Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add Signal-Execution-Labs/forex-trading-ai-agent --skill sentiment-analyzergit clone --depth 1 https://github.com/Signal-Execution-Labs/forex-trading-ai-agentWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/signal-execution-labs/forex-trading-ai-agent/sentiment-analyzer)<a href="https://agentmods.dev/skills/signal-execution-labs/forex-trading-ai-agent/sentiment-analyzer"><img src="https://agentmods.dev/badge/skills/signal-execution-labs/forex-trading-ai-agent/sentiment-analyzer/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/signal-execution-labs/forex-trading-ai-agent/sentiment-analyzer"><img src="https://agentmods.dev/badge/skills/signal-execution-labs/forex-trading-ai-agent/sentiment-analyzer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00000 | $0.00672 |
| Opus 5 | $0.00000 | $0.00336 |
| Sonnet 5 | $0.00000 | $0.00134 |
| Haiku 4.5 | $0.00000 | $0.00067 |
Grade A, and why
sentiment-analyzer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
1 near-identical copy found in the catalogue:
- sentiment-analyzer — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 114 lines — stays where its author put it; the contents beside it link to each section on GitHub.
📊 Sentiment Analyzer
K.I.T.'s Social Intelligence - Know what the market FEELS before it moves!
Features
🐦 Twitter/X Sentiment
- Real-time crypto tweets analysis
- Influencer tracking (Elon, CZ, Vitalik, etc.)
- Hashtag volume monitoring
- Viral tweet detection
🤖 Reddit Sentiment
- r/wallstreetbets analysis
- r/cryptocurrency monitoring
- r/Bitcoin, r/ethereum tracking
- Meme coin detection
- FOMO/FUD scoring
📰 News Sentiment
- Crypto news headlines
- Bloomberg, Reuters, CoinDesk
- Breaking news alerts
- Regulatory news detection
😱 Fear & Greed Index
- Alternative.me integration
- Historical correlation analysis
- Extreme fear = BUY signals
- Extreme greed = SELL signals
📈 Social Volume
- Mention volume tracking
- Unusual activity detection
- Pump group monitoring
- Whale wallet tracking mentions
Usage
from sentiment_analyzer import SentimentEngine
engine = SentimentEngine()
# Get overall sentiment
sentiment = await engine.analyze(
symbol="BTC",
sources=["twitter", "reddit", "news"]
)
print(f"Overall: {sentiment.score:.2f}") # -1 to 1
print(f"Mood: {sentiment.mood}") # BULLISH/BEARISH/NEUTRAL
print(f"Fear & Greed: {sentiment.fear_greed}")
print(f"Social Volume: {sentiment.volume_change:+.1%}")
# Track specific influencer
alerts = await engine.track_influencer("elonmusk")
# Get trending topics
trending = await engine.get_trending(limit=10)
Sentiment Signals
| Score | Interpretation | Action |
|---|---|---|
| > 0.7 | Extreme Greed | Consider selling |
| 0.3-0.7 | Bullish | Hold/accumulate |
| -0.3-0.3 | Neutral | Wait for signal |
| -0.7--0.3 | Bearish | Reduce exposure |
| < -0.7 | Extreme Fear | Consider buying |
Configuration
sentiment_analyzer:
twitter:
api_key: ${TWITTER_API_KEY}
influencers:
- elonmusk
- caborek
- VitalikButerin
keywords:
- bitcoin
- crypto
- ethereum
reddit:
client_id: ${REDDIT_CLIENT_ID}
client_secret: ${REDDIT_CLIENT_SECRET}
subreddits:
- wallstreetbets
- cryptocurrency
- Bitcoin
news:
sources:
- coindesk
- cointelegraph
- bloomberg
alerts:
extreme_sentiment: true
influencer_tweets: true
unusual_volume: true
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 8d ago First seen · 114 lines · 0 tokens per session scan A 176edfc69a6e
sentiment-analyzer is a skill published in the GitHub repository Signal-Execution-Labs/forex-trading-ai-agent (136 stars, last pushed 8d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 672 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
cost-efficiency-analyzer
Analyzes cost structure, cost efficiency, and expense management from P&L data. Use when the user asks about costs, expenses, COGS, operating expenses, cost ratios, cost control, spending efficiency, margin compression from cost side, or wants to understand where money is going. Also use for "are we spending too…
onboarding
First-time user onboarding to set up investment profile, watchlists, portfolio, and preferences.
equity-risk-reviewer
Use for portfolio-facing stock risk briefs that separate market facts, interpretation, and non-investment-advice boundaries.
stock-valuation
Multi-method stock valuation using DCF, comparable company analysis, EV multiples, and residual income models.
technical-analysis
Technical analysis of US stocks using charts and indicators.
chart-master
Generate professional financial charts in Mermaid, ASCII, or HTML/Chart.js — MA lines, histograms, candlestick, volume, RSI/MACD, and more for markdown reports.