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geronimo-iiageronimo-iia/agent-skills/skills/summarize/SKILL.md

summarize

Skill2 starsChanged 3 months agoMITView on GitHub

Summarize URLs, local files (PDFs, documents, audio), and YouTube links. Composable pipeline using llm + markitdown + Jina Reader. Supports local models via Ollama. Use when you need a quick one-shot summary without ingesting into the knowledge base.

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The file

We split this skill into the rules it gives an agent

2 rules. 0 are common, 1 is distinctive, and 1 is very specific to this project.

  • 0

    Common

    Most of the 81 edited versions we read keep these.

  • 1

    Distinctive

    This author's own choices. Worth a look.

  • 1

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Use over the composable pipeline when:

    line 94
  2. Very specific:

    Use ctx_fetch_and_index instead when you need the content indexed and searchable later in the same session.

    line 109

“Common” is measured: kept by at least 6 in 10 of the 81 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Summarize

Composable pipeline for summarizing web pages, documents, audio, and video. Mix and match tools based on input type and model preference.

Install

llm needs a persistent install so plugins survive between calls:

uv tool install llm
llm install llm-ollama            # optional: local models via Ollama
brew install steipete/tap/summarize  # optional: all-in-one CLI alternative

markitdown and fabric work as one-shot uvx calls — no permanent install needed.

Quick start by input type

URL → summary

# Jina Reader: zero-install, returns clean markdown
curl -s "https://r.jina.ai/https://example.com" | llm "summarize this"

# With a specific model
curl -s "https://r.jina.ai/https://example.com" | llm -m claude-opus-5 "summarize"

PDF / Word / PPTX / HTML → summary

uvx markitdown file.pdf | llm "summarize"
uvx markitdown file.docx | llm "summarize"

YouTube / audio → summary

# Via summarize CLI (handles transcription)
summarize "https://youtu.be/<id>" --youtube auto

# Local: yt-dlp + whisper pipeline
yt-dlp -x --audio-format mp3 -o /tmp/audio.mp3 "https://youtu.be/<id>"
uvx openai-whisper /tmp/audio.mp3 --output_format txt
llm "summarize" < /tmp/audio.txt

Stdin / arbitrary text

cat notes.txt | llm "summarize"
echo "long text..." | llm -m ollama/llama3.2 "summarize in 3 bullets"

llm — model selection

llm models                        # list available models
llm -m claude-opus-5 "..."        # Anthropic (needs ANTHROPIC_API_KEY)
llm -m gpt-4o "..."               # OpenAI (needs OPENAI_API_KEY)
llm -m ollama/llama3.2 "..."      # local via Ollama (no key needed)
llm -m gemini-1.5-flash "..."     # Google (needs GEMINI_API_KEY)

Set default model once:

llm models default claude-sonnet-5

fabric — opinionated extraction patterns

Useful when you want structured extraction, not just a summary:

uvx fabric --pattern summarize < input.txt
curl -s "https://r.jina.ai/https://example.com" | uvx fabric --pattern extract_wisdom
curl -s "https://r.jina.ai/https://example.com" | uvx fabric --pattern extract_ideas
uvx fabric --list   # see all available patterns

summarize CLI (steipete) — when to prefer it

Use over the composable pipeline when:

  • You need audio diarization (--diarize)
  • YouTube via Apify fallback (--youtube auto)
  • Multi-provider routing from one tool (--model cli/claude, OpenRouter, GitHub Copilot)
  • You want --length control without writing a prompt
summarize "https://example.com" --model anthropic/claude-sonnet-5 --length long
summarize "file.pdf" --model google/gemini-3-flash-preview
summarize "https://youtu.be/<id>" --youtube auto
summarize "https://example.com" --cli claude   # uses claude CLI, no API key

When NOT to use

Use ctx_fetch_and_index instead when you need the content indexed and searchable later in the same session. These tools are one-shot — output is not stored in the context-mode knowledge base.

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