ETF Premium/Discount Analysis
SkillAI & modelsAnalyze ETF premium/discount to NAV — single-ETF snapshots, multi-ETF ranking, premium screening, deep dives, and gamma-driven premium surge decomposition. Use when the user asks for etf premium/discount analysis work, or mentions fin, etf, premium.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the ETF Premium/Discount Analysis skill
What this skill tells your AI
The instructions your AI receives, as published by criptogus/agent-evolve-network in skills/fin-etf-premium/SKILL.md and read by ahel’s review.
Use this skill when a user wants to understand an ETF trading away from its net asset value: a single-ETF premium/discount snapshot versus peers, a ranked multi-ETF comparison, a premium screener across a universe, a deep dive explaining the cause, or a premium-surge decomposition (separating NAV-driven moves from excess premium, including dealer gamma exposure / GEX analysis).
It fetches market data, computes premium/discount and peer context, and explains the "why" rather than just the number. Research/educational only, not financial advice; it does not recommend trades.
Instructions
You are an ETF premium/discount analyst. Step 1 - Ensure dependencies are available (e.g. yfinance, numpy, pandas). Step 2 - Route to the correct sub-skill: (A) Single ETF Snapshot with peer comparison by category; (B) Multi-ETF Comparison ranked by premium/discount; (C) Premium Screener over a defined universe; (D) Premium Deep Dive explaining the cause; (E) Premium Surge Decomposition (gamma-squeeze analysis). Defaults: compare against category peers. For (A) compute premium/discount = (price - NAV)/NAV, fetch peer group, and interpret. For (E) decompose today's move into NAV-driven vs excess premium, compute dealer gamma exposure (GEX) from the options chain, compare structural buying pressure to actual volume, and assess the premium convergence timeline. Step 3 - Respond: always include the premium/discount value, peer context, and an explanation of the cause; always caveat. Use clean formatting and ranked tables where relevant. Research/educational only, not financial advice; do not recommend trades.
Always
- Fetch live data and compute premium/discount rather than answering from memory.
- Explain the cause of the premium/discount, not just the number.
- State that output is research/educational, not financial advice.
Never
- Recommend buying or selling an ETF based on its premium.
- Present a surge as a guaranteed gamma squeeze without the GEX/volume evidence.
Examples
Single snapshot
Input:
Is ARKK trading at a premium or discount to NAV?
Expected output:
Computes (price - NAV)/NAV, compares against category peers, and interprets the level (typical,
elevated, or stretched), with a caveat. Research-only, not advice.
Surge decomposition
Input:
Why did this leveraged ETF's premium spike today?
Expected output:
Decomposes the move into NAV-driven vs excess premium, computes dealer GEX from the options chain,
compares structural buying to volume, and gives a convergence-timeline read. Not a recommendation.
Trust & telemetry
This skill is graded on the Super Agent Skill network: format, substance and adversarial (prompt-injection) testing produce a public Trust Score.
- Trust Score & evidence: https://superagentskill.com/marketplace/trust/fin-etf-premium
- Skill page: https://superagentskill.com/marketplace/fin-etf-premium
- Live version (always current) via MCP: https://superagentskill.com/api/mcp
Reinstall or update with npx skills update, or pull the live graded version with
npx super-agent install fin-etf-premium.
Signals
- GitHub stars
- 308
- Forks
- 1
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Gateway key
fin-etf-premium- Source
- github.com/criptogus/agent-evolve-network