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 kayzaa/k.i.t.-bot --skill sentiment-analyzergit clone --depth 1 https://github.com/kayzaa/k.i.t.-botWrote 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/kayzaa/k.i.t.-bot/sentiment-analyzer)<a href="https://agentmods.dev/skills/kayzaa/k.i.t.-bot/sentiment-analyzer"><img src="https://agentmods.dev/badge/skills/kayzaa/k.i.t.-bot/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/kayzaa/k.i.t.-bot/sentiment-analyzer"><img src="https://agentmods.dev/badge/skills/kayzaa/k.i.t.-bot/sentiment-analyzer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
This is a copy
100% identical to sentiment-analyzer — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
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 kayzaa/k.i.t.-bot (5 stars, last pushed 6mo 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. It is 100% identical to sentiment-analyzer, differing in 0 lines, and is treated as a copy.
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