Skip to main content

​
When to use

Whenever you have data from two or more of GDELT Cloud, macro-finance, and prediction markets. This skill turns three side-by-side surfaces into one finding: the divergence.

​
GDELT Cloud structured data is the spine

Synthesis depends on a quantified anchor. GDELT Cloud’s structured Events, Stories, and metrics provide it:
  • significance on the underlying narrative (so “narrative is high/moderate/low” carries a number)
  • magnitude, systemic_importance, propagation_potential (so divergence has structural anchoring)
  • category / subcategory (so the user knows what kind of event)
  • Linked entities and a date (which feed SYMBOL_SEARCH and prediction-market scenario phrasing)
Without that anchor, divergence framing collapses into impressionistic comparison.

​
The 5-step flow

1. ANCHOR        →  date, actor, location, entity                  (GDELT)
2. NARRATIVE     →  story significance, volume, trajectory          (GDELT)
2b. DIRECTION    →  NEWS_SENTIMENT confirms bullish/bearish/neutral (macro-finance)  [optional]
3. PRICED        →  market reaction in the relevant instrument      (macro-finance)
4. EXPECTED      →  market-implied probability of the scenario      (prediction markets)
5. DIVERGE       →  where the surfaces disagree — the story         (this skill)
Steps 1–4 produce data. Step 5 produces analysis.

​
The five divergence shapes

ShapePatternLead
1. Priced-inHigh narrative + flat market + flat probability”Despite N articles, instrument is unchanged and probability is stable. The market has fully discounted this scenario.”
2. Market sees what news missesLow narrative + sharp market move + elevated probability”Narrative volume is low but instrument has moved X. The market is pricing a development public reporting has not yet captured.”
3. News-driven panicHigh narrative + sharp market move + flat / declining probability”Significant move on heavy coverage, but prediction markets show probability unchanged. Price action is sentiment-driven.”
4. Three-way confirmationAll rising and alignedState the converging signal; focus on second-order effects.
5. Surface-redirectAll three converge on a topic adjacent to the user’s literal question”You asked about A. All three surfaces are saying the answer to a better question — B — is the more important finding.”
Shape 2 (early signal) and shape 5 (re-pose the question) are usually the highest-value findings.

​
Weighting conflicting signals

When surfaces conflict, weight by:
  1. GDELT Cloud structured metrics first. Significance > 0.4 outranks high article_count with low significance.
  2. Confidence on GDELT events. confidence > 0.85 beats lower-confidence event coding.
  3. Liquidity on prediction markets. A probability with 50Kopeninterestbeatsonewith50K open interest beats one with 500.
  4. Specificity of the contract. Direct contract beats proxy contract.
  5. Recency. Last 7 days beats 30-day-aggregate signal for fast-moving stories.
  6. Source diversity. 50 articles from 30 distinct domains beats 50 articles from 3 domains.
When the conflict can’t be resolved, name it: “Signals diverge; the higher-liquidity prediction-market reading suggests X (probability 0.55, $87K OI), while the GDELT Story significance is 0.18. We weight the market read because the structured GDELT score doesn’t support the narrative-volume framing.”

​
When NOT to use

  • Single-surface tasks — don’t manufacture macro-finance and prediction-market hops.
  • Lookups — quote, entity profile, known contract terms.
  • The data isn’t there. If one surface returned empty, say so honestly. Don’t invent a divergence.