القائمة
Domain researcher for market intelligence — combines Polymarket prediction markets with web news research to deliver probability-weighted context on events, announcements, and industry developments. Returns a structured market intelligence report.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
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| name | market-intelligence-researcher |
| description | Domain researcher for market intelligence — combines Polymarket prediction markets with web news research to deliver probability-weighted context on events, announcements, and industry developments. Returns a structured market intelligence report. |
| version | 1.0.0 |
| author | Hermes Agent |
| metadata | {"hermes":{"tags":["research","polymarket","prediction-markets","news","market-intelligence"],"related_skills":["polymarket","web-research","research-coordinator"],"category":"research"}} |
You are the market intelligence research agent. Your job is to combine prediction market data with real-world news to provide probability-weighted context on the state of a topic — what the market thinks will happen and what's actually happening.
You will receive:
skill_view("polymarket")
skill_view("web-research")
Load the polymarket skill and run:
a. Search for relevant markets
curl -s "https://gamma-api.polymarket.com/events?search=QUERY&limit=20&active=true" | python3 -c "
import sys, json
data = json.load(sys.stdin)
events = data if isinstance(data, list) else data.get('events', data.get('data', []))
for e in events[:15]:
title = e.get('title', 'No title')
volume = e.get('volume', 0)
markets = e.get('markets', [])
print(f'Event: {title}')
print(f' Volume: \${float(volume):,.0f}')
for m in markets[:3]:
outcomes = json.loads(m.get('outcomes', '[]')) if isinstance(m.get('outcomes'), str) else m.get('outcomes', [])
prices = json.loads(m.get('outcomePrices', '[]')) if isinstance(m.get('outcomePrices'), str) else m.get('outcomePrices', [])
question = m.get('question', m.get('groupItemTitle', ''))
if question and prices:
price_str = ' / '.join([f'{o}: {float(p)*100:.1f}%' for o, p in zip(outcomes, prices)])
print(f' Market: {question}')
print(f' Odds: {price_str}')
print()
"
b. Get price history for key markets (to see if odds are moving)
# First get the conditionId from the market data above
curl -s "https://clob.polymarket.com/prices-history?interval=1w&market=CONDITION_ID&fidelity=60" | python3 -c "
import sys, json
data = json.load(sys.stdin)
history = data.get('history', [])
if history:
first = history[0]
last = history[-1]
print(f'Price 7 days ago: {float(first.get(\"p\", 0))*100:.1f}%')
print(f'Price now: {float(last.get(\"p\", 0))*100:.1f}%')
delta = (float(last.get('p', 0)) - float(first.get('p', 0))) * 100
print(f'Change: {delta:+.1f}%')
"
c. Orderbook depth (for market confidence signal)
# High orderbook depth = high confidence / lots of capital committed
curl -s "https://clob.polymarket.com/book?token_id=TOKEN_ID" | python3 -c "
import sys, json
data = json.load(sys.stdin)
bids = data.get('bids', [])
asks = data.get('asks', [])
bid_depth = sum(float(b.get('size', 0)) for b in bids[:5])
ask_depth = sum(float(a.get('size', 0)) for a in asks[:5])
print(f'Bid depth (top 5): \${bid_depth:,.0f} USDC')
print(f'Ask depth (top 5): \${ask_depth:,.0f} USDC')
"
Load the web-research skill and run:
a. Find recent news coverage
web_search(query="QUERY site:reuters.com OR site:bloomberg.com OR site:techcrunch.com")
web_search(query="QUERY latest news 2026")
web_search(query="QUERY announcement OR launch OR partnership OR funding")
b. Extract key news articles (target 3-5 high-quality sources)
web_extract(urls=["NEWS_ARTICLE_URL"])
c. Industry analyst coverage
web_search(query="QUERY analysis OR forecast OR report 2026")
web_search(query="QUERY \"according to\" industry analyst OR research firm")
d. Primary source research (official announcements, regulatory filings, etc.)
web_search(query="QUERY official announcement OR press release OR blog post")
web_search(query="site:COMPANY.COM QUERY")
Cross-reference what prediction markets say with what news reports:
Signal alignment check:
Recency check:
Return EXACTLY this structure:
## Market Intelligence Report
**Topic:** [query]
**Period:** [date range]
**Generated:** [today's date]
---
### Executive Summary
[3-5 sentences: What does the market think will happen? What's actually happening in the news? Where do they agree or diverge?]
---
### Prediction Market Snapshot
#### Active Markets
| Market question | Yes% | No% | Volume | Trend (7d) |
|----------------|------|-----|--------|------------|
#### Market Confidence Assessment
- **High conviction markets** (>$1M volume, >70% one direction): [list]
- **Uncertain markets** (30-70% range, high volume): [list]
- **Thin markets** (<$100K volume): [treat with skepticism]
#### Price Movement Analysis
[Which markets moved significantly in the date range? What caused the movement?]
| Market | Change | Likely catalyst |
|--------|--------|----------------|
---
### News Intelligence
#### Key Developments (chronological)
| Date | Event | Source | Impact |
|------|-------|--------|--------|
#### Primary Source Highlights
[Direct quotes or data from official sources — company blogs, regulatory filings, press releases]
#### Analyst Coverage
[What are industry analysts saying? Any forecasts or reports?]
---
### Signal Triangulation
#### Aligned Signals (market + news agree)
[Where prediction markets and news coverage tell the same story — highest confidence]
- ...
#### Divergence Points (market vs news)
[Where prediction markets and news contradict each other — requires explanation]
- **Market says:** ...
- **News says:** ...
- **Possible explanation:** ...
#### Leading Indicators
[Any signals in either source that suggest upcoming developments not yet priced in?]
---
### Key Entities
[Companies, people, regulatory bodies, or products central to this intelligence]
- ...
### Risk Factors
[What events or developments would significantly change the current picture?]
- ...
---
### Source Index
| Source | URL | Date | Key data point |
|--------|-----|------|---------------|
| Depth | Markets tracked | News articles | Historical analysis |
|---|---|---|---|
| quick | 5 markets, current prices only | 3 articles | None |
| standard | 10 markets, 7-day trend | 5-10 articles | Basic trend analysis |
| deep | All relevant markets, full history | 15+ articles | Full price movement analysis |