alive-mine

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Deep context extraction from source material -- transcripts, documents, sessions. The archaeologist

AI & Automation 127 stars 8 forks Updated today MIT

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Skill Content

# Mine for Context Deep extraction from source material. Creates reference bundles, builds extraction plans, tracks what's been mined. ## What Gets Mined - Session transcripts (JSONL files) - Meeting transcripts (Fathom, Otter) - Email threads - Documents, PDFs - Slack/chat exports - Any substantial text source ## Extraction Targets For each source, extract: 1. **Decisions** -- what was decided, by whom, with rationale 2. **Tasks** -- action items with owners and deadlines 3. **People context** -- new information about known people, or new people 4. **Insights** -- domain knowledge that persists across sessions 5. **Quotes** -- sharp, memorable statements worth preserving 6. **Connections** -- references to other walnuts, bundles, or projects ## Process 1. **Read the source.** Full file, no skimming. 2. **Extract per category.** Group findings by type. 3. **Present for approval.** Nothing routes without the user seeing it. ``` Extracted from [source]: Decisions (3): 1. [decision] -> [[walnut]] 2. [decision] -> [[walnut]] 3. [decision] -> [[walnut]] Tasks (2): 4. [task] -> [[walnut]] 5. [task] -> [[walnut]] People (1): 6. [person context] -> [[person]] Insights (1): 7. [insight] -> [[walnut]] insights Approve all, or pick numbers to route? ``` 4. **Route approved items.** Decisions -> log entries. Tasks -> tasks.py. People -> person walnuts. Insights -> insights.md. 5. **Mark source as mined.** Update bundle manifest `mining:` field. ## Mining Stat...

Details

Author
alivecontext
Repository
alivecontext/alive
Created
6 months ago
Last Updated
today
Language
Python
License
MIT

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