⚡ Quicky Wiki
Memory serverOfficial Halley project
Created by Anzal
Published in halleylabs/quicky-wiki. Publisher page
24 GitHub stars
What it is
An LLM-powered knowledge compiler with temporal tracking, confidence scoring, and multi-target compilation
AutoGineer overview, from the brain’s own files
Quicky Wiki is a knowledge management tool that transforms raw documents into a structured, confidence-scored knowledge base. It uses LLMs to extract atomic claims from sources, maintaining a SQLite-backed knowledge graph that tracks the provenance and dependency chain of every piece of information. Unlike static wikis, it treats knowledge as dynamic, applying temporal tracking to observe how beliefs strengthen, weaken, or decay over time. The system includes a dashboard for visualizing the graph, querying information with citations, and performing health checks to identify knowledge gaps or contradictions. It also functions as an MCP server, allowing AI agents to interact with the compiled knowledge base directly.
How it works
Memory model: Hybrid
AutoGineer overview, from the brain’s own files
The system ingests documents and uses an LLM to extract atomic claims, which are stored in a SQLite database along with their confidence scores and temporal events. These claims are linked to form a knowledge graph. The system tracks epistemic events such as reinforcements or challenges to claims, propagating confidence changes through dependent information. Users can compile this data into various formats like markdown, slides, or flashcards, and interact with the knowledge via a dashboard or an MCP server.
| Folder | Role | Files |
|---|---|---|
| .github | agent-config | 1 |
| src | source-code | 42 |
The agent reads
- list_entities evidence: README.md
- list_pages evidence: README.md
The agent writes
- update_entity_metadata evidence: README.md
Requirements
Runtimes: Node.js >=20
Databases: SQLite
API keys (by name): ANTHROPIC_API_KEY, GOOGLE_API_KEY, GROQ_API_KEY, OPENAI_API_KEY
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Google Antigravity, Kiro, OpenCode, Amp, Roo Code, Cline, JetBrains Junie, Devin (ex-Windsurf)
Set it up
- Not scanned by AutoGineerIts dependencies are installed by npm when you run it and were not scanned by AutoGineer (8 declared). https://www.npmjs.com/package/quicky-wiki
Created by Anzal. Published in halleylabs/quicky-wiki. Licence: MIT License.
Usage
AutoGineer overview, from the brain’s own files
- Ingesting local files or URLs to build a knowledge base
- Querying the knowledge base for answers with confidence scores
- Visualizing knowledge connections through an interactive graph
- Performing health checks to find contradictions or knowledge gaps
- Compiling knowledge into formats like Obsidian-compatible markdown or Anki flashcards
- Connecting to AI agents via the built-in MCP server
- “qw ingest research_paper.pdf”
- The system extracts atomic claims from the PDF, calculates confidence scores, and adds them to the SQLite knowledge graph.
- “qw query 'What are the primary risks of this project?'”
- The system returns an answer based on the ingested knowledge, including confidence scores and citations for the claims.
- “qw metabolism --redteam”
- The system identifies and challenges high-confidence claims to test the integrity of the knowledge base.
- “qw compile markdown”
- The system exports the current knowledge graph into Obsidian-compatible markdown files in the wiki directory.
Documentation by Anzal
⚡ Quicky Wiki
<sub>A <a href="https://halleylabs.dev">Halley</a> project</sub>
Turn any collection of documents into a living, confidence-scored knowledge base — powered by LLMs.
Quicky Wiki extracts claims from your sources, tracks how confident each claim is, watches for contradictions, and gives you a visual dashboard to explore everything. Think of it as a personal Wikipedia that actually tells you what it's unsure about.
Image: npm version Image: License: MIT Image: Node.js >= 20
30-Second Demo
npx quicky-wiki init --name "My Research" # auto-detects your API keys
qw ingest paper.pdf # extract claims from a paper
qw ingest https://arxiv.org/abs/2401.12345 # or a URL
qw serve # open the dashboardThat's it. Open http://localhost:3737 and you've got:
A knowledge graph you can zoom into, hover over, and click through
Ask Wiki — chat with your knowledge base, get answers with confidence scores and citations
Claims, pages, timeline, and health views
What Makes It Different
| Feature | Traditional Wiki | Quicky Wiki |
|---|---|---|
| Who writes it? | You | LLM extracts claims from your sources |
| Confidence? | ❌ Everything looks equally true | ✅ Every claim has a confidence score |
| Contradictions? | Hidden in edit history | Surfaced automatically |
| Temporal tracking? | ❌ | ✅ Claims strengthen, weaken, and decay over time |
| Knowledge gaps? | Unknown unknowns | Discovered and suggested |
| Multi-format output? | Just a wiki | Wiki, slides, flashcards, graph, timeline |
Install
npm install -g quicky-wikiThe package exposes three equivalent commands: quicky-wiki, qw, and create-quicky-wiki (all run the same CLI). Use whichever fits your muscle memory or tooling.
Or use directly with npx:
npx quicky-wiki initFrom a git clone of this repo, run npm run build before npx quicky-wiki …, node dist/cli.js …, or npm start, because the published CLI is the compiled dist/ bundle.
Requirements
Node.js >= 20
An API key from one of these providers:
| Provider | Env Variable | Example Model |
|---|---|---|
| Google Gemini | GOOGLE_API_KEY | gemini-2.0-flash |
| OpenAI | OPENAI_API_KEY | gpt-4o |
| Anthropic | ANTHROPIC_API_KEY | claude-sonnet-4-20250514 |
| Ollama | (local, no key) | llama3 |
| Any OpenAI-compatible | Custom env var + --base-url | Groq, Together, vLLM, LM Studio |
qw init auto-detects whichever API key you have set — no config needed.
Usage
Initialize
mkdir my-wiki && cd my-wiki
qw init --name "My Research"
# Or specify a provider explicitly
qw init --provider gemini --model gemini-2.0-flash
qw init --provider openai --model gpt-4o
# OpenAI-compatible (Groq, Together, etc.)
qw init --provider openai-compatible \
--base-url https://api.groq.com/openai/v1 \
--api-key-env GROQ_API_KEYIngest Sources
# Local files (markdown, text, PDF)
qw ingest paper.md --type paper --quality peer-reviewed
qw ingest notes.md --type note
# URLs (fetches and extracts automatically)
qw ingest https://example.com/article
# Batch ingest a directory
qw ingest raw/Query Your Knowledge
qw query "What are the key approaches to reinforcement learning?"
qw query "How do transformers compare to RNNs?"Explore with the Dashboard
qw serve # http://localhost:3737
qw serve --port 8080 # custom portThe dashboard includes:
Overview — Stats at a glance
Knowledge Graph — Interactive canvas visualization (Obsidian-inspired dark theme, hover to unfold connections)
Claims — Browse all extracted claims with confidence scores
Pages — Wiki pages compiled from your claims (optional entity kind and metadata when configured)
Timeline — Temporal view of knowledge events
Health — Knowledge integrity: stale claims, contradictions, gaps
Ask Wiki — Chat with your knowledge base
When you open a page, the slideout shows Linked pages (graph neighbors) and the rendered wiki markdown from wiki/. Inline Obsidian-style wikilinks work in that preview: [[Page Title]] and [[label|Page Title]] open the matching page by title. The dashboard script is embedded in the built CLI, so after changing TypeScript sources, run npm run build and restart serve to see UI updates.
Knowledge Health
qw lint # check for issues
qw metabolism --report # full health report
qw metabolism --decay # apply confidence decay over time
qw metabolism --resurface # find stale claims worth revisiting
qw metabolism --redteam # challenge high-confidence claimsCompile to Other Formats
qw compile markdown # Obsidian-compatible wiki pages
qw compile slides --topic X # Marp slide deck
qw compile anki # flashcards
qw compile graph # D3 knowledge graph
qw compile timeline # temporal visualizationDiscover New Directions
qw discover --mode gaps # what's missing?
qw discover --mode horizon # frontier topics
qw discover --mode bridges # connections between distant concepts
qw discover --mode contradictions # conflicting claimsHow It Works
Source Document
↓
LLM Extraction → Claims (with confidence scores)
↓ ↓
Knowledge Graph (SQLite) Epistemic Events (temporal log)
↓
Compiled Outputs: Wiki pages, slides, flashcards, graph, timeline
↓
Dashboard (interactive visualization + chat)Key Concepts
Claim — An atomic, verifiable statement extracted from a source. Has a confidence score, provenance, and dependency chain.
Epistemic Event — A change in belief: created, reinforced, challenged, weakened, superseded, or resolved.
Knowledge Diff — When you ingest a new source, you see what's new, reinforced, challenged, and what gaps were found.
Cascade — When a foundational claim is challenged, confidence changes propagate through dependent claims.
Metabolism — Active maintenance: decay, resurfacing, red-teaming.
MCP Server
Quicky Wiki includes a built-in Model Context Protocol server for integration with AI agents:
qw mcp # stdio mode (for Claude Desktop, etc.)
qw mcp --http --port 3000 # HTTP modeTools include querying, search (full-text on the graph), listing pages and claims, ingestion, and health reporting. Pages can carry an entity kind and metadata (see config / compiled wiki frontmatter). MCP adds list_entities (filter by kind and metadata) and update_entity_metadata for merging metadata without re-ingesting; list_pages and ingest_file accept optional kind-related parameters. Use each tool’s schema in your MCP client for full argument lists.
Project Structure
my-wiki/
├── .quicky/
│ ├── config.yaml # LLM provider, model, wiki name
│ └── graph.sqlite # knowledge graph (claims, sources, events)
├── raw/ # your source documents (immutable)
└── wiki/ # compiled output (Obsidian-compatible markdown)Development
git clone <repo-url> quicky-wiki && cd quicky-wiki
npm install
npm run build # required before serve / dist-based CLI picks up TS changes
npm run typecheck # optionalLicense
MIT
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Advantages
- Every extracted claim is assigned a confidence score, allowing users to distinguish between verified and uncertain information. evidence: README.md
- The system automatically tracks temporal changes, allowing claims to strengthen, weaken, or decay over time. evidence: README.md
- Built-in MCP server support enables direct integration with AI agents for querying and updating the knowledge base. evidence: README.md
Limitations
- Requires an active API key from a supported LLM provider or a local Ollama instance to perform extraction and analysis. evidence: README.md
- The system requires a manual build step from source before the CLI can be used after modifying TypeScript files. evidence: README.md
Security
Risk level: No risk found
This brain runs
This brain runs nothing: AutoGineer found no hooks, scripts, plugin code or MCP servers in its files.
Attribution and licence
- Created by
- Anzal
- Publisher
- Halley (Official Halley project)
- Source
- halleylabs/quicky-wiki · commit cfeecbf
- Licence
- MIT License (files served here)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
AutoGineer is not affiliated with or endorsed by Anzal or Halley.
Install from source
48 of its files are not served here, so there is no ZIP. halleylabs/quicky-wiki at cfeecbf