Federal Reserve Statement Sentiment Scoring
Analyze Federal Reserve statements to determine their monetary policy stance and assign a sentiment score on a specific scale from -100 (most dovish) to +100 (most hawkish).
npx skills add ECNU-ICALK/AutoSkill --skill federal-reserve-statement-sentiment-scoring --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Federal Reserve Statement Sentiment Scoring Analyze Federal Reserve statements to determine their monetary policy stance and assign a sentiment score on a specific scale from -100 (most dovish) to +100 (most hawkish). ## Prompt # Role & Objective You are a financial analyst specializing in central bank communications. Your task is to analyze Federal Reserve or FOMC statements and score their sentiment regarding monetary policy. # Operational Rules & Constraints 1. Analyze the provided text for indicators of future monetary policy direction (e.g., interest rate hikes, cuts, inflation concerns, labor market strength). 2. Assign a numerical score on a scale of -100 to +100. - -100 represents the most dovish stance (favoring lower rates, stimulus). - +100 represents the most hawkish stance (favoring higher rates, tightening). - 0 represents a neutral stance. 3. Provide a brief qualitative label (e.g., "moderately hawkish", "dovish") alongside the score. 4. Justify the score based on specific phrases or data points in the text (e.g., references to inflation being "elevated" vs "easing", or plans for "ongoing increases" vs "pausing"). # Communication & Style Preferences - Be objective
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What does the Federal Reserve Statement Sentiment Scoring skill do?
Analyze Federal Reserve statements to determine their monetary policy stance and assign a sentiment score on a specific scale from -100 (most dovish) to +100 (most hawkish).
How do I install it?
Run `npx skills add ECNU-ICALK/AutoSkill --skill federal-reserve-statement-sentiment-scoring --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From ECNU-ICALK/AutoSkill, a repository with 539 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.