LLM-Wiki
An LLM Wiki testing skill — explore and experiment with large language model knowledge bases.
What Is LLM-Wiki?
LLM-Wiki is an advanced conversational knowledge engineering and prompt experimentation skill on EasyClaw. Designed specifically for developers, AI prompt engineers, and technical researchers, it serves as an interactive testing sandbox to explore and structure large language model (LLM) internal knowledge bases — allowing users to dynamically build complex semantic knowledge graphs, audit training data cutoffs, and run high-fidelity retrieval tests headlessly.
The skill is built for developers designing custom RAG (Retrieval-Augmented Generation) architectures, researchers testing model reasoning limits, and AI engineers standardizing context injection parameters.
The expected outcome is a fully compiled Knowledge Graph and Prompt Brief: featuring structured entity relationship matrices, validated context boundaries, and optimized system prompt templates ready to deploy in your production applications.
How LLM-Wiki Works
1. Initialize the wiki sandbox. Launch the interactive testing environment. The skill maps your target LLM model parameters (such as context window size, temperature, and system tokens).
2. Knowledge graph construction. Provide your raw topic data. The skill parses the concepts, extracts core entities and their relationships, and constructs a structured semantic knowledge graph locally in your workspace.
3. Training cutoff audit. Test the model's knowledge boundaries. The skill runs structured lookup queries to determine what historical events, codes, or policies the model knows natively vs. what requires external context injection.
4. Context retrieval simulation. It simulates RAG performance: testing how the model reasons when specific chunk-sized contexts are injected, auditing for context dilution or recall errors.
5. Output generation. Graph structures, prompt templates, and audit logs are written directly to your local workspace, paired with clean, scannable Markdown summaries in your chat.
Key Features
- Semantic knowledge graph builder: Structures raw concepts into queryable entity-relation matrices.
- Context window auditor: Simulates and tests model recall rates under different injected context sizes.
- Model boundary checker: Audits native model knowledge cutoff dates and parameters.
- System prompt optimizer: Designs and stabilizes complex system instructions for custom apps.
- Few-shot examples compiler: Writes structured input/output examples to maximize model reasoning.
- Direct local JSON saving: Writes compiled graphs and audit logs directly to your workspace.
What Problems Does LLM-Wiki Solve?
1. Building a semantic knowledge graph for an educational topic
An AI engineer is building an educational app explaining "quantum physics." They provide raw textbook excerpts to the wiki. The skill parses the text, extracts the core concepts (e.g., "superposition," "entanglement," "wave function"), maps their logical connections, and generates a structured, queryable JSON knowledge graph database, ready for application import.
2. Auditing model knowledge cutoff boundaries
A developer wants to know if a specific model natively understands the latest 2024 web frameworks. They run a boundary check. The skill queries the model's active training limits, identifies what information requires live Web search integrations, and designs a custom RAG injection strategy to bridge the knowledge gap.
3. Simulating RAG retrieval and context recall
An AI architect is experiencing "hallucinations" in their custom customer-service bot. They upload their system prompt and reference manuals. The skill simulates RAG retrieval: analyzing how the model processes 3,000-word manuals, highlighting where context dilution occurs, and restructuring the prompt to prioritize key guidelines.
4. Optimizing system prompts for a JSON extractor
A software team wants to extract user data from emails and require a strict, parsable JSON format. The skill designs a JSON-enforcer prompt: specifying the exact JSON keys, including a few-shot example, and adding custom validation rules to prevent markdown backticks from breaking their API parsers.
5. Correcting conflicting rules in complex system prompts
A prompt engineer has a complex assistant prompt that frequently fails because of contradictory instructions. The wiki audits the prompt: identifying role conflicts and vague terms, and reorganizes the instructions into a clear, hierarchical Markdown structure with explicit priority rankings, stabilizing performance.
Example Workflow
A developer needs to build a structured system prompt and knowledge graph for a custom code-auditing bot.
1. They open EasyClaw and activate LLM-Wiki.
2. They run: *"Build a knowledge graph explaining Node.js security patterns, and write a system prompt for a secure code auditor."*
3. The skill extracts security entities (XSS, CSRF, SQL Injection), maps their mitigation relations, and compiles the system prompt.
4. It writes the graph to `public/data/wiki/security-graph.json` and the prompt template to `security-prompt.md`.
5. It displays the completed prompt and a visual text-based graph tree in the chat.
Prompt templates and knowledge graphs designed in under 45 seconds.
Getting Started with LLM-Wiki
Add this skill to your EasyClaw workspace
Describe your task in a chat message
Review the output and iterate if needed
Export or share the results directly from EasyClaw
Combine with other skills to build automated workflows
Frequently Asked Questions
What is a Semantic Knowledge Graph?
A Semantic Knowledge Graph is a structured database composed of nodes (entities like concepts, libraries, or errors) and edges (the relationships linking them, like "MITIGATED_BY" or "EXTENDS"), allowing AI models to retrieve contextually rich, connected data.
What is a "RAG" simulation?
RAG stands for Retrieval-Augmented Generation. The simulation tests how a model processes and reasons when specific chunks of external reference manuals are dynamically injected into its prompt, auditing for recall accuracy and context dilution.
Can the skill export valid JSON schemas?
Yes. The prompt optimizer specializes in designing system prompts that enforce strict, parsable JSON outputs, and can also compile the required JSON schema files for your database validations.
Do I need to pay for external prompt databases?
No. The core knowledge graph building, prompt optimizing, and model boundary checking are executed entirely locally in your workspace session, requiring no external paid platform subscriptions.
Can I build prompts in languages other than English?
Yes. The skill supports multilingual prompt engineering and graph design. You can input data in English or Chinese and request the final system prompts and JSON graphs delivered in either language.
What is the "decline to edit" rule?
The skill provides automated warnings, risk scores, and compliant redlines. It will never modify your live application code or prompts directly without your explicit authorization, protecting your codebase.
What is "context dilution"?
Context dilution occurs when a prompt becomes too long or contains too much irrelevant information, causing the model to ignore or forget key instructions (especially those placed in the middle of the text).
How do I troubleshoot a system prompt that keeps failing?
Paste your system prompt, the user query, and the incorrect output. The skill will run a diagnostic audit — identifying rule conflicts, formatting leaks, or ambiguous terms — and output a rewritten, stabilized template.
What file format is the knowledge graph saved as?
Knowledge graphs are written directly as standard, structured JSON (`.json`) files in your workspace exports folder, ready to import directly into your custom applications or graph databases.
Is my private prompt history secure?
Yes. All prompt engineering, database logging, and file writes are executed entirely locally in your workspace session, ensuring your proprietary prompts and automation rules remain strictly private.
Browse more in Academic or all skills.
Add LLM-Wiki to Your Workflow
Get EasyClaw, add this skill, and start building AI agent workflows in minutes.
Get EasyClaw Free →