lai-summarize git:20260417.89d056bA

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---
name: lai-summarize
model: sonnet
description: Use when generating a summary from captions, transcripts, or audio content. Agent-driven summarization using the agent's own LLM ability for highest quality. Falls back to CLI when needed. Use this skill whenever the user has a transcript, podcast, interview, or long caption file and wants a condensed overview, chapter summary, or key takeaways. Also triggers on "summarize", "生成摘要", "总结内容", "write summary", "episode summary", "总结这个播客", "what was discussed", "give me the highlights", "TL;DR", even if they just say "this transcript is really long, what are the main points?".
---

# Content Summarizer

**Agent-driven summarization.** The agent reads transcripts/captions and
writes structured summaries directly -- no external LLM API call by default.
Falls back to `lai summarize caption` (CLI with configurable LLM backend)
when the agent cannot process the content directly (e.g., very large files).

## When to Use

- Summarize podcast episodes, interviews, lectures, meetings
- Generate chapter-based summaries with timestamps
- Create TL;DR from long transcripts
- Produce SEO-friendly titles and descriptions
- Extract key entities (people, concepts, organizations)

## When NOT to Use

- Need full transcription -- use `/lai-transcribe`
- Need translation -- use `/lai-translate`
- Need speaker identification -- use `/lai-diarize` (summarize after)
- Input is not audio/caption content (articles, web pages)

## Parameters

| Parameter | Values | Default |
|-----------|--------|---------|
| `<source>` | Path to caption/transcript file | required |
| `--output <path>` | Output summary file path | `<source>.summary.md` |
| `--meta <path>` | Path to meta.md for episode metadata | auto-detect |
| `--lang <code>` | Summary language | source language |
| `--length <l>` | `short` \| `medium` \| `long` | `medium` |
| `--format <f>` | `markdown` \| `json` | `markdown` |

Length semantics:

| Length | Approximate Output |
|--------|--------------------|
| `short` | 200-400 words, 1 paragraph TL;DR + entity list |
| `medium` | 500-1000 words, chapters + quotes + entities |
| `long` | 1000-2000 words, detailed chapter summaries + full entity list |

## Output Schema (Markdown)

```markdown
---
title: "Episode Title"
seo_title: "SEO-Optimized Title (<=60 chars)"
seo_description: "One-sentence description (<=160 chars)"
tags: ["tag1", "tag2", "tag3"]
chapters:
  - title: "Chapter Title"
    start: 10.0
    end: 52.0
  - title: "Next Chapter"
    start: 52.0
    end: 180.0
confidence: 0.85
source_quality: high
---

One-paragraph TL;DR of the entire content -- 2-4 sentences, active voice.

## [00:10] Chapter Title

Summary paragraphs for this chapter, including key points discussed.

> *"Verbatim quote from the transcript, <=40 words"*

## [00:52] Next Chapter

Summary of the next section...

## Entities

- **Person Name** (Person): role or one-line context
- **Concept** (Concept): brief explanation
- **Organization** (Organization): one-line context
```

## Workflow

### Step 1 -- Read source content

Read the transcript/caption file. Supported inputs:
- JSON with `supervisions[]` (from alignment/diarization)
- SRT, VTT, ASS (subtitle files)
- Markdown (Gemini-style transcripts)
- Plain text (no timing)

Determine:
- Total duration and segment count
- Whether speakers are labeled
- Whether timestamps exist
- Source language

### Step 2 -- Gather metadata (optional)

If a `.meta.md` file exists alongside the source (or user provides one):
- Extract title, channel, speakers, duration
- Extract YouTube chapters (if present) -- these become **hard constraints**

**Chapter fidelity rule**: If `meta.md` has chapters, the summary MUST
use those exact chapter titles and timestamps. No merging, splitting,
reordering, or renaming. The body paragraphs within each chapter are
generated by the agent.

If no meta.md: the agent generates 1-8 chapters by identifying natural
topic shifts.

### Step 3 -- Generate summary

For each chapter:
1. Slice relevant segments by timestamp
2. Identify 1-3 key points
3. Select 1 quotable segment (short, self-contained, thesis-revealing)
4. Write 1-3 summary paragraphs

Additional sections:
- **TL;DR**: 2-4 sentence overview at the top
- **Entities**: 3-8 entries covering persons, concepts, organizations

Quality markers:
- `confidence`: 0.0-1.0 self-assessment
  - `0.9+`: clear domain, clean transcript, hard-constraint chapters
  - `0.75-0.89`: agent-generated chapters, clear topic flow
  - `0.5-0.74`: ambiguous segments, speculative chaptering
- `source_quality`: `high` / `medium` / `low`
  - `high`: complete transcript with speakers + clean prose
  - `medium`: mild ASR errors or missing speaker labels
  - `low`: heavily noisy transcript

### Step 4 -- Validate

Before writing output:
- [ ] Frontmatter is valid YAML
- [ ] Chapter headers match frontmatter `chapters[]` in title and order
- [ ] If hard-constraint chapters: all `start`/`end` values match source
- [ ] No chapter body is empty
- [ ] Every quote appears verbatim in the source transcript
- [ ] `tags[]` length between 4 and 8
- [ ] `seo_title` <= 60 chars, `seo_description` <= 160 chars

### Step 5 -- Write output

Write to `--output` path (default: `<source>.summary.md`).

## Fallback: CLI Summarization

For very large files (>500 segments) or when the agent cannot read the
entire transcript, fall back to the CLI:

```bash
lai summarize caption input.json -o summary.md
```

### LLM Configuration for CLI

The CLI requires an LLM backend:

```bash
# Gemini (recommended -- handles large contexts well)
lai config set summarization.llm.model_name gemini-2.5-flash
lai config set GEMINI_API_KEY your-key

# OpenAI-compatible
lai config set summarization.llm.model_name gpt-4o
lai config set OPENAI_API_KEY your-key

# Local model (Ollama, vLLM, etc.)
lai config set summarization.llm.model_name qwen3
lai config set summarization.llm.api_base_url http://localhost:11434/v1
```

### CLI Options

```bash
# Basic summary
lai summarize caption input.srt

# With metadata for richer context
lai summarize caption input.json meta=video.meta.md

# Chinese summary
lai summarize caption input.srt summarization.lang=zh

# Short summary
lai summarize caption input.srt summarization.length=short

# JSON output
lai summarize caption input.srt summarization.output_format=json

# Custom output path
lai summarize caption input.srt output=my_summary.md

# Custom LLM endpoint
lai summarize caption input.srt \
    summarization.llm.model_name=qwen3 \
    summarization.llm.api_base_url=http://localhost:8000/v1
```

## Error Handling

| Condition | Action |
|-----------|--------|
| Source file not found | Fail loud. Check path. |
| Empty transcript / no segments | Fail. Not a valid caption file. |
| Transcript too large for agent | Fall back to CLI with `lai summarize caption` |
| LLM not configured for CLI | Guide user to configure via `lai config set` |
| meta.md chapters malformed | Warn, fall back to agent-generated chapters |
| Chapter contains 0 segments | Fail. Chapter timestamp split is broken. |
| Generated summary fails validation | Fix and retry before writing output |

## Related Skills

| Skill | Use When |
|-------|----------|
| `/lai-transcribe` | Need transcript before summarizing |
| `/lai-align` | Need precise timing before summarizing |
| `/lai-diarize` | Need speaker labels for richer summaries |
| `/lai-translate` | Translate the summary to another language |
| `/lai-youtube` | Process YouTube video end-to-end |

### Common Workflow Chains

```bash
# Transcribe -> Summarize (agent-driven)
lai transcribe run podcast.mp3 transcript.json
# Then /lai-summarize for agent-driven summary

# Full pipeline: Transcribe -> Align -> Diarize -> Summarize
lai transcribe run video.mp4 transcript.json
lai alignment align video.mp4 transcript.json aligned.json
lai diarize run video.mp4 aligned.json diarized.json
# Then /lai-summarize on diarized.json for speaker-aware summary

# CLI fallback for large files
lai summarize caption huge_transcript.json \
    meta=video.meta.md \
    summarization.length=long
```

## Non-Goals

- Does NOT transcribe audio -- use `/lai-transcribe`
- Does NOT translate summaries -- use `/lai-translate` or a general translation skill
- Does NOT generate video chapters for YouTube upload
- Does NOT publish summaries to any platform
- Does NOT handle non-audio/caption content (articles, documents)