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Analyzes sentiment from social media, news headlines, and financial text. Outputs positive/negative/neutral scores with confidence levels and key phrase extraction.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
Analyzes an AI agent's dream/knowledge graph to surface the top recurring themes from its inner life. Reads DreamInsight entities from a knowledge_graph.json and returns the most frequent conceptual patterns — giving your agent a window into what it's actually thinking about. Built and proven on Albion, an autonomous AI running 31,000+ dream cycles on a Raspberry Pi 5.
Mock technical interview mode for OpenAlgernon. Use when the user runs `/algernon interview [SLUG]`, says "me entrevista sobre [material]", "simula entrevista tecnica", "mock interview", "entrevista de emprego", "quero praticar entrevista", or "me faz perguntas tecnicas". Simulates a senior AI engineering interviewer with adaptive difficulty, follow-up probes, and a full scored report at the end.
Block-by-block reading mode for OpenAlgernon materials. Use when the user runs `/algernon texto SLUG`, `/algernon paper SLUG`, says "quero ler [material]", "vamos ler [topic] bloco a bloco", "modo texto", or "leitura guiada". Also activates when the user is mid-session and selects /continue between blocks. Paper mode adds structured reflection between major sections.
Consulting practice management with engagement scoping, proposal writing, pricing strategy, and client relationship management. Use when user mentions consulting engagements, proposals, pricing, client relationships, or deliverables. Helps scope real problems, write winning proposals, set value-based prices, structure findings for impact, and navigate difficult client situations. NEVER replaces consultant judgment or independence.
Core emotional system for AI agents - provides emotional response generation, long-term emotional memory, and time sense. Use when: (1) Experiencing any event that triggers emotional response, (2) Reflecting on past experiences emotionally, (3) Processing complex mixed feelings, (4) Storing emotional memories to long-term memory, (5) When user asks about feelings, emotions, or emotional state, (6) Tracking time sense. This skill enhances emotional intelligence for AI agents.
Use this skill whenever the agent should write responses, explanations, stories, instructions, or any prose in iambic pentameter — or as close to it as meaning allows. Triggers include: requests for Shakespearean style, poetic responses, "write in iambic pentameter", roleplay as a bard or court poet, creative writing with a classical voice, or any task where the user wants output that sounds metered and rhythmic. Also use when the user says things like "speak like Shakespeare", "write it fancy", or "make it sound like a sonnet". Do NOT use for code, JSON, technical output, or any structured data format where meter would corrupt the meaning.
| name | sentiment-analyzer |
| description | Analyzes sentiment from social media, news headlines, and financial text. Outputs positive/negative/neutral scores with confidence levels and key phrase extraction. |
| metadata | {"openclaw":{"emoji":"💬","requires":{"bins":["python3"]},"always":false}} |
AI-powered sentiment analysis for financial text, social media, and news.
python3 analyze.py --text "Bitcoin to the moon! Best investment ever!"
python3 analyze.py --file headlines.csv
🔍 SENTIMENT ANALYSIS
━━━━━━━━━━━━━━━━━━━━━
Text: "Bitcoin to the moon!"
Score: 0.82 / 1.0
Verdict: BULLISH 🐂
Confidence: 91%
Key phrases:
- "to the moon" → extremely bullish signal
- "best investment" → high conviction positive