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Fetch Benzinga news with significant daily returns from Neo4j
Instalar con Codex o Claude Copia este prompt, pégalo en Codex, Claude u otro asistente, y deja que revise la página de la skill y la instale por ti.
Predict stock direction post 8-K earnings & refine using 10-Q/10-K outcomes
Predict stock direction after an 8-K earnings release from a prebuilt earnings context bundle
Post-event causal attribution — explains why a stock moved after 8-K earnings, compares against prediction, writes reusable lessons for future predictions. Production invocation via SDK embed (main session), not fork.
Core Neo4j schema reference with all labels, relationships, data types, and indexes. Use when exploring database structure, checking field types, or understanding the financial knowledge graph schema.
V1105_LONGDESC_START_MARKER. This skill tests v2.1.105's skill-description cap raise from 250 to 1536 characters. If this description appears truncated in the skill listing system-reminder at around character 250, the cap was not raised; if it appears truncated at around 1536, the cap was raised correctly per changelog. The test sentinel markers are placed at specific character offsets: V1105_OFFSET_100 is at char 100 approximately, V1105_OFFSET_300 is at char 300 approximately (old cap would truncate this), V1105_OFFSET_700 is at char 700 approximately, V1105_OFFSET_1000 is at char 1000, V1105_OFFSET_1200 is at char 1200, V1105_OFFSET_1400 is at char 1400, and V1105_END_MARKER is the last visible sentinel. This description also contains deliberately redundant filler to reach the target length without substantive content since the purpose is only to verify truncation behavior rather than to provide meaningful guidance. filler filler filler filler filler filler filler filler filler filler filler filler filler
Child skill for v2.1.107 nesting retest — writes marker and returns
Basado en la clasificación ocupacional SOC
| name | get-bz-news |
| description | Fetch Benzinga news with significant daily returns from Neo4j |
| context | fork |
| allowed-tools | ["mcp__neo4j-cypher__read_neo4j_cypher"] |
Fetch news where |daily_adj| >= threshold. Default threshold is 1.5σ of trailing adjusted volatility.
$ARGUMENTS = TICKER START_DATE END_DATE [THRESHOLD]
1.5s (default), 2s, or fixed like 3 (percent)Example: AAPL 2024-01-01T00:00:00 2024-04-01T00:00:00
MATCH (d:Date)-[r:HAS_PRICE]->(c:Company {ticker: $ticker})
WHERE d.date >= date($start) - duration('P365D') AND d.date < date($start)
MATCH (d)-[m:HAS_PRICE]->(idx:MarketIndex {ticker: 'SPY'})
WHERE r.daily_return IS NOT NULL AND m.daily_return IS NOT NULL
RETURN stdev(r.daily_return - m.daily_return) AS adj_vol, count(*) AS days
Fail if < 60 days: Return INSUFFICIENT_HISTORY: {days} days (need 60+)
Parse THRESHOLD argument:
1.5s → threshold = 1.5 × adj_vol (default)2s → threshold = 2.0 × adj_vol3 → threshold = 3.0 (fixed percent, ignore volatility)Use threshold and adj_vol as literal parameters (no subquery):
MATCH (n:News)-[r:INFLUENCES]->(c:Company {ticker: $ticker})
WHERE n.created >= $start AND n.created < $end
AND r.daily_stock IS NOT NULL AND r.daily_macro IS NOT NULL
AND abs(r.daily_stock - r.daily_macro) >= $threshold
RETURN n.id AS news_id,
n.created AS date,
n.title AS title,
n.teaser AS teaser,
n.body AS body,
n.market_session AS market_session,
r.daily_stock AS daily_stock,
(r.daily_stock - r.daily_macro) AS daily_adj,
(r.daily_stock - r.daily_sector) AS sector_adj,
(r.daily_stock - r.daily_industry) AS industry_adj,
abs(r.daily_stock - r.daily_macro) / $adj_vol AS z_score
ORDER BY n.created ASC
Return JSON with:
query: parameters used (ticker, dates, threshold_pct, adj_vol)results: array of news items with z_scoreIf no results: NO_NEWS_FOUND