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    LLM Wiki Builder — AI-Powered Markdown Knowledge Base

    1

    Build and maintain structured, link-stable Markdown wikis optimized for human clarity and LLM semantic search.

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    LLM Wiki Builder — AI-Powered Markdown Knowledge Base

    LLM Wiki Builder — AI-Powered Markdown Knowledge Base

    Example session with this skill installed

    Draft a new canonical wiki page for our 'Project Helios' internal API based on these notes: it uses OAuth2, was started 2023-05-10, and resides at /api/v1/helios. It links to the Auth Hub.

    • Read your context and instructions
    • Compiled the llm wiki builder

    I have drafted the canonical entry for Project Helios. This page is structured to serve as the primary reference point for the API, incorporating the provided dates, technical specs, and required internal links.

    Wiki Page Draft: topics/project-helios.md

    ---
title: "Project Helios Internal API"
created: 2024-05-20
updated: 2024-05-20
status: draft
aliases: ["Helios API", "V1 Helios"]
tags: ["api", "internal-tools", "project-helios"]
sources: ["Internal Project Notes 2023-05-10"]
---

# Project Helios Internal API

> **Summary:** The core internal API service for Project Helios, providing authenticated access to project resources via versioned REST endpoints.

## Key facts
- **Version:** v1
- **Base Path:** `/api/v1/helios`
- **Authentication:** OAuth2 implementation
- **Start Date:** 2023-05-10

## Details
Project Helios is the primary interface for internal service communication. It strictly adheres to OAuth2 standards for all resource requests. Current implementation focuses on the `/v1` namespace to ensure backward compatibility during the rollout phase.

## Related pages
- [[Auth Hub]] — Handles token issuance and validation for Helios.
- [[Project Index]] — High-level overview of all active internal initiatives.

## Sources
- [Internal Project Notes](path/to/notes.md) — Initial architecture and timeline; accessed 2024-05-20.

## Open questions
- What is the specific rate-limiting policy for the `/v1` endpoints?
- Are there plans for a GraphQL gateway implementation in Q4?

    Next steps

    1. Verify the relative path for the Auth Hub page to ensure the link resolves correctly.
    2. Move this file from your /inbox to the /topics directory.
    3. Update the index.md navigation to include Helios under Active Projects.

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

    About this skill

    Stop drowning in scattered notes, disconnected documents, and redundant research. This skill transforms fragmented information into a high-utility, maintainable Markdown wiki designed for both human readers and LLM context retrieval. It enforces structure and traceability without the bloat of traditional knowledge management tools.

    What it does

    • Architects structures by designing lightweight directory hierarchies for concepts, projects, and sources.
    • Generates canonical entries using standardized templates that prioritize summaries, key facts, and metadata.
    • Manages link integrity through relative Markdown or [[Wiki Link]] syntax to ensure durable navigation.
    • Processes source material by extracting claims, decisions, and definitions while preserving provenance.
    • Audits wiki health by identifying broken links, orphan pages, stale content, and factual conflicts.
    • Optimizes for RAG by providing a pipeline for semantic search, including heading-aware chunking strategies.

    How it works

    1. Analyze context by inspecting existing folders, naming conventions, and available search tools.
    2. Execute tasks using commands for page drafting, source ingestion, or maintenance reporting.
    3. Verify output against a quality checklist for link consistency and source traceability.

    Frameworks & tools

    The skill produces portable Markdown files compatible with Obsidian, Logseq, GitHub, or any standard text editor. It provides logic for semantic indexing using vector databases and embedding providers.

    Why this beats prompting it yourself

    Generic prompts often lead to hallucinations of non-existent page links or inconsistent metadata fields. This skill enforces a strict canonical naming system and provenance tracking, ensuring your knowledge base remains a reliable source of truth rather than a collection of vague summaries.

    Use cases

    • Building a technical project wiki from PR descriptions and meeting notes.
    • Managing a research library with linked concept pages and source citations.
    • Architecting a documentation hub optimized for LLM semantic search.
    • Auditing existing Markdown vaults for duplicates and broken links.

    Known limitations

    Does not perform automated file system writes or live web crawling without external tool integration. Semantic search requires a configured indexer and embedding provider.

    Cannot perform direct file operations or live indexing without external tools. Semantic search requires a separate embedding provider. Link verification is limited to provided file paths.

    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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    What's inside

    Frequently Asked Questions

    The skill generates standard Markdown files with robust metadata and cross-linking, making it natively compatible with tools like Obsidian, Logseq, and GitHub, as well as any LLM-based RAG pipeline.

    Unlike basic prompting, this skill enforces a strict naming convention and link-integrity check to prevent 'hallucinated' links and ensures every entry includes verifiable source provenance.

    This skill provides the architectural logic and chunking strategies to format your Markdown for vector databases, ensuring your AI agents can efficiently retrieve specific, relevant context.

    No, this skill focuses on the structural and content logic; you will need to provide the files or integrate it with a tool that has file system permissions to perform the actual writes.

    You receive the complete skill package including structured templates, link-integrity logic, and maintenance protocols designed to work across major AI agent platforms.

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