Sentiment Analyzer

SkillCommerce & finance

Lets your agent analyze text like reviews and social posts for positive, negative, or neutral sentiment.

Use Sentiment Analyzer in Claude, ChatGPT or Ahel Desktop

Free. Sign in, add Sentiment Analyzer and connect your AI. About a minute.

Also: Claude Code · Cursor · Codex

Then ask your AI: use the Sentiment Analyzer skill

Details

Instructions available. Your AI can read the instructions. Execution depends on the setup they require.

Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.

Sentiment AnalyzerStart free
About this skill

About AI quantitative trading platform for crypto, stocks, and forex with backtesting, live trading, market data, and multi-agent research.vibe-trading ,trading-agents,ai-trader,ai-trading

What this skill tells your AI

The instructions your AI receives, as published by signal-execution-labs/forex-trading-ai-agent in skills/sentiment-analyzer/SKILL.md and read by ahel’s review.

Analyze sentiment in any text — reviews, social posts, feedback, or articles. Returns sentiment polarity (positive, negative, neutral), confidence score, and key phrases driving the sentiment. Useful for brand monitoring, customer feedback analysis, and content evaluation. Read the API map before selecting a capability.

Call SandBase capabilities

Use the capability identifiers below as discovery hints, not MCP tool names. Find the matching endpoint with sandbase_discover(q: "<provider and capability>"); use its returned name in sandbase_inspect(name: "<returned name>"). Read inputSchema, pricing, and execute_as, then call sandbase_run using execute_as.arguments.name and schema-defined arguments. If a run_id is returned, poll sandbase_run_get(run_id: "<returned run_id>") within the task budget until completed or failed; report pending or failed runs without resubmitting them automatically.

Capability identifiers

  • strale_sentiment_analyze

Workflow

  1. Understand the user's research question, target, and context.
  2. Resolve each selected capability with sandbase_discover, then inspect the returned name with sandbase_inspect to confirm the schema and pricing.
  3. Follow execute_as with sandbase_run; collect async results with sandbase_run_get as described above.
  4. Synthesize findings into a clear, evidence-backed answer.
  5. Cite sources, note evidence gaps, and separate observations from interpretations.

Guidelines

  • Always call sandbase_inspect before using any capability.
  • Cite sources and preserve attribution (URLs, usernames, dates, metrics).
  • Separate factual observations from analysis and recommendations.
  • If data is unavailable, note the gap and continue with available evidence.
  • Read-only research only. Never take actions on platforms.

Signals

GitHub stars
152
Forks
872
Last commit
Sep 2026
Advanced
Item type
skill
Key
sentiment-analyzer
Source
github.com/signal-execution-labs/forex-trading-ai-agent