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name: llm-extraction-services description: "Use Marker LLM enhancement services and structured extraction safely." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Marker LLM Extraction Services

Use this sub-skill when a task needs Marker’s --use_llm hybrid mode, provider selection, LLM-assisted table/form/equation/page correction, or ExtractionConverter structured JSON extraction.

Read first

• Pick and dry-run provider config with references/llm-services.md and scripts/llm_config_probe.py.
• Build and validate extraction schemas with references/structured-extraction.md and scripts/validate_extraction_schema.py.
• Decide which LLM processors matter with references/llm-processors.md.
• Diagnose failures with references/troubleshooting.md.

Fast routing

• For ordinary non-LLM conversion, output formats, PdfConverter, TableConverter, marker_single, and batch CLI basics, route to ../conversion-cli-api/.
• For custom non-LLM processors/renderers/providers or class-path debugging beyond LLM service paths, route to ../configuration-extension/.
• For FastAPI, Streamlit, or deployment concerns, route to ../server-deployment/.

Core rules

• Always set use_llm / --use_llm; Marker’s ConfigParser.get_llm_service() returns None without it, even if llm_service is supplied.
• Default LLM backend is marker.services.gemini.GoogleGeminiService; pass provider-specific keys/config in CLI flags, config_json, or API config.
• Do not put API keys in skill files, shared logs, or examples. Use environment injection or local config files managed outside the skill.
• Run bundled scripts only for dry-run inspection; they validate config/schema shape and never call LLM APIs.