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 kukapay/crypto-skills --skill market-sentimentgit clone --depth 1 https://github.com/kukapay/crypto-skillsWrote 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/kukapay/crypto-skills/market-sentiment)<a href="https://agentmods.dev/skills/kukapay/crypto-skills/market-sentiment"><img src="https://agentmods.dev/badge/skills/kukapay/crypto-skills/market-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/kukapay/crypto-skills/market-sentiment"><img src="https://agentmods.dev/badge/skills/kukapay/crypto-skills/market-sentiment.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk warn
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 | $0.00048 | $0.00437 |
| Opus 5 | $0.00024 | $0.00218 |
| Sonnet 5 | $0.00010 | $0.00087 |
| Haiku 4.5 | $0.00005 | $0.00044 |
Grade A, and why
market-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 11d 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.
What it actually says
Crypto Market Sentiment
Overview
This skill enables aggregation of news from popular cryptocurrency RSS feeds, performs sentiment analysis on the articles, and computes a market sentiment score ranging from -1 (highly negative) to +1 (highly positive), along with evidence-based explanations.
Workflow
Follow these steps to analyze crypto market sentiment:
- Select RSS Feeds: Choose popular crypto RSS feeds (see references/rss_feeds.md for a curated list).
- Fetch News: Retrieve recent articles from the selected feeds.
- Analyze Sentiment: Classify each article's sentiment as positive (+1), negative (-1), or neutral (0) based on content keywords and context.
- Calculate Score: Compute the average sentiment score across all articles.
- Generate Explanation: Provide evidence from the news items supporting the score.
Sentiment Classification Guidelines
- Positive (+1): News about adoption, launches, partnerships, ETF approvals, price rallies, regulatory wins, or technological breakthroughs.
- Negative (-1): News about hacks, crashes, regulatory crackdowns, liquidations, delays, or criticisms.
- Neutral (0): Factual updates, mixed outcomes, or speculative content without clear bias.
Output Format
The skill outputs:
- Sentiment Score: Numerical value between -1 and 1.
- Explanation: Breakdown by feed/source, key positive/negative drivers, and overall market implications.
Resources
scripts/
sentiment_analyzer.py: Python script to fetch RSS feeds, parse articles, and compute sentiment score. Run withpython sentiment_analyzer.pyto get automated results.
references/
rss_feeds.md: List of popular crypto RSS feeds with URLs and descriptions.sentiment_examples.md: Examples of sentiment classification for common news types.
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.
- 11d ago First seen · 43 lines · 48 tokens per session scan A 8054d8e5c110
market-sentiment is a skill published in the GitHub repository kukapay/crypto-skills (36 stars, last pushed 4mo ago), licensed MIT. It adds 48 tokens to every session and 437 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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