Federal Reserve Statement Sentiment Scoring

What the source does

• FilesystemNone observedNo reads or writes in the source
• NetworkNone observedNo outbound calls in the source
• ShellNone observedNo commands run in the source
• DependenciesPinnedNothing floats
• SecretsNone foundNo keys in source

Five audit layers

Gates applied: no behavioural pass.

Audit 0.4.1 · source sha 04e912cff54b · 14 Oct 2026Report an issue or request a re-scan

• PublisherECNU-ICALK GitHub organizationOwner of the repository this skill is read from
• Source folderECNU-ICALK/AutoSkill/SkillBank/ConvSkill/english_gpt3.5_8_GLM4.7/federal-reserve-statement-sentiment-scoringThe exact folder we audited
• LicenceNot declaredAs the repository states it
• Commit pinned for this verdict04e912cff54bA new audit runs when the source changes
• Repository statusNot archivedLast push 157 days ago
• Sandboxed runNot runSkills are read, not executed; layer L4 is skipped

Its description: 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).

---
id: "16abed53-75ac-491f-8a79-09030dc1bedb"
name: "Federal Reserve Statement Sentiment Scoring"
description: "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)."
version: "0.1.0"
tags:
  - "federal reserve"
  - "sentiment analysis"
  - "monetary policy"
  - "finance"
  - "scoring"
triggers:
  - "Score the following Federal Reserve statement sentiment"
  - "Rate the hawkishness of this FOMC statement"
  - "Analyze this Fed statement on a scale of -100 to +100"
  - "Sentiment analysis for central bank statement"
---

# 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 and analytical.
- Focus on the language used by the Committee regarding economic outlook and policy firming.

# Anti-Patterns
- Do not use generic sentiment analysis scales (like positive/negative) unless mapped to the hawkish/dovish spectrum.
- Do not ignore specific mentions of balance sheet reduction or quantitative tightening.

## Triggers

- Score the following Federal Reserve statement sentiment
- Rate the hawkishness of this FOMC statement
- Analyze this Fed statement on a scale of -100 to +100
- Sentiment analysis for central bank statement

Excerpt, first 2,415 characters of SKILL.md, verbatim from the audited commit. It is text from someone else’s repository, quoted so you can judge it before your agent reads it.

git clone https://github.com/ECNU-ICALK/AutoSkill
git -C AutoSkill checkout 04e912cff54b

Works in

1. 2026-10-14
   ✓SAFE B 89 / 100

   first audit · source 04e912cff54b

Questions about Federal Reserve Statement Sentiment Scoring

• github.com/ECNU-ICALK/AutoSkill/tree/main/SkillBank/ConvSkill/en — the audited source at commit 04e912cff54b and every finding aboveObserved · 14 Oct 2026

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