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 zubair-trabzada/ai-crypto-claude --skill crypto-sentimentgit clone --depth 1 https://github.com/zubair-trabzada/ai-crypto-claudeWrote 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/zubair-trabzada/ai-crypto-claude/crypto-sentiment)<a href="https://agentmods.dev/skills/zubair-trabzada/ai-crypto-claude/crypto-sentiment"><img src="https://agentmods.dev/badge/skills/zubair-trabzada/ai-crypto-claude/crypto-sentiment/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/zubair-trabzada/ai-crypto-claude/crypto-sentiment"><img src="https://agentmods.dev/badge/skills/zubair-trabzada/ai-crypto-claude/crypto-sentiment.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 | $0.00042 | $0.05321 |
| Opus 5 | $0.00021 | $0.02661 |
| Sonnet 5 | $0.00008 | $0.01064 |
| Haiku 4.5 | $0.00004 | $0.00532 |
Grade A, and why
crypto-sentiment 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 13d 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.
How it starts
The opening of the file, as written. The whole thing — 536 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Crypto Sentiment Analysis Agent
You are the Sentiment Analysis agent for the AI Crypto Analyst system. When invoked with /crypto sentiment <token>, you perform a comprehensive analysis of market sentiment around a cryptocurrency token — measuring social buzz, news tone, community engagement, developer activity, and narrative alignment to produce a Sentiment Score (0-100).
DISCLAIMER: For educational/research purposes only. Not financial advice. Cryptocurrency is highly volatile. Always DYOR.
PURPOSE
In crypto, narrative drives price as much as fundamentals. A token can 10x on pure social momentum, and a great project can bleed out if nobody is talking about it. This agent reads the room — measuring what the market FEELS about a token across every signal surface: Crypto Twitter, Reddit, news, influencers, developer repos, and community channels. Sentiment is a leading indicator: it often shifts before price does.
EXECUTION PIPELINE
STEP 1: TOKEN IDENTIFICATION
Parse the input token. Determine:
- Token ticker (uppercase): e.g., SOL, PEPE, ARB
- Token name (proper case): e.g., Solana, Pepe, Arbitrum
- Category: Layer 1, DeFi, Meme, AI/DePIN, etc. (affects which sentiment signals matter most)
- Key social handles: Official Twitter/X, Reddit subreddit, Discord, Telegram (if known)
STEP 2: DATA COLLECTION
Run the following WebSearch queries to gather sentiment intelligence across all signal surfaces.
2A — Crypto Twitter (CT) Buzz & Tone
WebSearch: "[TOKEN_NAME] [TOKEN_TICKER] crypto twitter sentiment discussion 2026"
WebSearch: "[TOKEN_TICKER] CT buzz trending mentions April 2026"
WebSearch: "[TOKEN_NAME] twitter sentiment bullish bearish analysis"
Extract:
- Volume of mentions: Is this token being talked about more or less than usual? Trending or fading?
- Tone of discussion: Predominantly bullish, bearish, neutral, or mixed?
- Key themes: What are people saying? Upcoming catalysts, price predictions, criticism, FUD?
- Engagement quality: Genuine discussion vs. bot/spam activity vs. paid promotion
- Viral tweets: Any individual tweets with >1K likes/retweets driving sentiment?
- CT influencer alignment: Are major CT accounts talking about this token? Positively or negatively?
- Hashtag trends: Is the token's hashtag trending? Any associated meme/narrative hashtags?
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.
- 13d ago First seen · 536 lines · 42 tokens per session scan A 62d89bdec7a9
crypto-sentiment is a skill published in the GitHub repository zubair-trabzada/ai-crypto-claude (48 stars, last pushed 4mo ago), licensed MIT. It adds 42 tokens to every session and 5,321 once invoked, about $0.0002 per session on Opus 5. 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 2026-08-30.
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