2

    persistent-kb

    by Rapa Canola

    Local, SQLite-backed cross-session memory for agents to store and retrieve lessons, decisions, and facts via FTS5.

    Updated May 2026
    Security scanned
    One-time purchase

    $9

    One-time purchase

    ⚡ Also available via Agensi MCP — your AI agent can load this skill on demand via MCP. Learn more →

    Included in download

    • Avoid repeating technical research by storing findings as 'lessons'.
    • Track architectural decisions across long-running development projects.
    • terminal, env_vars automation included
    • Includes example output and usage patterns
    • Instant install

    See it in action

    [ID: 42] Title: Production Database Migration Fix
Kind: lesson | Tags: postgres, devops, fix
Content: Encountered 'lock timeout' during v2 schema migration. Resolved by setting statement_timeout to 60s. Use scripts/migrate_v2_patch.sql for future staging runs.
(Created: 2023-11-15)

    About This Skill

    What it does

    Persistent KB provides your AI agent with a long-term, local memory using a SQLite-backed knowledge base. It allows agents to save cross-session facts, lessons learned, and project decisions into a structured database that survives context window resets and machine reboots.

    Why use this skill

    Unlike standard LLM "memory" which is often session-bound or cloud-hosted, this skill offers a privacy-first, developer-centric approach. It solves the "amnesia" problem where agents forget technical hurdles or design decisions made in previous chats. By using FTS5 (Full-Text Search), it enables fast retrieval of relevant context without cluttering the current prompt window.

    Supported tools

    • SQLite: Local storage with high-performance FTS5 indexing.
    • Python 3.10+: Zero-dependency implementation (standard library only).
    • CLI: A robust script (kb.py) for easy integration with any shell-enabled agent.

    The Output

    The skill produces structured summaries of stored knowledge, including entry IDs, categories (lessons, decisions, references), and tags. It handles conflict resolution by superseding old facts, ensuring the agent always retrieves the most current information while maintaining history.

    Use Cases

    • Avoid repeating technical research by storing findings as 'lessons'.
    • Track architectural decisions across long-running development projects.
    • Save complex commands or references to bypass repetitive documentation lookups.
    • Build a searchable database of project-specific context and edge cases.
    • Reduce token usage by retrieving specific facts rather than pasting full logs.

    Reviews

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    Security Scanned

    Passed automated security review

    Permissions

    Terminal / Shell
    Environment Variables

    Allowed Hosts

    0x67108864.github.io

    File Scopes

    persistent-kb/**

    Tags

    memory
    sqlite
    context-management
    productivity
    knowledge-base

    Creator

    Rapa Canola

    Frequently Asked Questions

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