llm-pipeline
Pydantic-AI agents, RAG, embeddings for Pulse Radar knowledge extraction.
npx skills add majiayu000/claude-skill-registry --skill llm-pipeline-djimontyp-task-tracker --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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) # classification: SIGNAL (>0.6) / NOISE (<0.3) # 3. Auto-trigger extraction when threshold met if unprocessed_count >= 10: # ai_config.message_threshold await extract_knowledge_from_messages_task.kiq() # 4. KnowledgeOrchestrator runs Pydantic AI agent agent = Agent( model=model, system_prompt=get_extraction_prompt("uk"), output_type=KnowledgeExtractionOutput, # CRITICAL: structured output output_retries=5, ) result = await agent.run(messages_content) # 5. Save to DB + embed await save_topics_and_atoms(result.output) await embed_atoms_batch_task.kiq(atom_ids) ``` </extraction-flow
- RAG vs CAG
What does the llm-pipeline skill do?
Pydantic-AI agents, RAG, embeddings for Pulse Radar knowledge extraction.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill llm-pipeline-djimontyp-task-tracker --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.