llm-wiki-maintainer 1.1

    2

    Turn your AI agent into a digital librarian that builds and audits a persistent markdown knowledge base.

    $5

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    Works with the AI Agents you already use

    Claude CodeClaude CodeCursorCursorCodex CLICodex CLIMuseMuseDotsDots+21 more

    llm-wiki-maintainer 1.1

    Example session with this skill installed

    Record our decision to switch to FastAPI based on today's logs, reconcile the conflicting rate limit data from source_A and source_B, and run a health check on the wiki.

    • Read your context and instructions
    • Compiled the llm-wiki-maintainer 1 1

    Updated index.md with 2 new entries.
    Created decision-record: 'migration-to-fastapi.md'
    Appended log.md: [2023-10-24] Resolved conflict between source_A and source_B regarding API rate limits.
    Running wiki_lint.py... 0 orphans found. Wiki is healthy.

    Updated index.md

    Connects securely to your tools. The creator never sees your data.

    About this skill

    Maintain a Persistent, Compound Knowledge Base

    The LLM Wiki Maintainer solves the "memory leak" problem in AI workflows where knowledge is lost after a conversation ends. Instead of ephemeral chats, this skill enables your agent to build and maintain a living markdown wiki that grows more valuable over time.

    What it does

    This skill provides a structured framework for an AI agent to act as a librarian and synthesizer. It manages a directory of markdown files using a strict schema, ensuring that raw sources remain immutable while synthesized "knowledge pages" are continuously updated. It handles provenance tracking, contradiction management, and automated indexing.

    Why use this skill

    • Persistence: Every useful answer the AI generates is automatically filed back into the wiki for future use.
    • Provenance: Tracks source hashes and claims so you always know where information came from.
    • Structural Integrity: Includes Python scripts for linting, SQLite-backed indexing, and automated log generation to prevent the wiki from becoming a "junk drawer."
    • Conflict Resolution: Instead of overwriting old data, it reconciles contradictions through dedicated reflection pages.

    Technical Features

    Supports markdown-based folders, SQLite for high-speed querying of page metadata, and custom linting rules for structural health. It works seamlessly with local file systems or cloud-based repositories.

    • Requires local Python environment for lint/store scripts.
    • No built-in OCR; raw sources must be text-based.
    • Performance may degrade on wikis exceeding 5,000 pages.

    How to use

    1. 1

      Connect your agent to the Agensi MCP

      Copy the prompt and paste it into Claude, ChatGPT, Cursor or any agent that supports MCP. It sets up the connection for you. Manual setup

    2. 2

      Sign in to Agensi

      Your agent shows a sign-in link. Sign in and click Allow.

    3. 3

      Ask for what you need

      Your agent loads this skill and uses it on your task. It always asks before any payment.

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    Security scan passed 10 days ago

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    Listed6 months ago

    What's inside

    Frequently Asked Questions

    The skill adds a persistent "librarian" layer to your agent, allowing it to save, update, and manage information across sessions in a structured markdown wiki rather than letting knowledge disappear when a chat ends.

    llm-wiki-maintainer 1.1 is designed for agents that have file system access and can execute Python scripts, making it compatible with most local frameworks and modular AI orchestration platforms.

    You will receive the core logic framework, a set of automated Python scripts for SQLite indexing and markdown linting, a predefined folder schema, and documentation for integration.

    Existing data remains safe; the skill uses a non-destructive synthesis process where raw sources are kept immutable while the agent builds separate "knowledge pages" to reconcile and summarize the information.

    Yes, this version is a complete, self-contained package, and purchasing it grants you access to any minor bug fixes or documentation improvements released for version 1.1.

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