| name | web3-investor |
| version | 3.8.0 |
| description | AI-native DeFi investment intelligence. Discover, analyze, and compare yield opportunities across 2,500+ protocols with intent-aware search, LLM-powered deep analysis, 7-dimension risk scoring, DeFi security scanning, smart money sentiment, and multi-round conversational refinement. All intelligence runs server-side — zero API keys on the client. |
| author | Antalpha AI Team |
| homepage | https://www.antalpha.com/ |
| metadata | {"openclaw":{"requires":{"bins":"[Truncated]"},"mcp":{"antalpha-skills":"[Truncated]"},"security_notes":["All queries are sent to the Antalpha MCP server (mcp-skills.ai.antalpha.com)","Zero API keys required on the client side","All trading is zero-custody — private keys never leave the user's wallet","User investment intent is processed server-side for analysis"]}} |
Web3 Investor
Your AI-powered DeFi research analyst. Not a dashboard — a thinker.
Web3 Investor turns vague investment intent into structured, risk-aware recommendations. It doesn't just fetch APY data — it understands what you're looking for, scores every opportunity across 7 risk dimensions, cross-references smart money flows, scans for contract vulnerabilities, and explains its reasoning in plain English.
The Problem It Solves
DeFi yield farming today looks like this:
- Open DeFiLlama → see 2,500+ pools → overwhelming
- Check audits, TVL, APY trend, IL risk — each on a different site
- Cross-reference with Twitter sentiment and whale wallets
- Try to figure out if the yield is sustainable or just emission bait
- Give up and put money in USDC savings at 4%
Web3 Investor collapses this into one natural-language request:
"I want stablecoin yield, conservative risk, on Ethereum"
→ 5 personalized recommendations with full risk analysis in 3 seconds
🧠 How It Works
The Intelligence Pipeline
User Intent (natural language)
│
├─ 1. Intent Classification (keyword + LLM fusion)
│ Extract: asset type, risk level, chain, time horizon,
│ position size, liquidity needs, implicit constraints
│
├─ 2. Intent Gate (NEEDS_CLARIFICATION or PASS)
│ If ambiguous → ask user a focused question
│ If clear → proceed with accumulated context (multi-round session)
│
├─ 3. Discovery Engine (DeFiLlama + Dune Analytics + CoinGecko)
│ Fetch 200+ candidates → filter by chain, TVL, risk threshold
│ → deduplicate → rank by risk-adjusted score
│
├─ 4. Risk Scoring (7 dimensions, 0-100)
│ TVL, audit status, chain maturity, yield sustainability,
│ deposit token safety, reward token safety, protocol trust
│ → composite risk level: LOW / MEDIUM / HIGH / VERY_HIGH
│
├─ 5. DeFi Security Scan
│ AI-powered contract scanner → scam detection → critical issue flag
│
├─ 6. Smart Money Sentiment (Dune Analytics)
│ Track whale/fund flows → inflow/outflow signal
│
├─ 7. Recommendation Explanation
│ "Why this product?" — benchmarked vs bank deposits,
│ risk classification (controllable vs uncontrollable),
│ honest alternatives if a better option exists
│
└─ Output: Ranked recommendations with full context
🛠 Three Tools. Deep Intelligence.
investor_discover — Find Opportunities
The entry point. Converts natural language into structured intent, discovers opportunities, and returns ranked recommendations.
What makes it different from a simple DeFiLlama query:
| Feature | DeFiLlama | investor_discover |
|---|
| Input | Chain + sort | Natural language ("stablecoin, conservative") |
| Intent understanding | None | Keyword + LLM fusion with 95%+ accuracy |
| Risk filtering | Manual | Automatic 7-dimension scoring + threshold |
| Multi-round | N/A | Session-based intent accumulation |
| Clarification | N/A | Asks focused questions when intent is ambiguous |
| Smart money | N/A | Integrated whale flow signals |
| DeFi security | N/A | AI contract scanner per pool |
| Explanation | N/A | "Why this?" reasoning per recommendation |
Multi-round Session Example:
Round 1:
User: "Find me good yields"
Agent: "What's your risk tolerance? [Conservative] [Moderate] [Aggressive]"
→ Session stores partial intent
Round 2:
User: "Conservative, stablecoins only"
Agent: (accumulates Round 1 + Round 2 intent)
→ Returns conservative stablecoin recommendations on Ethereum
Request:
{
"agent_id": "uuid",
"natural_language": "stablecoin yield, conservative risk, Ethereum",
"structured_preferences": {
"chain": "ethereum",
"min_apy": 5,
"asset_type": "stablecoin"
},
"limit": 5
}
Response highlights:
{
"gate_status": "PASS",
"recommendations": [{
"name": "Aave V3 USDC",
"yield": { "apy": 5.2, "apy_base": 2.8, "apy_reward": 2.4 },
"scale": { "tvl_usd": 1500000000 },
"risk": {
"risk_level": "LOW",
"risk_score": 82,
"risk_factors": { "tvl_score": 95, "audit_score": 90, ...
... ...
investor_analyze — Deep Analysis
LLM-powered 5-step reasoning chain for a single product. Goes beyond numbers to provide understanding.
Analysis depths:
| Depth | What You Get | Use Case |
|---|
basic | Key metrics + risk score + 1-paragraph summary | Quick check |
detailed | Full risk breakdown + yield source analysis + sustainability assessment + smart money + historical APY | Recommended for investment decisions |
full | Everything in detailed + peer comparison + protocol profile + governance analysis + LLM narrative | Due diligence |
The LLM analysis covers:
Step 1: Yield Source Analysis
→ Is this APY from trading fees (sustainable) or token emissions (unsustainable)?
→ APY breakdown: base yield vs reward yield ratio
Step 2: Sustainability Assessment
→ Historical APY trend (7d / 30d / 90d)
→ APY volatility (standard deviation)
→ Revenue coverage (can the protocol afford these rewards?)
Step 3: Risk Narrative
→ Comprehensive risk story, not just a score
→ Smart money sentiment overlay
→ DeFi security scan results (scam flags, critical issues)
Step 4: Competitive Position
→ How does this compare to peers in the same category?
→ Protocol profile: governance, longevity, audit history
Step 5: Investor Considerations
→ Actionable guidance for the specific investor profile
→ Key risks and key positives
→ "If you're conservative, consider X. If aggressive, consider Y."
investor_compare — Side-by-Side Comparison
Compare 2–5 products with LLM-powered interpretation across customizable dimensions.
Default comparison dimensions: APY, risk score, TVL
Extended dimensions: fees, lock period, audit count, IL risk, governance type, smart money sentiment, incentive sustainability
LLM comparison output:
{
"llm_comparison": {
"narrative": "Aave offers superior security with $1.5B TVL and 6 audits, while Compound provides higher raw yield at 6.1% but with smaller TVL...",
"risk_comparison": "Aave's risk score (82) significantly outperforms Compound (68), primarily due to larger TVL and more comprehensive audit coverage",
"recommendation_with_reasoning": {
"for_conservative": "Choose Aave — battle-tested protocol, deep liquidity, multiple top-tier audits",
"for_aggressive": "Consider Compound — 0.9% higher APY, acceptable risk for short-term positions",
"key_tradeoff": "0.9% yield premium vs significantly lower risk score (82 vs 68)"
}
}
}
🏗️ Server-Side Architecture
┌─────────────────────┐
│ AI Agent │ MCP JSON-RPC ┌──────────────────────────────────────┐
│ (OpenClaw) │ ─────────────────────────────► │ Antalpha MCP Server │
│ │ │ mcp-skills.ai.antalpha.com │
│ "Find me yield" │ │ │
│ → 3 tool calls │ ◄───────────────────────────── │ Intent Classifier (keyword + LLM) │
│ → zero API keys │ structured results │ Risk Scoring (7 dimensions) │
│ → zero custody │ │ DeFi Security Scanner │
└─────────────────────┘ │ LLM Analysis (5-step chain) │
│ Smart Money (Dune Analytics) │
│ Market Context (bull/bear/sideways) │
│ Protocol Profiles (DefiLlama) │
│ Data Validation (cross-source) │
│ Explanation Engine (benchmarked) │
│ │
│ Data Sources: │
│ ├─ DefiLlama (2,500+ pools) │
│ ├─ Dune Analytics (smart money) │
│ ├─ CoinGecko (market data) │
│ ├─ DeFi Security (contract scan) │
│ └─ Internal LLM (analysis) │
└──────────────────────────────────────┘
🔐 Risk Intelligence
7-Dimension Risk Scoring
Each opportunity is scored 0–100 across seven independent dimensions:
| Dimension | What It Measures | Weight |
|---|
| TVL Score | Total Value Locked — protocol maturity indicator | High |
| Audit Score | Number and quality of security audits | High |
| Chain Score | Chain maturity (L1 vs L2 vs new chain) | Medium |
| Sustainability | Yield source analysis (fees vs emissions) | High |
| Deposit Token | Safety tier of the deposit asset (USDC > DAI > random) | Medium |
| Reward Token | Liquidity and safety of reward tokens | Low |
| Protocol Trust | Governance, longevity, track record | Medium |
Risk Levels:
- LOW (score ≥ 70): Battle-tested, well-audited, high TVL
- MEDIUM (score 50–69): Established but with some risk factors
- HIGH (score 30–49): Newer or with significant warnings
- VERY_HIGH (score < 30): Experimental or flagged
DeFi Security Scanning
Every pool is scanned by an AI-powered contract security engine:
{
"defi_security": {
"aiScore": 87,
"safetyPercentage": 92,
"isScam": false,
"criticalIssues": 0
}
}
Smart Money Sentiment
Real-time whale and fund wallet activity aggregated from Dune Analytics:
{
"smart_money": {
"flow": "inflow",
"sentiment_score": 0.72,
"confidence": "high",
"buy_volume_usd": 15000000,
"sell_volume_usd": 3200000,
"net_flow_usd": 11800000,
"signal_count": 23
}
}
Market Context Awareness
The engine is aware of the current market cycle (bull/bear/sideways), total DeFi TVL, BTC dominance, and US Treasury rates — providing context-aware recommendations that adjust for macro conditions.
🚀 Quick Start
Prerequisites
- Python 3.8+
- No API keys needed — all intelligence runs server-side
Registration (one-time)
./scripts/run.sh register
Usage
./scripts/run.sh discover --chain ethereum --min-apy 5 --limit 5
./scripts/run.sh discover --natural-language "stablecoin yield, conservative"
./scripts/run.sh analyze --product-id <id> --depth detailed
./scripts/run.sh analyze --product-id <id> --depth full
./scripts/run.sh compare --ids <id1> <id2> <id3>
⚠️ Critical Rules
Rule 1: Discovery First
Never give generic investment advice without real-time data.
❌ "I recommend Aave for stablecoin yield"
✅ investor_discover → analyze results → data-backed recommendation
Rule 2: Explain Your Reasoning
Every recommendation should include the explanation object — summary, reasons for/against, benchmark comparison, risk classification.
Rule 3: Risk Is Non-Negotiable
- APY data may be delayed — always show
data_quality.score and last_updated
- Never recommend VERY_HIGH risk products without explicit user acknowledgment
- Investment decisions are the user's own responsibility — always DYOR
Rule 4: Honest About Limitations
- If no good options exist for the user's criteria, say so with
generateHonestNoResultExplanation
- Show alternatives even if they don't perfectly match — "This is close but has X tradeoff"
🔒 Security
| Layer | Protection |
|---|
| Private Keys | Zero contact — never held, transmitted, or stored |
| Data Sources | Triple-validated (DefiLlama + Dune + CoinGecko) with cross-source checks |
| Contract Safety | AI-powered DeFi security scanner on every pool |
| Risk Scoring | 7 independent dimensions, no single point of failure |
| Scam Detection | Automated flagging via DeFi Security engine (isScam check) |
| Data Quality | Freshness tracking, confidence scores, validation issue logging |
📝 Changelog
v3.8.0 (2026-04-14)
- Full SKILL.md rewrite — English, feature-focused, comprehensive
- Documented complete server-side intelligence pipeline
- Added 7-dimension risk scoring documentation
- Added DeFi Security scanning documentation
- Added Smart Money sentiment documentation
- Added Market Context awareness documentation
- Added multi-round session intent accumulation flow
- Added honest "no result" explanation behavior
- Added incentive sustainability scoring documentation
- Added data quality cross-validation documentation
v3.7.2 (2026-04-13)
- Client cleanup: removed deprecated tools (feedback/confirm-intent/get-intent)
🤝 Contributing
Test donations welcome:
- Network: Base Chain
- Address:
0x1F3A9A450428BbF161C4C33f10bd7AA1b2599a3e
Maintainer: Web3 Investor Skill Team
Registry: https://clawhub.com/skills/web3-investor
License: MIT
