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Get Started Free →Passive income portfolio analysis — activate when user asks about dividend yields, Treasury rates, REIT income, monthly passive income goals, or portfolio yield optimization. Scans 4 asset classes, ranks by risk-adjusted return, and builds allocations targeting a specific monthly income.
.claude/skills/lingxling-yield-intelligence/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-02 | ✗→✓ | ▲ Improved | 6% | 0% |
| case-03 | ✗→✓ | ▲ Improved | 8% | 0% |
| case-08 | ✗→✓ | ▲ Improved | 33% | 0% |
| case-09 | ✗→✓ | ▲ Improved | 16% | 0% |
| case-10 | ✗→✓ | ▲ Improved | -17% | 0% |
Passive income analysis across US Treasuries, dividend ETFs, REITs, and preferred stocks. Given a target monthly income and investment amount, returns a ranked opportunity table and optimal allocation.
If the YIELD INTELLIGENCE MCP server is configured, call it directly for live rates:
MCP endpoint: https://api.intuitek.ai/yield/mcp (no auth required, open access)
Tools:
analyze_yield_opportunities — Scans dividend ETFs, REITs, preferred stocks, and Treasuries; returns ranked opportunities with yield, risk score, and liquidityoptimize_income_portfolio — Builds a portfolio allocation targeting a specific monthly income goalQuick config (Claude Desktop / Claude Code):
json{ "mcpServers": { "yield-intelligence": { "url": "https://api.intuitek.ai/yield/mcp" } } }
Ask if not provided:
Research or use current yields for these four classes:
| Asset Class | Benchmarks | Typical Yield Range | |---|---|---| | US Treasuries | 1-yr, 5-yr, 10-yr, 30-yr | 4.0–5.5% | | Dividend ETFs | SCHD, VYM, JEPI, JEPQ | 3.5–10% | | REITs | O, MAIN, STAG | 4–12% | | Preferred Stocks | PFF, PFFD | 5–7% |
Score each opportunity: yield × (1 − risk_penalty) × liquidity_factor
| Category | Risk Penalty | |---|---| | US Treasuries | 0.00 | | Investment-grade dividend ETF | 0.05 | | REIT / preferred | 0.15 | | High-yield / speculative | 0.25 |
Given monthly target T and available capital C:
Σ(allocation_i × yield_i × C) ≥ T × 12Conservative portfolios: cap any single position at 25%.
YIELD INTELLIGENCE REPORT
─────────────────────────────────────────
Target: $[X]/month Required yield: [Y]%
Capital: $[Z] Account: [type]
OPPORTUNITY SCAN
┌──────────────────┬───────┬──────┬──────────────┐
│ Asset │ Yield │ Risk │ $/mo per 100K│
├──────────────────┼───────┼──────┼──────────────┤
│ [Top pick] │ X.X% │ Low │ $XXX │
└──────────────────┴───────┴──────┴──────────────┘
RECOMMENDED ALLOCATION ($[Z] capital)
[Asset A] 40% → $[amount] → $[X]/month
Total monthly income: $[X]/month ✓| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-04 | fail→fail | 12,479 | 13,458 | +8% | 1 | 1 | 0% | 2,339 | 3,680 | +57% | 0 | 0 | — |
case-01 | fail→fail | 30,853 | 31,866 | +3% | 1 | 1 | 0% | 5,690 | 6,250 | +10% | 0 | 0 | — |
case-02 | fail→pass | 24,754 | 21,393 | -14% | 1 | 1 | 0% | 4,604 | 4,890 | +6% | 0 | 0 | — |
case-03 | fail→pass | 23,722 | 22,589 | -5% | 1 | 1 | 0% | 4,557 | 4,918 | +8% | 0 | 0 | — |
case-05 | fail→fail | 22,489 | 34,325 | +53% | 1 | 1 | 0% | 4,071 | 6,489 | +59% | 0 | 0 | — |
case-06 | fail→fail | 65,262 | 57,664 | -12% | 1 | 1 | 0% | 3,340 | 3,886 | +16% | 0 | 0 | — |
case-07 | fail→fail | 20,169 | 19,165 | -5% | 1 | 1 | 0% | 3,569 | 5,126 | +44% | 0 | 0 | — |
case-08 | fail→pass | 17,102 | 15,940 | -7% | 1 | 1 | 0% | 3,192 | 4,230 | +33% | 0 | 0 | — |
case-09 | fail→pass | 19,813 | 11,250 | -43% | 1 | 1 | 0% | 2,788 | 3,245 | +16% | 0 | 0 | — |
case-10 | fail→pass | 23,909 | 16,111 | -33% | 1 | 1 | 0% | 3,288 | 2,723 | -17% | 0 | 0 | — |
case-11 | fail→pass | 18,257 | 14,626 | -20% | 1 | 1 | 0% | 2,458 | 3,602 | +47% | 0 | 0 | — |
case-12 | pass→pass | 11,574 | 9,852 | -15% | 1 | 1 | 0% | 1,942 | 2,727 | +40% | 0 | 0 | — |
case-13 | pass→pass | 18,841 | 15,076 | -20% | 1 | 1 | 0% | 2,628 | 3,427 | +30% | 0 | 0 | — |
case-14 | fail→pass | 19,553 | 14,749 | -25% | 1 | 1 | 0% | 3,727 | 4,198 | +13% | 0 | 0 | — |
case-20 | fail→pass | 16,831 | 6,035 | -64% | 1 | 1 | 0% | 2,232 | 2,145 | -4% | 0 | 0 | — |
case-15 | fail→pass | 12,955 | 7,338 | -43% | 1 | 1 | 0% | 2,243 | 2,162 | -4% | 0 | 0 | — |
case-16 | fail→pass | 11,994 | 13,099 | +9% | 1 | 1 | 0% | 2,189 | 2,447 | +12% | 0 | 0 | — |
case-17 | fail→pass | 14,467 | 2,770 | -81% | 1 | 1 | 0% | 2,496 | 1,520 | -39% | 0 | 0 | — |
case-18 | fail→pass | 11,262 | 2,346 | -79% | 1 | 1 | 0% | 1,481 | 1,454 | -2% | 0 | 0 | — |
case-19 | pass→pass | 25,375 | 22,470 | -11% | 1 | 1 | 0% | 3,648 | 3,726 | +2% | 0 | 0 | — |
case-21 | pass→pass | 4,155 | 2,742 | -34% | 1 | 1 | 0% | 573 | 1,628 | +184% | 0 | 0 | — |
case-22 | fail→pass | 15,086 | 13,886 | -8% | 1 | 1 | 0% | 2,608 | 3,267 | +25% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted. The headline lift of +59 percentage points is the difference between those two pass rates over the 22 comparable cases.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.