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Ingest any transcript (pasted, file, or URL), extract speaker identities and positions, generate soul+skill files per speaker, and route questions to the most relevant voice with full attribution. Use when the user wants to \"create agents from a conference,\" \"talk to a speaker,\" \"ingest a transcript,\" \"build a summit mind,\" \"conference-minds,\" or wants persistent conversational access to conference content. Works with meetups, podcasts, panels, keynotes, interviews, and any multi-speaker content.\nauthor: schwentker\nlicense: MIT\n---\n\n# conference-minds\n\nTransform ephemeral conference content into persistent, conversational intelligence.\n\n## What It Does\n\nconference-minds takes a transcript (any format: raw paste, .txt, .md, .srt, .vtt, JSON) and produces a queryable knowledge layer where each speaker becomes a distinct conversational agent. Ask a question, get an answer attributed to the specific speaker whose position best addresses it, with a direct citation to the transcript passage.\n\n## When To Use This Skill\n\n- User pastes or uploads a conference transcript\n- User says \"ingest this talk\" or \"create agents from this panel\"\n- User wants to \"ask Jensen about inference costs\" after watching a keynote\n- User references \"conference-minds\" or \"summit mind\" by name\n- User wants to query a past event's content conversationally\n- User uploads multiple transcripts to build a composite conference mind\n\n## Architecture\n\n### Three-Layer Pipeline\n\n```\nINGEST          EXTRACT           SERVE\ntranscript  ->  speakers[]    ->  routed response\n                  soul.md           + attribution\n                  skills.md\n                  passages[]\n```\n\n### Layer 1: Ingest\n\nAccepts transcript in any common format. Detects speaker labels automatically (e.g., \"SPEAKER:\", \"John:\", timestamps with names). Cleans formatting artifacts, merges broken lines, normalizes timestamps.\n\n**Supported inputs:**\n- Raw pasted text\n- .txt, .md files\n- .srt, .vtt subtitle files\n- YouTube transcript format (timestamp + text)\n- JSON structured transcripts\n- Multiple files for multi-session conferences\n\n### Layer 2: Extract\n\nFor each detected speaker:\n\n1. **Identity**: Name, role (if mentioned), affiliation\n2. **Soul file** (`{speaker}_soul.md`): Communication style, rhetorical patterns, key phrases, intellectual posture (contrarian, consensus-builder, technical, visionary)\n3. **Skills file** (`{speaker}_skills.md`): Domain expertise areas, specific claims made, frameworks referenced, technologies discussed\n4. **Passages index**: Every statement attributed to this speaker with timestamp/position reference\n\nFor the conference as a whole:\n5. **Summit mind** (`summit_mind.md`): Composite themes, points of agreement/disagreement across speakers, emergent questions nobody asked\n\n### Layer 3: Serve\n\nWhen the user asks a question:\n\n1. **Intent classification**: What domain does this question touch?\n2. **Speaker routing**: Which speaker(s) have relevant expertise? Use weighted selection based on:\n   - Direct topical match (speaker discussed this specific subject)\n   - Expertise proximity (speaker's domain is adjacent)\n   - Rhetorical stance (if user asks \"who disagrees with X\")\n3. **Response generation**: Synthesize an answer in the speaker's voice using their soul file for tone and their passages for content\n4. **Attribution**: Every claim links back to a specific transcript passage with position marker\n5. **Multi-voice option**: For broad questions, present multiple speakers' perspectives\n\n## Commands\n\n### Ingest\n\n```\nconference-minds ingest <transcript>\nconference-minds ingest --file path/to/transcript.txt\nconference-minds ingest --name \"Cisco AI Summit 2026\"\nconference-minds ingest --multi path/to/session1.txt path/to/session2.txt\n```\n\n### Query\n\n```\nconference-minds ask \"What did the panel think about agent security?\"\nconference-minds ask \"Who disagreed with the cloud-first approach?\"\nconference-minds ask --speaker \"Peter Steinberger\" \"What's your view on MCP?\"\n```\n\n### Explore\n\n```\nconference-minds speakers              # List all extracted speakers\nconference-minds speakers --detail     # Show expertise areas per speaker\nconference-minds themes                # Show emergent conference themes\nconference-minds tensions              # Show points of disagreement\nconference-minds export                # Export all soul/skill files\n```\n\n### Manage\n\n```\nconference-minds list                  # List all ingested conferences\nconference-minds delete <conference>   # Remove a conference mind\nconference-minds merge <conf1> <conf2> # Combine conferences into one mind\n```\n\n## File Structure\n\nAfter ingestion, conference-minds creates:\n\n```\n~/.conference-minds/\n  conferences/\n    cisco-ai-summit-2026/\n      meta.json                    # Conference metadata\n      transcript_raw.md            # Original transcript preserved\n      transcript_clean.md          # Cleaned, normalized version\n      summit_mind.md               # Composite conference intelligence\n      speakers/\n        jensen-huang/\n          soul.md                  # Communication style, personality\n          skills.md                # Domain expertise, specific claims\n          passages.json            # Indexed statements with positions\n        pat-gelsinger/\n          soul.md\n          skills.md\n          passages.json\n      themes.json                  # Extracted conference themes\n      tensions.json                # Points of disagreement\n```\n\n## Dependencies\n\n### Required\n- Python 3.10+\n- No external API keys required for basic operation\n\n### Optional (enhanced features)\n- **Ollama** (local inference): For privacy-preserving speaker agent responses without API costs\n- **OpenAI/Anthropic API key**: For higher-quality extraction and response generation\n- **whisper/transcription skills**: Chain with audio transcription for end-to-end pipeline\n\n## How It Works Under the Hood\n\n### Speaker Detection Algorithm\n\n1. Scan for repeated name patterns at line starts (e.g., \"John:\", \"SPEAKER 1:\")\n2. Detect timestamp + name patterns from subtitle formats\n3. Fall back to paragraph-level attribution using linguistic cues\n4. Handle moderator/interviewer vs panelist distinction\n5. Merge speaker references (e.g., \"Dr. Smith\" and \"Smith\" are the same person)\n\n### Soul File Generation\n\nEach speaker's soul.md captures:\n\n```markdown\n# {Speaker Name} - Communication Soul\n\n## Voice\n- Sentence structure: {short/complex/mixed}\n- Vocabulary register: {technical/accessible/mixed}\n- Rhetorical devices: {analogy-heavy, data-driven, story-led}\n- Signature phrases: [\"...\", \"...\"]\n\n## Intellectual Posture\n- {contrarian | consensus-builder | provocateur | synthesizer | pragmatist}\n- Key tensions they hold: [...]\n- What they push back on: [...]\n\n## Values (inferred)\n- [extracted from positions taken and language used]\n```\n\n### Transit-Weighted Speaker Selection\n\nWhen routing a question to the right speaker:\n\n```\nrelevance_score = (\n    topical_match * 0.5 +      # Did they discuss this topic?\n    expertise_depth * 0.3 +      # How deeply did they go?\n    recency_weight * 0.1 +       # More recent statements weighted slightly\n    uniqueness * 0.1             # Did they say something others didn't?\n)\n```\n\nMultiple speakers returned when scores are close, enabling \"panel\" responses.\n\n### Attribution Format\n\nEvery response includes:\n\n```\n[Speaker Name, timestamp/position] \"Paraphrased or quoted passage\"\n```\n\nUser can request full original passage for verification.\n\n## Privacy and Ethics\n\n- All processing can run locally via Ollama (no data leaves the machine)\n- Original transcripts stored locally in ~/.conference-minds/\n- No data sent to external services unless user explicitly configures API keys\n- Speaker agents are clearly labeled as AI reconstructions, not the actual people\n- Attribution is mandatory in all responses to prevent misrepresentation\n\n## Chaining with Other Skills\n\nconference-minds is designed to chain with the OpenClaw ecosystem:\n\n- **whisper / transcription skills** -> auto-transcribe audio before ingestion\n- **moltbook-interact** -> speaker-agents can post to Moltbook\n- **elite-longterm-memory** -> persist context across sessions\n- **duckduckgo-search / web search** -> enrich speaker profiles with external context\n- **docx / pdf skills** -> export conference mind as formatted report\n\n## Examples\n\n### Basic: Ingest and Query\n\n```\n> conference-minds ingest --name \"YC Interview: Peter Steinberger\"\n  [paste transcript]\n\nIngested: 1 speaker detected (Peter Steinberger)\n  - 47 passages indexed\n  - Expertise: local-first AI, agent architecture, CLI philosophy, swarm intelligence\n  - Soul: contrarian, story-led, technically precise with accessible framing\n\n> conference-minds ask \"Why does Peter prefer CLI over MCP?\"\n\n[Peter Steinberger, 34:12] The preference is architectural, not ideological.\nMCP servers require restarts when configurations change. CLIs do not. More\nimportantly, MCP was designed for bots while CLIs were designed for humans.\nAgents turn out to be excellent at Unix. Building for humans first means\nsufficiently capable agents adapt naturally.\n\nSource: passages 31-33 of transcript\n```\n\n### Advanced: Multi-Conference Merge\n\n```\n> conference-minds merge \"Cisco AI Summit\" \"OpenClaw Meetup Feb 5\"\n\nMerged: 14 speakers across 2 events\nNew tensions detected:\n  - Cloud-first (Cisco speakers) vs Local-first (OpenClaw community)\n  - Enterprise governance vs Individual sovereignty\n  - Agent orchestration vs Agent autonomy\n\n> conference-minds ask \"Where do enterprise and open-source agent visions diverge?\"\n\n[Panel response - 3 speakers]\n...\n```\n","readmeExcerpt":"--- name: conference-minds version: 1.0.0 description: Transform conference transcripts into persistent, conversational speaker-agents. 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