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 kevinnft/ai-agent-skills --skill nft-analysisgit clone --depth 1 https://github.com/kevinnft/ai-agent-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/kevinnft/ai-agent-skills/nft-analysis)<a href="https://agentmods.dev/skills/kevinnft/ai-agent-skills/nft-analysis"><img src="https://agentmods.dev/badge/skills/kevinnft/ai-agent-skills/nft-analysis/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/kevinnft/ai-agent-skills/nft-analysis"><img src="https://agentmods.dev/badge/skills/kevinnft/ai-agent-skills/nft-analysis.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 | $0.00029 | $0.01920 |
| Opus 5 | $0.00015 | $0.00960 |
| Sonnet 5 | $0.00006 | $0.00384 |
| Haiku 4.5 | $0.00003 | $0.00192 |
Grade A, and why
nft-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 7d 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 — 240 lines — stays where its author put it; the contents beside it link to each section on GitHub.
NFT Project Analysis
Comprehensive framework for evaluating NFT projects and providing investment recommendations. Focuses on fundamentals, red flags, and risk assessment rather than speculation.
When to Use
- User asks to analyze an NFT collection
- User asks "should I buy this NFT?"
- User wants to evaluate NFT project health
- User asks about NFT price trends or investment potential
Analysis Framework
1. Data Collection
Primary sources:
- OpenSea (floor price, volume, holders, sales)
- Blur, LooksRare, Rarible (alternative marketplaces)
- Twitter (community size, engagement, updates)
- Discord/Telegram (community activity, team presence)
- Etherscan/blockchain explorer (contract, holder distribution)
- Project website (roadmap, team, utility)
Key metrics:
- Floor price (current + historical)
- Trading volume (24h, 7d, 30d)
- Total volume (all-time)
- Unique holders
- Total supply
- Recent sales activity
- Listed count (how many NFTs are listed for sale)
- Marketplace presence (which platforms list the collection)
- Social media followers
- Community engagement
CRITICAL: Check marketplace liquidity FIRST
- Verify NFT is actually listed on marketplaces (OpenSea, Blur, etc.)
- Check on-chain holders count (0 holders = NFTs stuck in contract)
- Verify contract is verified on block explorer
- If zero marketplace presence + zero holders = DEAD MARKET (instant avoid)
2. Chart Pattern Recognition
Healthy patterns:
- Gradual uptrend with consolidation zones
- Higher lows, higher highs
- Multiple horizontal support levels
- Steady volume growth
- Low volatility
Death patterns:
- Pump & dump (sharp spike → crash)
- Death spiral (continuous decline, no support)
- Extreme volatility (0 → peak → 0 → peak → 0)
- Declining volume
- No consolidation zones
Example death chart:
Launch: 0.1 ETH (accumulation)
Month 2: 0.5 ETH (PUMP — 5x in days)
Month 3: 0.0 ETH (CRASH — rug pull)
Month 4: 0.4 ETH (dead cat bounce)
Month 5: 0.003 ETH (death spiral)
Pattern: TEXTBOOK RUG PULL
What ships with it
3 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.
- 7d ago First seen · 240 lines · 29 tokens per session scan A 7a9602abc38c
nft-analysis is a skill published in the GitHub repository kevinnft/ai-agent-skills (14 stars, last pushed 1mo ago), licensed MIT. It adds 29 tokens to every session and 1,920 once invoked, about $0.0001 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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