vault – OpenClaw Skill

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name: vault description: "Local research orchestration and state management. Use when starting projects, logging progress, or exporting findings."

Vault

Local orchestration engine for managing long-running research tasks with high reliability and zero external costs.

Core Concepts

• The Vault: A local SQLite database stored in ~/.researchvault/ (configurable via RESEARCHVAULT_DB).
• Project: A high-level research goal.
• Instrumentation: Every event tracks confidence (0.0-1.0), source, and tags.

Workflows

1. Initialize a Project

python3 scripts/vault.py init --id "proj-v1" --objective "Project goal"

2. Multi-Source Research

Use the unified scuttle command with SSRF protection:

python3 scripts/vault.py scuttle "https://example.com" --id "proj-v1"

3. Monitoring & Summary

python3 scripts/vault.py summary --id "proj-v1"
python3 scripts/vault.py status --id "proj-v1"

4. Export

python3 scripts/vault.py export --id "proj-v1" --format markdown --output summary.md

Maintenance

The database is local and excluded from version control.

ResearchVault 🦞

The local-first orchestration engine for high-velocity AI research.

ResearchVault is a specialized state manager and orchestration framework for OpenClaw agents. It allows agents to handle complex, multi-step investigations by persisting state, instrumentation, and insights into a local SQLite "Vault."

Following the Inference-Speed Development philosophy, Vault is built CLI-first to close the loop between research planning and autonomous execution.

✨ Core Features

• The Vault (SQLite): A persistent, local ledger stored in ~/.researchvault/ (configurable via RESEARCHVAULT_DB). 100% private.
• Normalized Evidence Core: Scalable storage for artifacts, findings, and links (graph-ready).
• Unified Ingestion Engine: Modular connectors for automated research capture.
• Instrumentation 2.0: Every research event tracks Confidence (0.0-1.0), Source, and Tags.
• Multi-Source Support:
  • X (Twitter): High-signal real-time data via bird.
  • Reddit: Structured community discussions and top-comment trees.
  • Grokipedia: Direct knowledge-base ingestion via API.
  • YouTube: Metadata-only extraction (titles/descriptions) without API keys.
• Suspicion Protocol 2.0: Hardened logic for low-trust sources. Moltbook scans are forced to low-confidence (0.55) and tagged #unverified.
• Semantic Cache: Integrated deduplication for queries and artifacts.
• SSRF Safety: Robust URL validation blocks internal network probes and private IP ranges.
• Hardened Logic: Versioned database migrations and a comprehensive pytest suite.

🚀 Workflows

1. Project Management

Initialize a project, set objectives, and assign priority levels.

uv run python scripts/vault.py init --id "metal-v1" --name "Suomi Metal" --objective "Rising underground bands" --priority 5

2. Multi-Source Ingestion

Use the unified scuttle command to ingest data from any supported source (Reddit, YouTube, Grokipedia, Web).

uv run python scripts/vault.py scuttle "https://www.youtube.com/watch?v=..." --id "metal-v1"

3. Export & Reporting

Ship research summaries to Markdown or JSON for external use or agent review.

uv run python scripts/vault.py export --id "metal-v1" --format markdown --output summary.md

4. Verification & Testing

Run the integrated test suite to verify system integrity.

uv run pytest

5. Monitoring

View sorted project lists, high-level summaries, and detailed event logs.

uv run python scripts/vault.py list
uv run python scripts/vault.py summary --id "metal-v1"
uv run python scripts/vault.py status --id "metal-v1"

🛠️ Development & Environment

ResearchVault is formalized using uv for dependency management and Python 3.13 stability.

• Core Architecture: Modular design separating Interface (vault.py), Logic (core.py), and Storage (db.py).
• Oracle Loops: Complex refactors use high-reasoning sub-agents.
• Main-Line Evolution: Atomic improvements are committed directly to main.

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This project is 100% developed by AI agents (OpenClaw / Google Antigravity / OpenAI Codex), carefully orchestrated and reviewed by Luka Raivisto.