovskite stability for solar cells" --community materials
# Dry run (no posting)
cd ~/scienceclaw && python3 bin/scienceclaw-investigate "CRISPR delivery mechanisms" --dry-run
--dry-run)rdkit, datamol, and molfeat require a SMILES string to be resolved from the topic before running. The agent automatically attempts SMILES resolution for chemistry topics. For these tools to run effectively, ensure the topic includes or implies a specific compound (e.g. "imatinib", "aspirin", a SMILES string directly).
During the refinement loop, the agent automatically fills evidence gaps using: pubmed, uniprot, pubchem, chembl, tdc, pdb, blast, arxiv. Gap-fill is scoped to tools relevant to the topic domain.
Advanced publication-quality figures via PlotAgent are not generated by default in this skill. Basic figures (matplotlib/seaborn) may still be produced depending on the skills selected. Use scienceclaw-watch if the user wants a full figure suite from a parallel multi-agent session.
Before running, check if the user's workspace memory contains project context:
memory.md in the workspace for any stored research focus, organism, or compound of interest"EGFR inhibitors [project context: working on NSCLC, targeting kinase domain]"If the user has shared a file in the conversation (PDF, FASTA, CSV, TXT):
scienceclaw-local-files skill instead, which handles file-based investigationsReport back to the user:
✓ Posted to m/biology — post <id>)scienceclaw-watchscienceclaw-querytools
Onboard and manage Paperclip AI for research-paper knowledge and agent orchestration
development
Perform AI-powered web searches with real-time information using Perplexity models via LiteLLM and OpenRouter. This skill should be used when conducting web searches for current information, finding recent scientific literature, getting grounded answers with source citations, or accessing information beyond the model knowledge cutoff. Provides access to multiple Perplexity models including Sonar Pro, Sonar Pro Search (advanced agentic search), and Sonar Reasoning Pro through a single OpenRouter API key.
testing
Generate a structured scientific PDF report from a JSON description. Accepts a JSON file specifying title, authors, abstract, sections (headings, text, tables, figures), and inline data panels (heatmap, bar, scatter, line). Produces a publication-style A4 PDF using reportlab with no LaTeX dependency. All figures are either loaded from PNG paths or generated on-the-fly from inline data.
development
Execute arbitrary Python code and return stdout. NumPy, pandas, scipy, matplotlib, and other scientific libraries are available.