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Process pending transcript distillation
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Based on SOC occupation classification
| name | distill-pending |
| description | Process pending transcript distillation |
| execution | inline |
Trigger distillation of pending transcripts and check distillation status.
If you need to manually trigger distillation for the current session:
# Add to queue for daemon processing
echo '{"tool":"distill_trigger","args":{"session_id":"CURRENT_SESSION_ID"},"ts":'$(date +%s)'}' >> /tmp/chitta-queue.jsonl
View registered transcripts and their distillation state:
chitta transcript_list
This shows:
session_id: Transcript identifierlast_processed_line: How many lines have been distilleddistilled: Whether any distillation has occurredrealm: Project contextDistilled learnings are stored as memories with:
[learn] prefix for extracted insightsDaemon distillation settings (in chittad --help):
--distill-interval MINS: Check interval (default: 5)--distill-min-turns N: Min turns before distilling (default: 4)--distill-model MODEL: LLM model for extraction--no-distill: Disable automatic distillationIf distillation isn't running:
chitta health_checkcurl -s http://localhost:11434/v1/modelschitta transcript_listls -la /tmp/chitta-queue.jsonl*Trigger autonomous curiosity-driven exploration. The soul picks a topic from memory gaps or curiosity seeds, searches the web, and stores what it finds as dream-tagged memories.
Fine-tune the Qwen3-0.6B hint model — corpus gen, LoRA/unsloth, GGUF export, Ollama
Review soul discoveries (fixes, improvements, corrections) one by one, accept or discard each, implement accepted ones, build chitta, and optionally release.
First-principles review — question requirements, delete unnecessary parts, simplify, optimize with evidence, automate last. Use for code review, refactor, performance, or architecture.
Token-savvy session continuation. Rebuilds working context from transcript + soul memories in ~1500 tokens instead of replaying full history. Use when starting a new session to continue previous work.
Resume a thread by loading its ~800-token context capsule