Agent skill

Analyzing Market Sentiment

This skill provides comprehensive cryptocurrency market sentiment analysis by combining multiple data sources Analyze cryptocurrency market sentiment using Fear & Greed Index, news analysis, and…

MITAuto-check passedBusiness, Finance & HR

Install Analyzing Market Sentiment

skills CLI

$ npx skills add aAAaqwq/AGI-Super-Team --skill analyzing-market-sentiment -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI

$ gh skill install aAAaqwq/AGI-Super-Team analyzing-market-sentiment --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy

$ git clone --depth 1 https://github.com/aAAaqwq/AGI-Super-Team.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/market-sentiment-analyzer .claude/skills/analyzing-market-sentiment && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Paste into Claude Code

Install the "analyzing-market-sentiment" agent skill from https://github.com/aAAaqwq/AGI-Super-Team/tree/main/skills/market-sentiment-analyzer into .claude/skills/analyzing-market-sentiment/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "analyzing-market-sentiment", then confirm the skill loads.

Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

$skill-installer

$skill-installer install https://github.com/aAAaqwq/AGI-Super-Team/tree/main/skills/market-sentiment-analyzer

Type this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.

skills CLI

$ npx skills add aAAaqwq/AGI-Super-Team --skill analyzing-market-sentiment -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install aAAaqwq/AGI-Super-Team analyzing-market-sentiment --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/aAAaqwq/AGI-Super-Team.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/market-sentiment-analyzer .agents/skills/analyzing-market-sentiment && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "analyzing-market-sentiment" agent skill from https://github.com/aAAaqwq/AGI-Super-Team/tree/main/skills/market-sentiment-analyzer into .agents/skills/analyzing-market-sentiment/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "analyzing-market-sentiment", then confirm the skill loads.

Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add aAAaqwq/AGI-Super-Team --skill analyzing-market-sentiment -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install aAAaqwq/AGI-Super-Team analyzing-market-sentiment --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/aAAaqwq/AGI-Super-Team.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/market-sentiment-analyzer .cursor/skills/analyzing-market-sentiment && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "analyzing-market-sentiment" agent skill from https://github.com/aAAaqwq/AGI-Super-Team/tree/main/skills/market-sentiment-analyzer into .cursor/skills/analyzing-market-sentiment/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "analyzing-market-sentiment", then confirm the skill loads.

Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

gemini skills install

$ gemini skills install https://github.com/aAAaqwq/AGI-Super-Team.git --path skills/market-sentiment-analyzer

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add aAAaqwq/AGI-Super-Team --skill analyzing-market-sentiment -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install aAAaqwq/AGI-Super-Team analyzing-market-sentiment --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/aAAaqwq/AGI-Super-Team.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/market-sentiment-analyzer .gemini/skills/analyzing-market-sentiment && rm -rf skills-src

Use ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.

Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/

Paste into Gemini CLI

Install the "analyzing-market-sentiment" agent skill from https://github.com/aAAaqwq/AGI-Super-Team/tree/main/skills/market-sentiment-analyzer into .gemini/skills/analyzing-market-sentiment/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "analyzing-market-sentiment", then confirm the skill loads.

Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

GitHub CLI

$ gh skill install aAAaqwq/AGI-Super-Team analyzing-market-sentiment

Installs for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).

skills CLI

$ npx skills add aAAaqwq/AGI-Super-Team --skill analyzing-market-sentiment -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/aAAaqwq/AGI-Super-Team.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/market-sentiment-analyzer .github/skills/analyzing-market-sentiment && rm -rf skills-src

Use ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.

GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/

Paste into GitHub Copilot

Install the "analyzing-market-sentiment" agent skill from https://github.com/aAAaqwq/AGI-Super-Team/tree/main/skills/market-sentiment-analyzer into .github/skills/analyzing-market-sentiment/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "analyzing-market-sentiment", then confirm the skill loads.

GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add aAAaqwq/AGI-Super-Team --skill analyzing-market-sentiment -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install aAAaqwq/AGI-Super-Team analyzing-market-sentiment --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/aAAaqwq/AGI-Super-Team.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/market-sentiment-analyzer .opencode/skills/analyzing-market-sentiment && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "analyzing-market-sentiment" agent skill from https://github.com/aAAaqwq/AGI-Super-Team/tree/main/skills/market-sentiment-analyzer into .opencode/skills/analyzing-market-sentiment/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "analyzing-market-sentiment", then confirm the skill loads.

OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

Facts

Skill name: analyzing-market-sentiment
GitHub stars: 105
Token cost: ~1.6k tokens
SKILL.md length: 337 words
Files: 12 (incl. scripts, references)
Skills in repo: 167
Repo updated: Oct 8, 2026
First seen: Oct 7, 2026
Licence: MIT

At a glance

This skill provides comprehensive cryptocurrency market sentiment analysis by combining multiple data sources Analyze cryptocurrency market sentiment using Fear & Greed Index, news analysis, and…

Works in 3 steps: Assess User Intent → Execute Sentiment Analysis → Present Results
Gauging overall market mood
SKILL.md covers Overview, Prerequisites, Instructions and Output, plus 3 more sections
Runs Python scripts from its folder; calls python and pip

What it does

Analyzing Market Sentiment is an agent skill from aAAaqwq/AGI-Super-Team. This skill provides comprehensive cryptocurrency market sentiment analysis by combining multiple data sources Analyze cryptocurrency market sentiment using Fear & Greed Index, news analysis, and market momentum. Use when gauging overall market mood, checking if markets are fearful or greedy, or analyzing sentiment for specific coins. Trigger with phrases like "analyze crypto sentiment", "check market mood", "is the market fearful", "sentiment for Bitcoin", or "Fear and Greed index".

Its SKILL.md is about 1.6k tokens, which your agent loads only when the skill is triggered. The skill folder holds 14 other files, including scripts and reference files (for example `ARD.md`, `PRD.md` and `config/settings.yaml`).

It sits in Business, Finance & HR, covering Crypto and DeFi analysis and Customer feedback analysis. It works with Bitcoin. The repository describes itself as: An installable, cross-framework AI organization: C-suite agents, expert subagents, curated skills, independent review, and one-command setup across 18 AI client/runtime adapters. The licence is MIT.

When your agent uses it

Gauging overall market mood
Checking if markets are fearful
Analyzing sentiment for specific coins
With phrases like analyze crypto sentiment

Example prompts

“analyze crypto sentiment”
“check market mood”
“is the market fearful”
“/analyzing-market-sentiment”

Requirements

Python 3
Pre-approved tools (allowed-tools): Read, Bash(crypto:sentiment-*)

Workflow steps

3 steps, taken from the step headings in SKILL.md.

Assess User Intent
Execute Sentiment Analysis
Present Results

What it can do on your machine

Read from SKILL.md and the folder at commit 7cefd81. It shows what the files ask for, not the result of running them.

Tool permissions
Pre-approves these tools, so the agent can use them without asking each time:
- Read
- Bash(crypto:sentiment-*)
From allowed-tools in the SKILL.md frontmatter.
Runs code
Ships 5 files in scripts/ (Python), which the agent can run.
Shell commands in SKILL.md call:
- python
- pip
From the folder's file list and the shell code blocks in SKILL.md.
Network
Links to these hosts (documentation or services it may open):
- alternative.me
- coingecko.com
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Analyzing Market Sentiment loads about 1.6k tokens when it runs, and up to ~4.6k if it reads all its reference files. Until then it costs about 129 tokens; SKILL.md has 337 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~129
When it runs · the whole SKILL.md, loaded when a task matches: ~1.6k
With references · SKILL.md plus every file in references/, read only if the agent opens them: ~4.6k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from aAAaqwq/AGI-Super-Team at commit 7cefd81, republished under its MIT licence (© aAAaqwq). 337 words, ~1,590 tokens.

Download SKILL.mdSave it as .claude/skills/analyzing-market-sentiment/SKILL.md (or your agent's skills folder). This skill also uses 11 other files; get the full folder from GitHub.

name: analyzing-market-sentiment
description: This skill provides comprehensive cryptocurrency market sentiment analysis by combining multiple data sources Analyze cryptocurrency market sentiment using Fear & Greed Index, news analysis, and market momentum. Use when gauging overall market mood, checking if markets are fearful or greedy, or analyzing sentiment for specific coins. Trigger with phrases like "analyze crypto sentiment", "check market mood", "is the market fearful", "sentiment for Bitcoin", or "Fear and Greed index".
allowed-tools: Read, Bash(crypto:sentiment-*)
version: 2.0.0
author: Jeremy Longshore <jeremy@intentsolutions.io>
license: MIT

Analyzing Market Sentiment

Overview

This skill provides comprehensive cryptocurrency market sentiment analysis by combining multiple data sources:

Fear & Greed Index: Market-wide sentiment from Alternative.me
News Sentiment: Keyword-based analysis of recent crypto news
Market Momentum: Price and volume trends from CoinGecko

Key Capabilities:

Composite sentiment score (0-100) with classification
Coin-specific sentiment analysis
Detailed breakdown of sentiment components
Multiple output formats (table, JSON, CSV)

Prerequisites

Before using this skill, ensure:

Python 3.8+ is installed
requests library is available: pip install requests
Internet connectivity for API access (Alternative.me, CoinGecko)
Optional: crypto-news-aggregator skill for enhanced news analysis

Instructions

Step 1: Assess User Intent

Determine what sentiment analysis the user needs:

Overall market: No specific coin, general sentiment
Coin-specific: Extract coin symbol (BTC, ETH, etc.)
Quick vs detailed: Quick score or full breakdown

Step 2: Execute Sentiment Analysis

Run the sentiment analyzer with appropriate options:

bash

# Quick sentiment check (default)
python {baseDir}/scripts/sentiment_analyzer.py

# Coin-specific sentiment
python {baseDir}/scripts/sentiment_analyzer.py --coin BTC

# Detailed analysis with component breakdown
python {baseDir}/scripts/sentiment_analyzer.py --detailed

# Export to JSON
python {baseDir}/scripts/sentiment_analyzer.py --format json --output sentiment.json

# Custom time period
python {baseDir}/scripts/sentiment_analyzer.py --period 7d --detailed

Step 3: Present Results

Format and present the sentiment analysis:

Show composite score and classification
Explain what the sentiment means
Highlight any extreme readings
For detailed mode, show component breakdown

Command-Line Options

Option	Description	Default
`--coin`	Analyze specific coin (BTC, ETH, etc.)	All market
`--period`	Time period (1h, 4h, 24h, 7d)	24h
`--detailed`	Show full component breakdown	false
`--format`	Output format (table, json, csv)	table
`--output`	Output file path	stdout
`--weights`	Custom weights (e.g., "news:0.5,fng:0.3,momentum:0.2")	Default
`--verbose`	Enable verbose output	false

Sentiment Classifications

Score Range	Classification	Description
0-20	Extreme Fear	Market panic, potential bottom
21-40	Fear	Cautious sentiment, bearish
41-60	Neutral	Balanced, no strong bias
61-80	Greed	Optimistic, bullish sentiment
81-100	Extreme Greed	Euphoria, potential top

Output

Table Format (Default)

==============================================================================
  MARKET SENTIMENT ANALYZER                         Updated: 2026-01-14 15:30
==============================================================================

  COMPOSITE SENTIMENT
------------------------------------------------------------------------------
  Score: 65.5 / 100                         Classification: GREED

  Component Breakdown:
  - Fear & Greed Index:  72.0  (weight: 40%)  → 28.8 pts
  - News Sentiment:      58.5  (weight: 40%)  → 23.4 pts
  - Market Momentum:     66.5  (weight: 20%)  → 13.3 pts

  Interpretation: Market is moderately greedy. Consider taking profits or
  reducing position sizes. Watch for reversal signals.

==============================================================================

JSON Format

json

{
  "composite_score": 65.5,
  "classification": "Greed",
  "components": {
    "fear_greed": {
      "score": 72,
      "classification": "Greed",
      "weight": 0.40,
      "contribution": 28.8
    },
    "news_sentiment": {
      "score": 58.5,
      "articles_analyzed": 25,
      "positive": 12,
      "negative": 5,
      "neutral": 8,
      "weight": 0.40,
      "contribution": 23.4
    },
    "market_momentum": {
      "score": 66.5,
      "btc_change_24h": 3.5,
      "weight": 0.20,
      "contribution": 13.3
    }
  },
  "meta": {
    "timestamp": "2026-01-14T15:30:00Z",
    "period": "24h"
  }
}

Error Handling

See {baseDir}/references/errors.md for comprehensive error handling.

Error	Cause	Solution
Fear & Greed unavailable	API down	Uses cached value with warning
News fetch failed	Network issue	Reduces weight of news component
Invalid coin	Unknown symbol	Proceeds with market-wide analysis

Examples

See {baseDir}/references/examples.md for detailed examples.

Quick Examples

bash

# Quick market sentiment check
python {baseDir}/scripts/sentiment_analyzer.py

# Bitcoin-specific sentiment
python {baseDir}/scripts/sentiment_analyzer.py --coin BTC

# Detailed analysis
python {baseDir}/scripts/sentiment_analyzer.py --detailed

# Export for trading model
python {baseDir}/scripts/sentiment_analyzer.py --format json --output sentiment.json

# Custom weights (emphasize news)
python {baseDir}/scripts/sentiment_analyzer.py --weights "news:0.5,fng:0.3,momentum:0.2"

# Weekly sentiment comparison
python {baseDir}/scripts/sentiment_analyzer.py --period 7d --detailed

Resources

Alternative.me Fear & Greed Index: https://alternative.me/crypto/fear-and-greed-index/
CoinGecko API: https://www.coingecko.com/en/api
Sentiment Analysis Theory: Contrarian indicator - extreme readings often precede reversals
See {baseDir}/config/settings.yaml for configuration options

Files

SKILL.md and 11 other files (scripts, references) in skills/market-sentiment-analyzer of aAAaqwq/AGI-Super-Team.

SKILL.md
ARD.md
PRD.md
config/settings.yaml
references/errors.md
references/examples.md
references/implementation.md
scripts/fear_greed.py
scripts/formatters.py
scripts/market_momentum.py
scripts/news_sentiment.py
scripts/sentiment_analyzer.py

Open the folder on GitHubat commit 7cefd81

Compare with similar skills

Analyzing Market Sentiment next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Analyzing Market Sentiment compared with similar skills
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Okx Sentiment Trackerdex-original/okx-agent-trade-kit	110	1 repos	~3.8k	Automated safety check: Pass	MIT	4 mo ago
Okx Sentiment Trackerokx/agent-skills	187	—	~5.2k	Automated safety check: Pass	MIT	18 days ago
ApocdataApocData/ApocData-skill	104	—	~1.9k	Automated safety check: Pass	Apache-2.0	26 days ago
Academy Skillbinance/binance-skills-hub	1.1k	—	~3.5k	Automated safety check: Notes	None	1 mo ago
Alpha Vantageagent-skills-hub/agent-skills-hub	112	2 repos	~1.6k	Automated safety check: Pass	MIT	9 days ago

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Questions about Analyzing Market Sentiment

What does Analyzing Market Sentiment do?

This skill provides comprehensive cryptocurrency market sentiment analysis by combining multiple data sources Analyze cryptocurrency market sentiment using Fear & Greed Index, news analysis, and…. Analyzing Market Sentiment is an agent skill from aAAaqwq/AGI-Super-Team. This skill provides comprehensive cryptocurrency market sentiment analysis by combining multiple data sources Analyze cryptocurrency market sentiment using Fear & Greed Index, news analysis, and market momentum.

When should I use Analyzing Market Sentiment?

Analyzing Market Sentiment fits situations like: gauging overall market mood; checking if markets are fearful; analyzing sentiment for specific coins; with phrases like analyze crypto sentiment.

How do I install Analyzing Market Sentiment in Claude Code?

Run `npx skills add aAAaqwq/AGI-Super-Team --skill analyzing-market-sentiment -a claude-code`. Or copy the skill folder (skills/market-sentiment-analyzer in aAAaqwq/AGI-Super-Team) into .claude/skills/analyzing-market-sentiment in your project. Claude Code loads it when a task matches its description.

How do I install Analyzing Market Sentiment in Codex?

Run `npx skills add aAAaqwq/AGI-Super-Team --skill analyzing-market-sentiment -a codex`. Or copy the skill folder (skills/market-sentiment-analyzer in aAAaqwq/AGI-Super-Team) into .agents/skills/analyzing-market-sentiment in your project. Codex loads it when a task matches its description.

Can I use Analyzing Market Sentiment in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add aAAaqwq/AGI-Super-Team --skill analyzing-market-sentiment -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/analyzing-market-sentiment, .gemini/skills/analyzing-market-sentiment, .github/skills/analyzing-market-sentiment and .opencode/skills/analyzing-market-sentiment in your project.

What does Analyzing Market Sentiment need to run?

Going by SKILL.md and its folder, Analyzing Market Sentiment needs Python for the scripts in its folder and the command-line tools its instructions call (python and pip). Our summary lists: Python 3. Its frontmatter pre-approves these tools: Read, Bash(crypto:sentiment-*).

Does Analyzing Market Sentiment access the network?

SKILL.md names 2 domains. As links in the text: alternative.me and coingecko.com. This is read from the text; nothing was executed.

Is Analyzing Market Sentiment safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Analyzing Market Sentiment use?

Analyzing Market Sentiment is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Analyzing Market Sentiment use?

About 1.6k tokens (SKILL.md is roughly 6.4k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 3.1k tokens, read only when the agent opens those files.

What are the alternatives to Analyzing Market Sentiment?

Skills that share tags, products or a category with Analyzing Market Sentiment: Okx Sentiment Tracker (dex-original/okx-agent-trade-kit, 110 stars), Okx Sentiment Tracker (okx/agent-skills, 187 stars), Apocdata (ApocData/ApocData-skill, 104 stars) and Academy Skill (binance/binance-skills-hub, 1.1k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Analyzing Market Sentiment?

aAAaqwq (a GitHub user) maintains it in aAAaqwq/AGI-Super-Team, which has 105 GitHub stars. The repository holds 167 skills in this directory. The repository was last updated on October 8, 2026.

Source: aAAaqwq/AGI-Super-Team on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Install Analyzing Market Sentiment

Facts

At a glance

What it does

When your agent uses it

Example prompts

Requirements

Workflow steps

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

Analyzing Market Sentiment

Overview

Prerequisites

Instructions

Step 1: Assess User Intent

Step 2: Execute Sentiment Analysis

Step 3: Present Results

Command-Line Options

Sentiment Classifications

Output

Table Format (Default)

JSON Format

Error Handling

Examples

Quick Examples

Resources

Files

Compare with similar skills

Similar skills

More from aAAaqwq/AGI-Super-Team

Related

Questions about Analyzing Market Sentiment