Orchestrate persistent AI research state and autonomous verification in a local SQLite vault.
npx skills add https://github.com/lraivisto/ResearchVault --skill researchvault
Please help me install this Agent Skill. Skill: researchvault Source: https://github.com/lraivisto/ResearchVault/tree/main Command: npx skills add https://github.com/lraivisto/ResearchVault --skill researchvault
Research teams often struggle to keep long-running experiments, evidence, and findings synchronized across sessions and devices.
Local-first Vault: persist projects, findings, artifacts, and links in a portable SQLite store.
Divergent Reasoning: manage branches and hypotheses to explore competing explanations without contaminating the main line.
Synthesis & Verification: automatic cross-artifact linking and auto-generated verification missions to validate findings.
Watchdog Ingestion: continuous background ingestion of targeted URLs and queries for ongoing signal.
Use Case: A research project ingests data from multiple sources, links related findings, runs cross-artifact analysis, and periodically verifies results, exporting clean summaries for reporting.
Initialize the vault and load a sample project to begin ingesting data, linking findings, and running verification missions.
High-intent search queries and answers about installing and using this skill.
You can persist AI research state by storing projects, artifacts, and links in a local-first SQLite vault, ensuring findings remain synchronized across sessions and devices without data loss.
Divergent reasoning manages competing hypotheses by creating isolated branches within the research vault, allowing you to explore alternative explanations without contaminating the primary project line.
Yes, the system auto-generates verification missions to validate findings ingested from multiple sources, applying cross-artifact synthesis and linking to ensure evidence consistency throughout your research.
Watchdog ingestion continuously monitors targeted URLs and queries in the background, automatically fetching and ingesting ongoing signals into your local research vault for persistent tracking.
Yes, the modular Python stack requires dependencies including requests, beautifulsoup4, mcp, and rich to handle web ingestion, parsing, CLI tooling, and formatted output for the research vault.
You can export verified findings using the included scripts, which synthesize cross-artifact data and generate clean summaries from the local SQLite vault for downstream reporting.
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