AI SummaryPulse Radar uses LLM pipeline to transform raw Telegram messages into structured knowledge. Core philosophy: Messages individually are noise; batched extraction reveals patterns. 1. JSON-only output — explicitly state "respond with ONLY JSON"
Install
Copy this and paste it into Claude Code, Cursor, or any AI assistant:
I want to install the "llm-pipeline" skill in my project. Please run this command in my terminal: # Install skill into your project (2 files) mkdir -p .claude/skills/llm-pipeline && curl --retry 3 --retry-delay 2 --retry-all-errors -o .claude/skills/llm-pipeline/SKILL.md "https://raw.githubusercontent.com/majiayu000/claude-skill-registry/main/skills/ai-llm/llm-pipeline/SKILL.md" && curl --retry 3 --retry-delay 2 --retry-all-errors -o .claude/skills/llm-pipeline/metadata.json "https://raw.githubusercontent.com/majiayu000/claude-skill-registry/main/skills/ai-llm/llm-pipeline/metadata.json" Then restart Claude Code (or reload the window in Cursor) so the skill is picked up.
Description
Pydantic-AI agents, RAG, embeddings for Pulse Radar knowledge extraction.
LLM Pipeline Skill
<overview> Pulse Radar uses LLM pipeline to transform raw Telegram messages into structured knowledge. Core philosophy: Messages individually are noise; batched extraction reveals patterns. </overview> <entity-hierarchy> ` TOPICS (categories) └─ ATOMS (knowledge units: problem/solution/decision/insight...) └─ MESSAGES (raw data, hidden layer) ` </entity-hierarchy> <extraction-flow> `python
1. Message arrives via Telegram webhook
await save_telegram_message(message) # triggers TaskIQ
2. Scoring (AI Judge, not heuristics - ADR-003)
score = await importance_scorer.score(message)
3. Auto-trigger extraction when threshold met
if unprocessed_count >= 10: # ai_config.message_threshold await extract_knowledge_from_messages_task.kiq()
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