L’interface est localisée ; les présentations et documents des Skills sont traduits automatiquement par IA dans votre langue. Le code, les commandes, chemins et termes techniques restent en version originale, et le document de l’auteur fait foi.
Explorer les Skills
Documents · SKILL.md

marker

marker

Use Marker to convert documents, configure pipelines, run LLM extraction, and operate app/server deployment surfaces.

Independent GitHub snapshot · Automated classification · Original documentation governs

Documentation de la Skill

GitHub

name: marker description: "Use Marker to convert documents, configure pipelines, run LLM extraction, and operate app/server deployment surfaces." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Marker Repo Skill

Use this repo skill when a user asks about Marker, marker-pdf, marker_single, marker, document-to-markdown/JSON/HTML/chunks conversion, OCR/table conversion, structured extraction, Marker LLM services, custom Marker processors/renderers/providers, or Marker app/server deployment.

Start Here

  1. Confirm the user wants the open-source Marker package, distributed as marker-pdf and imported as marker.

  2. For a fresh environment, install the base package first:

    pip install marker-pdf
    
  3. Install marker-pdf[full] only when converting non-PDF document families such as DOCX, PPTX, XLSX, HTML, or EPUB.

  4. Run a safe environment probe before heavy conversion, model downloads, or service launch:

    python scripts/marker_environment_check.py --check-cli
    
  5. Route to the focused sub-skill below, then use its nearest references/scripts for commands, API examples, validation, and troubleshooting.

Route By Task

User taskRead
Convert one PDF/image or a folder, choose output format, use Python API, run table-only/OCR-only conversion, parse outputs, tune workers/devicesub-skills/conversion-cli-api/SKILL.md
Explain or change config_json, ConfigParser, --processors, --converter_cls, providers, builders, processors, renderers, schema/debug outputsub-skills/configuration-extension/SKILL.md
Enable --use_llm, select Gemini/Vertex/Ollama/Claude/OpenAI/Azure services, build structured extraction schemas, troubleshoot LLM JSON/retry/key failuressub-skills/llm-extraction-services/SKILL.md
Run marker_server, call FastAPI endpoints, use marker_gui/marker_extract, adapt Modal deployment, build client templatessub-skills/server-deployment/SKILL.md

Shared References

  • references/install-and-environment.md: install variants, optional dependencies, backend/device checks, model/download cautions, and package identity.
  • references/troubleshooting.md: cross-cutting install/import, optional dependency, device, model/cache, credential, and workflow routing failures.
  • references/benchmark-notes.md: performance and benchmark context without making benchmark scripts a runtime dependency.
  • references/repo-provenance.md: source snapshot and evidence paths used to generate this skill.
  • references/repo-routing-metadata.json: structured routing metadata for DisCo import.

Safety Defaults

  • Do not run document conversion, model downloads, server processes, Streamlit apps, Modal deploys, or external LLM calls unless the user explicitly asks.
  • Prefer --help, dry-run config probes, schema validation, and small fixture checks before heavy conversion.
  • Treat original repo examples/tests as evidence only; this skill bundles its own references and scripts for future use.
  • Keep API keys and local environment paths out of generated code, prompts, logs, and shared examples.
Skills enregistrées