Reddit user shares detailed prompt for exporting personal knowledge from AI assistants
A Reddit user has shared a detailed prompt designed to extract structured personal knowledge from AI assistants, positioning it as a more comprehensive alternative to Anthropic's ChatGPT import feature.
Prompt Structure and Requirements
The prompt instructs the AI to perform a "PERSONAL KNOWLEDGE EXPORT" using everything it knows from conversation history, long-term memory (if available), interaction patterns, discussed projects, mentioned goals, and recurring themes in questions. The user specifies this is NOT a personality reflection, motivational writing, or summary, but rather a structured data extraction task.
The prompt requires the AI to export the most important: facts, projects, skills, beliefs, patterns, frameworks, recurring ideas, risks, opportunities, research interests, coined concepts, and strategic directions.
Three JSON Artifacts
The prompt demands exactly three JSON artifacts in this order:
- ARTIFACT 1 — PERSONAL KNOWLEDGE BASE: A JSON object with top-level keys including identity_core, core_life_domains, major_projects, skills_and_capabilities, repeated_thinking_patterns, key_beliefs, long_term_directions, strengths, risks_and_tensions, and highest_leverage_opportunities. Rules specify being concrete and specific, including metrics and proof points when possible, avoiding vague statements, extracting rather than inventing, and labeling uncertain items as "inferred".
- ARTIFACT 2 — IDEAS & FRAMEWORKS EXPORT: A JSON object capturing intellectual frameworks and concepts with sections including core_thesis, umbrella_concepts, operating_system_thinking, ritual_design, collaboration_frameworks, education_and_empowerment, product_strategy_patterns, research_directions, and recurring_questions_you_explore. Rules require capturing named or implied frameworks, giving each concept a definition, and including principles and mental models.
- ARTIFACT 3 — KNOWLEDGE GRAPH: A graph JSON with graph_meta, nodes[], and edges[]. Node types may include thesis, concept, framework, principle, product, pattern, risk, opportunity, value_target, initiative, problem, and strategy. Each node must contain id, type, title, summary, and tags[]. Edges must contain from, type, to, and weight (0.1–1.0). Edge types may include DEFINES, ENABLES, IMPLIES, SOLVES, DEPENDS_ON, MANIFESTS_AS, MEASURED_BY, COMPOSED_OF, REINFORCES, CONSTRAINS, APPLIES_TO, RISKS, and OPPORTUNITY_FOR.
Global Rules
The prompt includes global rules: output valid JSON only, no commentary, no extra text, be maximally information-dense, do not repeat content between artifacts unless necessary, and use "unknown" if truly lacking information.
The Reddit post suggests asking Claude to create an artifact to visualize the JSON output from the export as a bonus.
📖 Read the full source: r/ClaudeAI
👀 See Also
Two Patterns for Preventing AI Agent Memory Rot: AutoDream and Skeptical Retrieval
OpenClaw introduces two MIT-licensed patterns to address file-based AI memory rot: AutoDream for nightly memory consolidation and Skeptical Retrieval for decay-weighted memory scoring. Both work together in a self-improving loop to keep agent context current.
MonClaw: A Minimal OpenClaw Implementation Using OpenCode SDK
A lightweight alternative to OpenClaw built on the OpenCode SDK, featuring Telegram and WhatsApp support.
Steerling-8B: An Interpretable Language Model with Token-Level Attribution
Guide Labs released Steerling-8B, an 8-billion-parameter language model trained on 1.35 trillion tokens that can trace any generated token to input context, human-understandable concepts, and training data sources. The model achieves competitive performance with models trained on 2-7× more data.
Claude Design vs Huashu-Design: A Head-to-Head on HTML Layouts and Rate Limits
Claude Design builds HTML prototypes fast but hits rate limits quickly. Huashu-Design, an open-source Claude Code skill, runs on the normal subscription with no separate rate limit—but takes 20 minutes vs 5.