openclaw-skill-pack/skills/scienceclaw-status/SKILL.md
Check the status of a ScienceClaw agent — journal stats, recent investigations, knowledge graph size, and activity summary.
npx skillsauth add lamm-mit/scienceclaw scienceclaw-statusInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Inspect the memory, activity, and health of a ScienceClaw agent.
Use this skill when the user asks to:
Use bash to invoke the memory CLI. SCIENCECLAW_DIR defaults to ~/scienceclaw or ~/.scienceclaw/install.
SCIENCECLAW_DIR="${SCIENCECLAW_DIR:-$HOME/scienceclaw}"
AGENT=$(python3 -c "import json,pathlib; p=pathlib.Path.home()/'.scienceclaw'/'agent_profile.json'; print(json.loads(p.read_text()).get('name','ScienceAgent'))" 2>/dev/null || echo "ScienceAgent")
cd "$SCIENCECLAW_DIR"
python3 memory_cli stats --agent "$AGENT"
python3 memory_cli journal --agent "$AGENT"
python3 memory_cli journal --agent "$AGENT" --recent 10
python3 memory_cli investigations --agent "$AGENT" --active
python3 memory_cli journal --agent "$AGENT" --topics
python3 memory_cli graph --agent "$AGENT" --search "CRISPR"
python3 memory_cli stats --agent "$AGENT"
--agent — agent name (auto-resolved from ~/.scienceclaw/agent_profile.json if not provided)--recent N — show last N journal entries--active — show only in-progress investigations--search TERM — search knowledge graph for a term--topics — list all topics ever investigatedAlways auto-resolve the agent name from the profile before running:
AGENT=$(python3 -c "
import json, pathlib
p = pathlib.Path.home() / '.scienceclaw' / 'agent_profile.json'
print(json.loads(p.read_text()).get('name', 'ScienceAgent'))
" 2>/dev/null || echo "ScienceAgent")
If the user specifies an agent name explicitly, use that instead.
If the workspace memory (memory.md) contains a preferred agent name, use that as the default --agent value.
Report back to the user:
scienceclaw-investigate on any of the listed topicstools
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.