skills/metabolism-init/SKILL.md
# Metabolism Initialization Skill Overview This skill enables **knowledge metabolism initialization** for research topics through a structured Day 0 baseline-building process. ## Key Components **Configuration Setup**: The system checks for `metabolism/config.json` and creates it if absent, capturing research direction, keywords, categories, and processing history. **Directory Structure**: Establishes organized workspace with subdirectories for knowledge, hypotheses, experiments, conversatio
npx skillsauth add lamm-mit/scienceclaw skills/metabolism-initInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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This skill enables knowledge metabolism initialization for research topics through a structured Day 0 baseline-building process.
Configuration Setup: The system checks for metabolism/config.json and creates it if absent, capturing research direction, keywords, categories, and processing history.
Directory Structure: Establishes organized workspace with subdirectories for knowledge, hypotheses, experiments, conversations, and logging.
Three-Phase Workflow:
Broad Literature Survey — Delegates to /research-collect to gather foundational and recent works without date restrictions, generating metadata and downloads
Paper Analysis — Extracts core methodologies and conclusions from TeX sources or PDFs, tracking processed IDs to avoid duplication
Knowledge State Construction — Creates indexed summaries mapping identified topics with cross-references, timelines, and open questions
Documentation: Generates metabolism/knowledge/_index.md with research goals, topic tables, and relationship mapping, plus topic-specific markdown files and timestamped initialization logs.
The skill operates autonomously post-configuration, avoiding data fabrication and requiring content verification before modifications. It spawns sessions that share the working directory, streamlining collaborative processes.
tools
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.