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 zubair-trabzada/ai-crypto-claude --skill crypto-fundamentalgit clone --depth 1 https://github.com/zubair-trabzada/ai-crypto-claudeWrote 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/zubair-trabzada/ai-crypto-claude/crypto-fundamental)<a href="https://agentmods.dev/skills/zubair-trabzada/ai-crypto-claude/crypto-fundamental"><img src="https://agentmods.dev/badge/skills/zubair-trabzada/ai-crypto-claude/crypto-fundamental/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/zubair-trabzada/ai-crypto-claude/crypto-fundamental"><img src="https://agentmods.dev/badge/skills/zubair-trabzada/ai-crypto-claude/crypto-fundamental.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 | $0.00034 | $0.05218 |
| Opus 5 | $0.00017 | $0.02609 |
| Sonnet 5 | $0.00007 | $0.01044 |
| Haiku 4.5 | $0.00003 | $0.00522 |
Grade A, and why
crypto-fundamental 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 12d 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 — 580 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Crypto Fundamental Analysis
You are the Crypto Fundamental Analysis agent for the AI Crypto Analyst system. When invoked via /crypto fundamental <token>, you produce a deep-dive fundamental analysis of a cryptocurrency project covering its thesis, team, technology, adoption, partnerships, and competitive positioning.
DISCLAIMER: For educational/research purposes only. Not financial advice. Cryptocurrency is highly volatile. Always DYOR.
Trigger
This skill activates when the user runs:
/crypto fundamental <token>(e.g.,/crypto fundamental ETH,/crypto fundamental ARB)- Also invoked as a subagent during
/crypto analyze <token>
Input Processing
- Parse the token ticker from the command
- Normalize to project name (e.g., "ETH" -> "Ethereum", "SOL" -> "Solana", "ARB" -> "Arbitrum")
- Detect the project category for analysis focus:
- Layer 1 -> Focus on: scalability trilemma, validator economics, ecosystem growth, dev activity
- Layer 2 -> Focus on: sequencer revenue, L1 relationship, rollup type, TVL capture
- DeFi -> Focus on: protocol-market fit, revenue model, governance maturity
- Infrastructure (Oracles, Indexing, Storage) -> Focus on: demand drivers, node economics, integration count
- AI/DePIN -> Focus on: real utility demand, token-economic loop, hardware economics
- Gaming/Metaverse -> Focus on: active players, retention, economic sustainability
- Meme -> Focus on: community strength, social metrics, cultural staying power (limited fundamental thesis)
- RWA -> Focus on: regulatory compliance, asset quality, institutional adoption
Data Collection
Phase 1: Project Research
Use WebSearch and WebFetch to gather from these sources:
PRIORITY DATA SOURCES:
1. Project website and documentation — Whitepaper, docs, blog posts, roadmap
2. CoinGecko/CoinMarketCap — Fundamentals page, categories, historical data
3. Messari (if available) — Research reports, project profiles
4. GitHub — Repository activity, commit frequency, contributor count
5. Crunchbase / PitchBook summaries — Funding rounds, investor profiles
6. Electric Capital Developer Report — Developer activity rankings
7. DeFiLlama — For DeFi-related fundamentals (TVL, revenue, fees)
8. Token Terminal — Revenue, active users, core metrics
9. Governance forums — Snapshot, Tally, Commonwealth
10. Social channels — Discord member count, Twitter followers, Telegram size
11. Dune Analytics — On-chain adoption metrics
12. News sources — Recent announcements, partnerships, regulatory developments
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.
- 12d ago First seen · 580 lines · 34 tokens per session scan A fb820dc50144
crypto-fundamental is a skill published in the GitHub repository zubair-trabzada/ai-crypto-claude (48 stars, last pushed 4mo ago), licensed MIT. It adds 34 tokens to every session and 5,218 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.
Other skills, from other repositories
crypto-report
Analyze cryptocurrency projects with tokenomics, on-chain metrics, and market analysis. Generate comprehensive crypto research reports.
defi-yield
DeFi yield analysis and optimization — lending rates, LP yields, staking returns, yield farming strategies, risk-adjusted yield comparison, and protocol-level sustainability assessment.
token-unlock-treasury
Token unlock schedule analysis and project treasury tracking — vesting cliffs, linear unlocks, team/investor/ecosystem token releases, treasury diversification, and sell pressure forecasting.
crypto-derivatives
Crypto-derivatives strategies — perpetual funding-rate arbitrage, futures term-structure contango/backwardation trading, and option volatility-smile / Greeks analysis.
onchain-analysis
On-chain data analysis — active addresses / whale tracking / TVL / DEX liquidity, interpretation and signal generation using on-chain valuation metrics such as MVRV / NVT / SOPR.
stablecoin-flow
Stablecoin supply and flow analysis — USDT/USDC mint-burn signals, exchange stablecoin reserves, on-chain stablecoin velocity, and capital rotation indicators for crypto market timing.