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 nirholas/three.ws --skill market-sentiment-analysisgit clone --depth 1 https://github.com/nirholas/three.wsWrote 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/nirholas/three.ws/market-sentiment-analysis)<a href="https://agentmods.dev/skills/nirholas/three.ws/market-sentiment-analysis"><img src="https://agentmods.dev/badge/skills/nirholas/three.ws/market-sentiment-analysis.svg" alt="Measured on agentmods" 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 | $0.00037 | $0.00894 |
| Opus 5 | $0.00018 | $0.00447 |
| Sonnet 5 | $0.00007 | $0.00179 |
| Haiku 4.5 | $0.00004 | $0.00089 |
Grade A, and why
market-sentiment-analysis 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 3d 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 — 82 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Market Sentiment Analysis
When to use this skill
Use when the user asks about:
- Current overall crypto market sentiment
- Whether the market is greedy or fearful
- Social media buzz around a specific token
- Funding rate analysis for leveraged positions
- Contrarian indicators and crowd positioning
- Whether it's a good time to buy or sell based on sentiment
Sentiment Framework
1. Fear and Greed Index Interpretation
Assess the current state of market fear/greed:
- 0-24 (Extreme Fear): Market is very pessimistic — historically a buying zone for long-term holders
- 25-49 (Fear): Uncertainty prevails — caution warranted but opportunities may exist
- 50 (Neutral): Market is balanced
- 51-74 (Greed): Optimism building — consider taking some profits on winners
- 75-100 (Extreme Greed): Euphoria — historically precedes corrections
Note the trend direction: is fear/greed increasing or decreasing over 7d/30d?
2. On-Chain Sentiment Metrics
Analyze blockchain data for positioning signals:
- Exchange inflows/outflows: Net outflows = accumulation (bullish); net inflows = distribution (bearish)
- Whale activity: Large wallet movements to/from exchanges
- MVRV ratio: Market Value vs Realized Value — above 3.5 historically overheated, below 1.0 undervalued
- SOPR (Spent Output Profit Ratio): Above 1 = holders selling at profit; below 1 = selling at loss
- Active addresses trend: Growing = healthy network activity
- Stablecoin supply ratio: High stablecoin supply relative to BTC market cap = dry powder ready to deploy
3. Derivatives Sentiment
Examine leveraged market positioning:
- Funding rates: Positive = longs paying shorts (bullish crowd); negative = shorts paying longs (bearish crowd)
- Open interest: Rising OI + rising price = strong trend; rising OI + flat price = coiled spring
- Long/short ratio: Extreme readings (>2.0 or <0.5) suggest crowded positioning
- Liquidation levels: Where are large clusters of liquidations? These act as magnets
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.
- 3d ago First seen · 82 lines · 37 tokens per session scan A 16295fd9af90
market-sentiment-analysis is a skill published in the GitHub repository nirholas/three.ws (111 stars, last pushed yesterday), licensed Apache-2.0. It adds 37 tokens to every session and 894 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 2026-09-03.
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