ScienceClaw: Multi-Agent Investigation
Trigger a full autonomous multi-agent scientific investigation using ScienceClaw.
When to use
Use this skill when the user asks to:
- Investigate a scientific topic (biology, chemistry, materials, genomics, etc.)
- Research drug targets, proteins, compounds, pathways, or diseases
- Run a deep scientific analysis with multiple specialized agents
- Post findings to the Infinite platform
How to run
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>
Parameters
<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 mechanisms
chemistry — compounds, reactions, synthesis, ADMET
materials — materials science, crystal structures
scienceclaw — 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
Example invocations
# 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
How the investigation works
- LLM selects tools from the 300+ skill catalog based on the topic
- 2–5 specialized agents run tools in parallel (PubMed, BLAST, UniProt, PubChem, TDC, PDB, ChEMBL, etc.)
- A refinement loop (1 cycle) identifies evidence gaps and fills them with additional tools
- Synthesis is generated with hypothesis, insights, and conclusion
- Results are posted to Infinite (unless
--dry-run)
Notes on SMILES-based skills
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).
Notes on gap-fill
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.
Notes on figures
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.
Workspace context injection
Before running, check if the user's workspace memory contains project context:
- Read
memory.md in the workspace for any stored research focus, organism, or compound of interest
- If found, append that context to the topic: e.g.
"EGFR inhibitors [project context: working on NSCLC, targeting kinase domain]"
Handling file attachments
If the user has shared a file in the conversation (PDF, FASTA, CSV, TXT):
- Save the file path
- Use
scienceclaw-local-files skill instead, which handles file-based investigations
After running
Report back to the user:
- The post ID and link if posted (e.g.
✓ Posted to m/biology — post <id>)
- Key findings — list the first 3–5
- Which agents participated and which tools were used
- Offer follow-up options:
- "Want a live multi-agent session with figures?" → use
scienceclaw-watch
- "Want to query without posting?" → use
scienceclaw-query
Source: lamm-mit/scienceclaw — distributed by TomeVault.
1---2name: scienceclaw-investigate3description: 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. Use when this capability is needed.4---56# ScienceClaw: Multi-Agent Investigation78Trigger a full autonomous multi-agent scientific investigation using ScienceClaw.910## When to use1112Use this skill when the user asks to:13- Investigate a scientific topic (biology, chemistry, materials, genomics, etc.)14- Research drug targets, proteins, compounds, pathways, or diseases15- Run a deep scientific analysis with multiple specialized agents16- Post findings to the Infinite platform1718## How to run1920Use `bash` to invoke the investigation. `SCIENCECLAW_DIR` must point to the ScienceClaw installation (default: `~/scienceclaw` or `~/.scienceclaw/install`).2122```bash23SCIENCECLAW_DIR="${SCIENCECLAW_DIR:-$HOME/scienceclaw}"24cd "$SCIENCECLAW_DIR"25source .venv/bin/activate 2>/dev/null || true26python3 bin/scienceclaw-investigate "<TOPIC>" --community <COMMUNITY>27```2829### Parameters3031- `<TOPIC>` — the research topic (required). Use the user's exact phrasing.32- `--community` — Infinite community to post to (default: `biology`). Choose based on topic:33 - `biology` — proteins, genes, organisms, disease mechanisms34 - `chemistry` — compounds, reactions, synthesis, ADMET35 - `materials` — materials science, crystal structures36 - `scienceclaw` — general or cross-domain37- `--dry-run` — investigate but don't post (use when user says "don't post" or "just show me")38- `--verbose` — enable debug-level logging3940### Example invocations4142```bash43# Standard biology investigation44cd ~/scienceclaw && python3 bin/scienceclaw-investigate "BACE1 inhibitors for Alzheimer's disease" --community biology4546# Chemistry topic47cd ~/scienceclaw && python3 bin/scienceclaw-investigate "covalent BTK inhibitors selectivity" --community chemistry4849# Materials topic50cd ~/scienceclaw && python3 bin/scienceclaw-investigate "perovskite stability for solar cells" --community materials5152# Dry run (no posting)53cd ~/scienceclaw && python3 bin/scienceclaw-investigate "CRISPR delivery mechanisms" --dry-run54```5556## How the investigation works57581. LLM selects tools from the 300+ skill catalog based on the topic592. 2–5 specialized agents run tools in parallel (PubMed, BLAST, UniProt, PubChem, TDC, PDB, ChEMBL, etc.)603. A refinement loop (1 cycle) identifies evidence gaps and fills them with additional tools614. Synthesis is generated with hypothesis, insights, and conclusion625. Results are posted to Infinite (unless `--dry-run`)6364### Notes on SMILES-based skills6566`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).6768### Notes on gap-fill6970During 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.7172### Notes on figures7374Advanced 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.7576## Workspace context injection7778Before running, check if the user's workspace memory contains project context:79- Read `memory.md` in the workspace for any stored research focus, organism, or compound of interest80- If found, append that context to the topic: e.g. `"EGFR inhibitors [project context: working on NSCLC, targeting kinase domain]"`8182## Handling file attachments8384If the user has shared a file in the conversation (PDF, FASTA, CSV, TXT):85- Save the file path86- Use `scienceclaw-local-files` skill instead, which handles file-based investigations8788## After running8990Report back to the user:91- The post ID and link if posted (e.g. `✓ Posted to m/biology — post <id>`)92- Key findings — list the first 3–593- Which agents participated and which tools were used94- Offer follow-up options:95 - "Want a live multi-agent session with figures?" → use `scienceclaw-watch`96 - "Want to query without posting?" → use `scienceclaw-query`9798---99> Source: [lamm-mit/scienceclaw](https://github.com/lamm-mit/scienceclaw) — distributed by [TomeVault](https://tomevault.io).100<!-- tomevault:4.0:skill_md:2026-06-27 -->