lattifai/lattifai-skills/lai-summarize

Content Summarizer

Preferred model: Claude Sonnet (cost-efficient for this agent-driven workload). This skill runs on whatever model is active in the parent session — any Claude model works; no hard switch.

This skill summarizes using the agent's own LLM capability — the model you are running right now. It does not call out to any external LLM service by default. Helper scripts turn the source into an agent-ready prompt and validate the finished summary, so the agent only has to write prose.

Primary path (agent-driven, default): prepare.py → agent writes summary.md → validate.py. Secondary path: lai summarize caption (CLI with its own LLM backend) — only when the transcript is too large for the agent's context, or there is no agent in the loop.

Inputs

Any caption format supported by lattifai-captions (SRT, VTT, ASS, JSON, Gemini markdown, plain text, …).

Optional meta.md beside the source enriches the summary. If `meta.md` defines `chapters:`, those titles and timestamps are hard constraints — no merge, split, rename, or reorder.

meta.md must wrap its YAML in --- frontmatter delimiters; otherwise validate.py can't parse it and hard constraints are silently skipped (with a warning on stderr). Minimal template:

---
title: "Episode Title"
chapters:
  -{ title: "Intro", start: 0.0, end: 60.0 }
  -{ title: "Main Topic", start: 60.0, end: 420.0 }
---
 
Free-form notes below the frontmatter are fine.

Primary Path (agent-driven)

Parameters (agent choices)

• <source> — caption/transcript file
• --output <path> — default <source>.summary.md
• --meta <path> — episode metadata (auto-detected beside source)
• --lang <code> — summary language (default: source language)
• length — short / medium / long ≈ 200–400 / 500–1000 / 1000–2000 words (default medium)

Workflow

<base> = source media stem (e.g. podcast from podcast.mp3) or YouTube ID. Files all land in the current directory:

# 1. Build an agent-ready prompt input from the source + meta.md
python skills/lai-summarize/scripts/prepare.py podcast.aligned.json -o podcast.prompt_input.md
 
# 2. Agent reads <base>.prompt_input.md and writes <base>.summary.md per the schema below.
 
# 3. Validate frontmatter, chapters, and verbatim quotes
python skills/lai-summarize/scripts/validate.py podcast.aligned.json podcast.summary.md

prepare.py produces a transcript with [MM:SS] timestamps and speaker labels, plus a # Meta block that marks meta.md chapters as HARD CONSTRAINT when present.

Output Schema

---
title: "Episode Title"
seo_title: "SEO title (≤60 chars)"
seo_description: "One-sentence description (≤160 chars)"
tags: ["tag1", "tag2"]             # 4–8 tags
chapters:
  -{ title: "Chapter Title", start: 10.0, end: 52.0 }
confidence: 0.85                    # 0.0–1.0 self-assessment
source_quality: high                # high | medium | low
---
 
TL;DR paragraph (2–4 sentences, active voice).
 
## [00:10] Chapter Title
Summary paragraph(s).
 
> *"Verbatim quote, ≤40 words."*
 
## Entities
 
-**Name** (Person|Concept|Organization): one-line context

What validate.py checks

• Frontmatter is valid YAML with all required fields
• seo_title ≤ 60, seo_description ≤ 160
• tags: 4–8 entries
• chapters: 1–8, each with title/start/end, start < end
• Hard constraint: chapter titles + timestamps match meta.md verbatim when present
• Body ## [MM:SS] Title headers match frontmatter chapters 1:1 in order
• Every > *"quote"* appears verbatim in source transcript text
• confidence ∈ [0, 1]; source_quality ∈ {high, medium, low}

Requires pyyaml (stdlib argparse / re otherwise).

Secondary Path (CLI)

The CLI takes two positional args (input, output); there is no -o:

lai summarize caption podcast.aligned.json podcast.summary.md

Configure an LLM backend once (required for this path only):

lai config set summarization.llm.model_name gemini-3-flash-preview
# Gemini key: see /lai-transcribe
 
# Or OpenAI-compatible:
lai config set summarization.llm.model_name gpt-4o
lai config set OPENAI_API_KEY <your-key>

CLI options: summarization.lang=zh, summarization.length=short, summarization.output_format=json, meta=video.meta.md.

Common Issues

Related Skills

• /lai-transcribe — produce the transcript first
• /lai-align — precise timestamps feed chapter boundaries
• /lai-diarize — speaker labels enable speaker-aware summaries
• /lai-translate — translate the summary