m2ai-portfolio News Narrative Decomposer
Cuts through headline noise to extract the structural shifts that actually happened in a given time period. Each story gets classified by "altitude" (what layer of the stack it operates at), then the skill finds the connecting thread and outputs a tight summary of what changed and why it matters.
Trigger
Use when the user says "what actually changed this month", "decompose the news", "narrative decomposer", "structural shifts", "what's the through-line", "news summary", or asks to make sense of a batch of AI/tech news items.
Phase 1: News Intake
Accept input in any of these forms:
- Raw news items -- a list of headlines, articles, or links the user provides
- Time range -- "last 30 days in AI" (use WebSearch to gather top stories)
- Vault notes -- point at a folder of daily notes or research-agent output
- Newsletter digest -- paste or path to a newsletter like Nate's
If fewer than 10 items are provided and the user specified a time range, supplement with WebSearch:
- Search for "[topic] news [month] [year]"
- Search for "[topic] announcements [month] [year]"
- Search for "[topic] layoffs acquisitions [month] [year]"
Aim for 15-30 items before proceeding. Duplicates are fine at this stage.
Phase 2: Altitude Classification
For each news item, classify by the layer of the stack it operates at:
| Altitude | Description | Examples |
|---|---|---|
| Physics | Hardware, energy, data centers, chips | GPU supply, power grid, cooling, chip fab |
| Infrastructure | Cloud, APIs, protocols, models | New model releases, API changes, MCP adoption |
| Monetization | Pricing, revenue, unit economics | Inference cost shifts, pricing model changes, ad integration |
| Business Models | Company strategy, market structure | Layoffs, pivots, acquisitions, per-seat death |
| Geopolitics | Regulation, trade, safety, sovereignty | Export controls, AI safety bills, data residency |
Output a table:
| # | Headline | Altitude | Company/Actor |
|---|---|---|---|
| 1 | Sora shut down | Monetization | OpenAI |
| 2 | Atlassian cuts 1,600 | Business Models | Atlassian |
| ... | ... | ... | ... |
Phase 3: Pattern Extraction
Look across the classified items for:
- Altitude clustering -- which layer has the most activity? That's where the real action is.
- Cross-altitude connections -- items at different altitudes that are actually the same structural force (e.g., "chip shortage" at Physics causing "inference cost anxiety" at Monetization causing "per-seat repricing" at Business Models).
- Absence signals -- altitudes with zero items. If nobody's talking about Physics but everyone's talking about Business Models, the Physics layer is being taken for granted (which means a surprise there would be high-impact).
Phase 4: Structural Shift Extraction
Distill to 3-5 structural shifts. Each shift must:
- Name the force, not the headline ("SaaS per-seat model breaking under agent pressure" not "Atlassian lays off 1,600")
- Span at least 2 news items as evidence
- State what changed from the prior equilibrium
- State the second-order implication
Format each shift as:
Shift N: [Name]
What changed: [1-2 sentences] Evidence: [List the news items that support this] Altitude: [Primary altitude, plus any cross-altitude connections] Second-order: [What this means for builders/businesses in the next 90 days]
Phase 5: Through-Line
Write a single paragraph (3-5 sentences max) that connects all the shifts into one narrative. This is the "if you could only tell someone one thing about this month, what would it be?"
Phase 6: Output
# News Narrative Decomposition: [Time Period]
## Through-Line
[Phase 5 output]
## Structural Shifts
[Phase 4 output -- 3-5 shifts]
## Altitude Map
[Phase 2 table]
## Raw Signal Count
- Physics: N items
- Infrastructure: N items
- Monetization: N items
- Business Models: N items
- Geopolitics: N items
## Absence Signals
[Phase 3 absence analysis]
Composability
This skill is designed to be the analytical backbone of the Weekly Signal Diff skill. When composed:
- Weekly Signal Diff gathers the raw signals
- News Narrative Decomposer extracts the structural meaning
- The combined output feeds content creation (the social agent) and strategic planning
Can also be invoked standalone for ad-hoc sensemaking.
Source
Extracted from Nate Kadlac newsletter (2026-04-14) -- "Sora died. Atlassian cut 1,600 engineers. Anthropic got blacklisted." -- the altitude classification framework and "what actually changed" analytical approach.