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 crypto-token-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/crypto-token-analysis)<a href="https://agentmods.dev/skills/kevinnft/ai-agent-skills/crypto-token-analysis"><img src="https://agentmods.dev/badge/skills/kevinnft/ai-agent-skills/crypto-token-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/crypto-token-analysis"><img src="https://agentmods.dev/badge/skills/kevinnft/ai-agent-skills/crypto-token-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 | $0.00049 | $0.13464 |
| Opus 5 | $0.00024 | $0.06732 |
| Sonnet 5 | $0.00010 | $0.02693 |
| Haiku 4.5 | $0.00005 | $0.01346 |
Grade A, and why
crypto-token-analysis scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -s "https://api.coingecko.com/api/v3/coins/{token_id}" How it starts
The opening of the file, as written. The whole thing — 1,739 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Crypto Token Deep Analysis Framework
Systematic approach for analyzing crypto tokens with focus on profit opportunities and risk avoidance. Used when user requests token analysis, investment research, or airdrop evaluation.
When to Use
- User asks to analyze a specific token (e.g., "$HPL analysis")
- Requests for "deep dive", "fundamental analysis", or "is X token good?"
- Airdrop opportunity evaluation (token OR project-level)
- Risk assessment before investment
- Comparing tokens in an ecosystem
- Analyzing crypto projects/protocols for airdrop potential (AI agents, DeFi protocols, infrastructure)
- Evaluating X/Twitter announcements for alpha/farming opportunities
- DePIN project analysis (Decentralized Physical Infrastructure Networks — Helium, IoTeX, Peaq, Render, etc.)
- Stock market dividend analysis (IDX/Indonesian stocks) — see
references/idx-dividend-analysis.mdfor workflow when APIs fail - NFT collection analysis (OpenSea, Blur, etc.) — floor price, volume, holder distribution, project status
Analysis Types
Type A: Token Analysis (existing token with market data)
Use when: Token already listed on CoinGecko/CMC, has trading volume, circulating supply.
Type B: Project/Protocol Analysis (pre-token or early-stage)
Use when: Analyzing for airdrop potential, product is live but no token yet, or token just launched with minimal data.
Key difference: Type B focuses on product reality, user onboarding, VC backing, and airdrop signals rather than liquidity metrics.
Type C: DePIN Project Analysis (hardware-based infrastructure)
Use when: Analyzing Decentralized Physical Infrastructure Networks — projects requiring hardware (IoT devices, sensors, nodes, GPUs, wireless hotspots).
Key difference: Type C evaluates hardware requirements, operator economics, network effects, and adoption barriers unique to physical infrastructure.
Type D: NFT Collection Analysis (floor price, volume, holders)
Use when: Analyzing NFT collections on OpenSea, Blur, or other marketplaces — evaluating floor price trends, trading volume, holder distribution, and project status.
What ships with it
4 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 · 1,739 lines · 49 tokens per session scan A dc6e5b710514
crypto-token-analysis is a skill published in the GitHub repository kevinnft/ai-agent-skills (14 stars, last pushed 1mo ago), licensed MIT. It adds 49 tokens to every session and 13,464 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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