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talk-stage1-extract

Turn articles, transcripts, or notes into a structured talk summary. Identify whether the material centers on practical experience or ideas, then extract its narrative, themes, sourced metrics, and gaps to guide further preparation.

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Extracts and structures source material (articles, transcripts, notes) into a talk summary with narrative arc, themes, metrics, and gaps. Auto-detects REX vs Concept type. Use when starting a new talk from any source material or auditing existing material before committing to a talk.

See how to install

Good for

  • Starting a talk from an article or notes
  • Assessing source material before committing to a talk
  • Organizing measurable results with their sources
  • Finding missing material before planning later stages

Files included (1)

  • SKILL.md4.7 KB

SKILL.md (original)

Review the instructions the agent will receive before you install.

Talk stage 1: Extract

Transforms raw material (article, transcript, notes, or a mix) into a structured summary ready for the pipeline's downstream stages. Auto-detects source type.

When to use this skill

  • Starting a new talk from any source material
  • First step of the talk pipeline (always run before other stages)
  • Auditing existing source material before committing to a talk

What this skill does

  1. Collects metadata by asking for slug, event, date, duration, audience, mode if not provided
  2. Reads the source and loads the source file or inline content
  3. Detects source type (REX with real-world proof vs Concept with ideas/thesis) based on content signals
  4. Extracts the narrative arc (chronological for REX, thematic for Concept)
  5. Extracts metrics with every measurable number and its source
  6. Identifies main themes (3-7 themes)
  7. Flags gaps: what's missing for a complete talk
  8. Writes {slug}-summary.md

Input

Required:

  • Source file path or inline content (article .mdx, transcript .md, notes)
  • Metadata: slug, event, date, duration, audience, type (--rex or --concept)

If metadata is missing -> AskUserQuestion before proceeding.

Output

talks/{YYYY}-{slug}-summary.md

Source type detection

REX signalsConcept signals
Specific datesTheses, arguments
Measured metricsGeneral observations
Project/tool namesTrend observations
Commits, releases, PRsAnalogies, metaphors
"I shipped", "We built""I think", "In my opinion"

If hybrid -> note both components in the summary.

Output format

# Talk Summary: {Provisional Title}

**Slug** : {slug}
**Event** : {event}
**Date** : {date}
**Duration** : {duration} min
**Audience** : {audience description}
**Type detected** : REX | Concept | Hybrid
**Source** : {source file path}

---

## Narrative Arc

{Arc description: 3-5 sentences. Chronological if REX, thematic if Concept.}

## Main Themes

| # | Theme | Short description | Weight |
|---|-------|------------------|--------|
| 1 | {theme} | {description} | High/Medium/Low |
...

## Key Metrics Extracted

{All measurable numbers found in the source}

Format: `{value}`: {context} (Source: {section/page/git})

Examples:
- `1,200 commits` over 7 months (Source: "acceleration" section)
- `-97% traffic` after SSE migration (Source: CHANGELOG v1.1.0)

If none -> "No verifiable metrics found (Concept mode)"

## Narrative Potential

{3-5 sentences on the strengths and possible narrative angles.
What makes this talk potentially strong. What might be missing.}

## Gaps Identified

- [ ] {gap 1}: {how to fill it}
- [ ] {gap 2}: {how to fill it}

If no obvious gaps -> "No major gaps identified."

## Recommendations for next stages

- **Research**: {recommended / not applicable (Concept mode)}: {why}
- **Concepts**: {priority themes to explore}
- **Position**: {angles already visible from the source material}

---

*Generated by talk-stage1-extract: {date}*
*Source: {source path}*

Metric extraction rules

  • Do not round without indicating it
  • Always include the metric's source
  • If two sources contradict, flag both without picking one
  • No invented metrics to fill gaps
  • Use {before} -> {after} format for evolutions

Anti-patterns

  • Vague summary ("This text is about AI...")
  • Omitting metrics, even approximate ones with their source
  • Hiding gaps (naming them is better than pretending they don't exist)
  • Changing the detected type without justification
  • Inventing a narrative arc not present in the source

Validation checklist

  • Source type detected and justified
  • Narrative arc in 3-5 clear sentences
  • All measurable metrics extracted with their source
  • Main themes listed (3-7 max)
  • Gaps explicitly identified
  • File saved to talks/{YYYY}-{slug}-summary.md

Tips

  • Run this before the orchestrator if you want to verify the source material is usable
  • The summary is the foundation: every downstream stage reads it
  • Hybrid sources (part REX, part Concept) are fine, name both components clearly

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