Generate AI summaries for markdown notes using Ollama and populate the frontmatter `summary` property. Use hierarchical map-reduce for notes exceeding model context. Trigger when asked to summarize notes, generate note abstracts, add AI summaries to frontmatter, or batch-summarize Obsidian vault notes.
Instalación
Instalar con Codex o Claude Copia este prompt, pégalo en Codex, Claude u otro asistente, y deja que revise la página de la skill y la instale por ti.
Generate AI summaries for markdown notes using Ollama and populate the frontmatter `summary` property. Use hierarchical map-reduce for notes exceeding model context. Trigger when asked to summarize notes, generate note abstracts, add AI summaries to frontmatter, or batch-summarize Obsidian vault notes.
allowed-tools
Read, Grep, Glob, Bash
Instructions
Ask the user which Ollama model to use (e.g., qwen3:8b, llama3, gemma2). The model must already be pulled in Ollama.
Dry-run first to preview summaries without modifying files:
uv run --with ollama,pyyaml \
skills/summarize-note/scripts/summarize_note.py <model> --dry-run <file_path> [...]
If summaries look good, run without --dry-run to write them:
uv run --with ollama,pyyaml \
skills/summarize-note/scripts/summarize_note.py <model> <file_path> [...]
Review the JSON output to confirm summaries were generated and written correctly.
Key behaviors
Notes with an existing human summary (no [AI] prefix) are skipped automatically.
Long notes are split by headings and summarized via concurrent map-reduce.
Thinking model tags (e.g. <think>) are stripped automatically.
Use --chunk-size to adjust for models with smaller context windows (default: 50000 chars, ~12K tokens, sized for 32K+ context models).
Use --base-url to point to a remote Ollama instance.
