[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6MTR28SSJ02J903MPHNE5KE":3,"skill-same:01M6MTR28SSJ02J903MPHNE5KE":66},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":17,"publishedAt":33,"linkOnly":34,"markdownContent":35,"markdownBody":36,"frontMatter":37,"refName":38,"commitSha":39,"skillDirectory":40,"forksCount":41,"repositoryHtmlUrl":42,"repositoryHomepageUrl":6,"ownerHtmlUrl":43,"ownerBlogUrl":6,"ownerDescription":44,"updatedAt":45,"install":46,"resources":59},"01M6MTR28SSJ02J903MPHNE5KE","multi-factor-strategy",null,"Guide users to create multi-factor stock selection strategies and generate independent YAML configuration files","Kernel8901\u002Fai-agent-skills-classification","Kernel8901","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F72647553?v=4","01M6KV388J2H5PNC8D8P940BJD","01M6KV38CS5B3AY63RKZB410YM",7,"MIT","https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002FGit和GitHub\u002Fmulti-factor-strategy\u002Fskill.md",[],[18,19,20,21,22,23,24,25,26,27,28,29,30,31,32],"strategy","factor","multi","multi factor strategy","create","generate","yaml","multi-factor","guide","files","configuration","selection","strategies","stock","users","2026-11-10 10:12:30",false,"---\nname: multi-factor-strategy\ndescription: Guide users to create multi-factor stock selection strategies and generate independent YAML configuration files\n---\n{\"homepage\":\"https:\u002F\u002Fgitcode.com\u002Fdatavoid\u002Fquantcli\",\"user-invocable\":true}\n\n# Multi-Factor Strategy Assistant\n\nGuide you to create multi-factor stock selection strategies and generate independent YAML configuration files.\n\n## Install quantcli\n\n```bash\n# Install from PyPI (recommended)\npip install quantcli\n\n# Or install from source\ngit clone https:\u002F\u002Fgitcode.com\u002Fdatavoid\u002Fquantcli.git\ncd quantcli\npip install -e .\n```\n\nVerify installation:\n```bash\nquantcli --help\n```\n\n## Quick Start\n\nA complete multi-factor stock selection strategy YAML example:\n\n```yaml\nname: Value-Growth Hybrid Strategy\nversion: 1.0.0\ndescription: ROE + Momentum factor stock selection\n\nscreening:\n  fundamental_conditions:    # Stage 1: Financial condition screening\n    - \"roe > 0.10\"           # ROE > 10%\n    - \"pe_ttm \u003C 30\"          # P\u002FE \u003C 30\n    - \"pe_ttm > 0\"           # Exclude losses\n  daily_conditions:          # Stage 2: Price condition screening\n    - \"close > ma10\"         # Above 10-day MA\n  limit: 100                 # Keep at most 100 stocks\n\n# Factor configuration (supports two methods, factors at top level)\nfactors:\n  # Method 1: Inline factor definition\n  - name: ma10_deviation\n    expr: \"(close - ma(close, 10)) \u002F ma(close, 10)\"\n    direction: negative\n    description: \"10-day MA deviation\"\n\n  # Method 2: External reference (reference factor files in factors\u002F directory, include .yaml suffix)\n  - factors\u002Falpha_001.yaml\n  - factors\u002Falpha_008.yaml\n\nranking:\n  weights:                   # Weight fusion\n    ma10_deviation: 0.20     # Inline factor\n    factors\u002Falpha_001.yaml: 0.40  # External reference factor\n    factors\u002Falpha_008.yaml: 0.40\n  normalize: zscore          # Normalization method\n\noutput:\n  limit: 30                  # Output top 30 stocks\n  columns: [symbol, name, score, roe, pe_ttm, close, ma10_deviation]\n```\n\n### Factor Configuration Methods\n\n**Factor configuration supports two methods (can be mixed):**\n\n| Method | Type | Example | Description |\n|--------|------|---------|-------------|\n| **Inline** | `dict` | `{name: xxx, expr: \"...\"}` | Define expression directly in YAML |\n| **External** | `str` | `factors\u002Falpha_001.yaml` | Load factor file from `factors\u002F` directory |\n\n**Example: Mixed usage**\n\n```yaml\nfactors:\n  # Inline: Custom factor\n  - name: custom_momentum\n    expr: \"close \u002F delay(close, 20) - 1\"\n    direction: positive\n\n  # External: Alpha101 factor library (include .yaml suffix)\n  - factors\u002Falpha_001.yaml\n  - factors\u002Falpha_005.yaml\n  - factors\u002Falpha_009.yaml\n\nranking:\n  weights:\n    custom_momentum: 0.3\n    factors\u002Falpha_001.yaml: 0.3\n    factors\u002Falpha_005.yaml: 0.2\n    factors\u002Falpha_009.yaml: 0.2\n```\n\nRun strategy:\n```bash\nquantcli filter run -f your_strategy.yaml\n```\n\n## Invocation\n\n```\n\u002Fmulti-factor-strategy\n```\n\n## Available Expression Functions\n\n### Data Processing Functions\n| Function | Usage | Description |\n|----------|-------|-------------|\n| delay | `delay(x, n)` | Lag n periods |\n| ma | `ma(x, n)` | Simple moving average |\n| ema | `ema(x, n)` | Exponential moving average |\n| rolling_sum | `rolling_sum(x, n)` | Rolling sum |\n| rolling_std | `rolling_std(x, n)` | Rolling standard deviation |\n\n### Technical Indicator Functions\n| Function | Usage | Description |\n|----------|-------|-------------|\n| rsi | `rsi(x, n=14)` | Relative strength index |\n| correlation | `correlation(x, y, n)` | Correlation coefficient |\n| cross_up | `cross_up(a, b)` | Golden cross (a crosses above b) |\n| cross_down | `cross_down(a, b)` | Death cross (a crosses below b) |\n\n### Ranking & Normalization Functions\n| Function | Usage | Description |\n|----------|-------|-------------|\n| rank | `rank(x)` | Cross-sectional ranking (0-1) |\n| zscore | `zscore(x)` | Standardization |\n| sign | `sign(x)` | Sign function |\n| clamp | `clamp(x, min, max)` | Clipping function |\n\n### Conditional Functions\n| Function | Usage | Description |\n|----------|-------|-------------|\n| where | `where(cond, t, f)` | Conditional selection |\n| if | `if(cond, t, f)` | Conditional selection (alias) |\n\n### Base Fields\n| Field | Description |\n|-------|-------------|\n| open, high, low, close | OHLC prices |\n| volume | Trading volume |\n| pe, pb | P\u002FE ratio, P\u002FB ratio |\n| roe | Return on equity |\n| netprofitmargin | Net profit margin |\n\n## Guided Workflow\n\n### Step 1: Strategy Goal定位\n\nI will first understand your strategy needs:\n- **Strategy Type**: Value, Growth, Momentum, Volatility, Hybrid\n- **Selection Count**: Concentrated(10-30), Medium(50-100), Diversified(200+)\n- **Holding Period**: Intraday, Short-term(week), Medium-term(month), Long-term(quarter)\n\n### Step 2: Factor Selection\n\nBased on your strategy goals, recommend suitable factor combinations:\n\n**Common Fundamental Factors**:\n| Factor | Expression | Direction | Description |\n|--------|------------|-----------|-------------|\n| roe | `roe` | positive | Return on equity |\n| pe | `pe` | negative | Lower P\u002FE is better |\n| pb | `pb` | negative | Price-to-book ratio |\n| netprofitmargin | `netprofitmargin` | positive | Net profit margin |\n| revenue_growth | `revenue_yoy` | positive | Revenue growth rate |\n\n**Common Technical Factors**:\n| Factor | Expression | Direction | Description |\n|--------|------------|-----------|-------------|\n| momentum | `(close\u002Fdelay(close,20))-1` | positive | N-day momentum |\n| ma_deviation | `(close-ma(close,10))\u002Fma(close,10)` | negative | MA deviation |\n| ma_slope | `(ma(close,10)-delay(ma(close,10),5))\u002Fdelay(ma(close,10),5)` | positive | MA slope |\n| volume_ratio | `volume\u002Fma(volume,5)` | negative | Volume ratio |\n\n**Alpha101 Built-in Factors** (can reference `{baseDir}\u002Falpha101\u002Falpha_XXX`):\n\nQuantCLI includes 40 WorldQuant Alpha101 factors that can be directly referenced:\n\n| Factor | Category | Description |\n|--------|----------|-------------|\n| `alpha101\u002Falpha_001` | Reversal | 20-day new high then decline |\n| `alpha101\u002Falpha_002` | Reversal | Down volume bottom |\n| `alpha101\u002Falpha_003` | Volatility | Low volatility stability |\n| `alpha101\u002Falpha_004` | Capital Flow | Net capital inflow |\n| `alpha101\u002Falpha_005` | Trend | Uptrend |\n| `alpha101\u002Falpha_008` | Capital Flow | Capital inflow |\n| `alpha101\u002Falpha_009` | Momentum | Long-term momentum |\n| `alpha101\u002Falpha_010` | Reversal | MA deviation reversal |\n| `alpha101\u002Falpha_011` ~ `alpha_020` | Extended | Volatility, momentum, price-volume factors |\n| `alpha101\u002Falpha_021` ~ `alpha_030` | Extended | Price-volume, trend, strength factors |\n| `alpha101\u002Falpha_031` ~ `alpha_040` | Extended | Position, volatility, capital factors |\n\n**View all built-in factors:**\n```bash\nquantcli factors list\n```\n\n**Usage Example:**\n```yaml\nfactors:\n  - alpha101\u002Falpha_001   # Reversal factor\n  - alpha101\u002Falpha_008   # Capital inflow\n  - alpha101\u002Falpha_029   # 5-day momentum\nranking:\n  weights:\n    alpha101\u002Falpha_001: 0.4\n    alpha101\u002Falpha_008: 0.3\n    alpha101\u002Falpha_029: 0.3\n```\n\n**Screening Conditions Example**:\n```yaml\nscreening:\n  conditions:\n    - \"roe > 0.10\"              # ROE > 10%\n    - \"netprofitmargin > 0.05\"  # Net profit margin > 5%\n```\n\n### Step 3: Weight Configuration\n\nAllocate weights based on factor importance, 0 means only for screening, not scoring:\n\n```yaml\nranking:\n  weights:\n    # Fundamental factors\n    roe: 0.30\n    pe: 0.20\n    # Technical factors\n    ma_deviation: 0.30\n    momentum: 0.20\n  normalize: zscore\n```\n\n### Step 4: Generate Strategy File\n\nI will generate a complete strategy YAML file for you:\n\n```yaml\nname: Your Strategy Name\nversion: 1.0.0\ndescription: Strategy description\n\n# Stage 1: Fundamental screening\nscreening:\n  conditions:\n    - \"roe > 0.10\"\n    - \"pe \u003C 30\"\n  limit: 200\n\n# Stage 2: Technical ranking\nranking:\n  weights:\n    roe: 0.30\n    pe: 0.20\n    ma_deviation: 0.30\n    momentum: 0.20\n  normalize: zscore\n\noutput:\n  columns: [symbol, score, rank, roe, pe, momentum]\n  limit: 30\n```\n\n### Step 5: Run & Evaluate\n\n**Run strategy**:\n```bash\nquantcli filter run -f your_strategy.yaml --top 30\n```\n\n**Evaluation points**:\n1. **Selected stock count**: Check if screening conditions are reasonable\n2. **Factor distribution**: Distribution of factor scores\n3. **Industry diversification**: Avoid over-concentration\n\n## FAQ\n\n**Q: How to allocate factor weights?**\nA: Core factors 0.3-0.4, auxiliary factors 0.1-0.2, ensure weights sum close to 1\n\n**Q: Screening conditions too strict resulting in empty results?**\nA: Gradually relax conditions, first see how many stocks meet each condition\n\n**Q: What expression syntax is supported?**\nA: Supports 40+ built-in functions: `ma()`, `ema()`, `delay()`, `rolling_sum()`, `rsi()`, `rank()`, `zscore()`, etc.\n","{\"homepage\":\"https:\u002F\u002Fgitcode.com\u002Fdatavoid\u002Fquantcli\",\"user-invocable\":true}\n\n# Multi-Factor Strategy Assistant\n\nGuide you to create multi-factor stock selection strategies and generate independent YAML configuration files.\n\n## Install quantcli\n\n```bash\n# Install from PyPI (recommended)\npip install quantcli\n\n# Or install from source\ngit clone https:\u002F\u002Fgitcode.com\u002Fdatavoid\u002Fquantcli.git\ncd quantcli\npip install -e .\n```\n\nVerify installation:\n```bash\nquantcli --help\n```\n\n## Quick Start\n\nA complete multi-factor stock selection strategy YAML example:\n\n```yaml\nname: Value-Growth Hybrid Strategy\nversion: 1.0.0\ndescription: ROE + Momentum factor stock selection\n\nscreening:\n  fundamental_conditions:    # Stage 1: Financial condition screening\n    - \"roe > 0.10\"           # ROE > 10%\n    - \"pe_ttm \u003C 30\"          # P\u002FE \u003C 30\n    - \"pe_ttm > 0\"           # Exclude losses\n  daily_conditions:          # Stage 2: Price condition screening\n    - \"close > ma10\"         # Above 10-day MA\n  limit: 100                 # Keep at most 100 stocks\n\n# Factor configuration (supports two methods, factors at top level)\nfactors:\n  # Method 1: Inline factor definition\n  - name: ma10_deviation\n    expr: \"(close - ma(close, 10)) \u002F ma(close, 10)\"\n    direction: negative\n    description: \"10-day MA deviation\"\n\n  # Method 2: External reference (reference factor files in factors\u002F directory, include .yaml suffix)\n  - factors\u002Falpha_001.yaml\n  - factors\u002Falpha_008.yaml\n\nranking:\n  weights:                   # Weight fusion\n    ma10_deviation: 0.20     # Inline factor\n    factors\u002Falpha_001.yaml: 0.40  # External reference factor\n    factors\u002Falpha_008.yaml: 0.40\n  normalize: zscore          # Normalization method\n\noutput:\n  limit: 30                  # Output top 30 stocks\n  columns: [symbol, name, score, roe, pe_ttm, close, ma10_deviation]\n```\n\n### Factor Configuration Methods\n\n**Factor configuration supports two methods (can be mixed):**\n\n| Method | Type | Example | Description |\n|--------|------|---------|-------------|\n| **Inline** | `dict` | `{name: xxx, expr: \"...\"}` | Define expression directly in YAML |\n| **External** | `str` | `factors\u002Falpha_001.yaml` | Load factor file from `factors\u002F` directory |\n\n**Example: Mixed usage**\n\n```yaml\nfactors:\n  # Inline: Custom factor\n  - name: custom_momentum\n    expr: \"close \u002F delay(close, 20) - 1\"\n    direction: positive\n\n  # External: Alpha101 factor library (include .yaml suffix)\n  - factors\u002Falpha_001.yaml\n  - factors\u002Falpha_005.yaml\n  - factors\u002Falpha_009.yaml\n\nranking:\n  weights:\n    custom_momentum: 0.3\n    factors\u002Falpha_001.yaml: 0.3\n    factors\u002Falpha_005.yaml: 0.2\n    factors\u002Falpha_009.yaml: 0.2\n```\n\nRun strategy:\n```bash\nquantcli filter run -f your_strategy.yaml\n```\n\n## Invocation\n\n```\n\u002Fmulti-factor-strategy\n```\n\n## Available Expression Functions\n\n### Data Processing Functions\n| Function | Usage | Description |\n|----------|-------|-------------|\n| delay | `delay(x, n)` | Lag n periods |\n| ma | `ma(x, n)` | Simple moving average |\n| ema | `ema(x, n)` | Exponential moving average |\n| rolling_sum | `rolling_sum(x, n)` | Rolling sum |\n| rolling_std | `rolling_std(x, n)` | Rolling standard deviation |\n\n### Technical Indicator Functions\n| Function | Usage | Description |\n|----------|-------|-------------|\n| rsi | `rsi(x, n=14)` | Relative strength index |\n| correlation | `correlation(x, y, n)` | Correlation coefficient |\n| cross_up | `cross_up(a, b)` | Golden cross (a crosses above b) |\n| cross_down | `cross_down(a, b)` | Death cross (a crosses below b) |\n\n### Ranking & Normalization Functions\n| Function | Usage | Description |\n|----------|-------|-------------|\n| rank | `rank(x)` | Cross-sectional ranking (0-1) |\n| zscore | `zscore(x)` | Standardization |\n| sign | `sign(x)` | Sign function |\n| clamp | `clamp(x, min, max)` | Clipping function |\n\n### Conditional Functions\n| Function | Usage | Description |\n|----------|-------|-------------|\n| where | `where(cond, t, f)` | Conditional selection |\n| if | `if(cond, t, f)` | Conditional selection (alias) |\n\n### Base Fields\n| Field | Description |\n|-------|-------------|\n| open, high, low, close | OHLC prices |\n| volume | Trading volume |\n| pe, pb | P\u002FE ratio, P\u002FB ratio |\n| roe | Return on equity |\n| netprofitmargin | Net profit margin |\n\n## Guided Workflow\n\n### Step 1: Strategy Goal定位\n\nI will first understand your strategy needs:\n- **Strategy Type**: Value, Growth, Momentum, Volatility, Hybrid\n- **Selection Count**: Concentrated(10-30), Medium(50-100), Diversified(200+)\n- **Holding Period**: Intraday, Short-term(week), Medium-term(month), Long-term(quarter)\n\n### Step 2: Factor Selection\n\nBased on your strategy goals, recommend suitable factor combinations:\n\n**Common Fundamental Factors**:\n| Factor | Expression | Direction | Description |\n|--------|------------|-----------|-------------|\n| roe | `roe` | positive | Return on equity |\n| pe | `pe` | negative | Lower P\u002FE is better |\n| pb | `pb` | negative | Price-to-book ratio |\n| netprofitmargin | `netprofitmargin` | positive | Net profit margin |\n| revenue_growth | `revenue_yoy` | positive | Revenue growth rate |\n\n**Common Technical Factors**:\n| Factor | Expression | Direction | Description |\n|--------|------------|-----------|-------------|\n| momentum | `(close\u002Fdelay(close,20))-1` | positive | N-day momentum |\n| ma_deviation | `(close-ma(close,10))\u002Fma(close,10)` | negative | MA deviation |\n| ma_slope | `(ma(close,10)-delay(ma(close,10),5))\u002Fdelay(ma(close,10),5)` | positive | MA slope |\n| volume_ratio | `volume\u002Fma(volume,5)` | negative | Volume ratio |\n\n**Alpha101 Built-in Factors** (can reference `{baseDir}\u002Falpha101\u002Falpha_XXX`):\n\nQuantCLI includes 40 WorldQuant Alpha101 factors that can be directly referenced:\n\n| Factor | Category | Description |\n|--------|----------|-------------|\n| `alpha101\u002Falpha_001` | Reversal | 20-day new high then decline |\n| `alpha101\u002Falpha_002` | Reversal | Down volume bottom |\n| `alpha101\u002Falpha_003` | Volatility | Low volatility stability |\n| `alpha101\u002Falpha_004` | Capital Flow | Net capital inflow |\n| `alpha101\u002Falpha_005` | Trend | Uptrend |\n| `alpha101\u002Falpha_008` | Capital Flow | Capital inflow |\n| `alpha101\u002Falpha_009` | Momentum | Long-term momentum |\n| `alpha101\u002Falpha_010` | Reversal | MA deviation reversal |\n| `alpha101\u002Falpha_011` ~ `alpha_020` | Extended | Volatility, momentum, price-volume factors |\n| `alpha101\u002Falpha_021` ~ `alpha_030` | Extended | Price-volume, trend, strength factors |\n| `alpha101\u002Falpha_031` ~ `alpha_040` | Extended | Position, volatility, capital factors |\n\n**View all built-in factors:**\n```bash\nquantcli factors list\n```\n\n**Usage Example:**\n```yaml\nfactors:\n  - alpha101\u002Falpha_001   # Reversal factor\n  - alpha101\u002Falpha_008   # Capital inflow\n  - alpha101\u002Falpha_029   # 5-day momentum\nranking:\n  weights:\n    alpha101\u002Falpha_001: 0.4\n    alpha101\u002Falpha_008: 0.3\n    alpha101\u002Falpha_029: 0.3\n```\n\n**Screening Conditions Example**:\n```yaml\nscreening:\n  conditions:\n    - \"roe > 0.10\"              # ROE > 10%\n    - \"netprofitmargin > 0.05\"  # Net profit margin > 5%\n```\n\n### Step 3: Weight Configuration\n\nAllocate weights based on factor importance, 0 means only for screening, not scoring:\n\n```yaml\nranking:\n  weights:\n    # Fundamental factors\n    roe: 0.30\n    pe: 0.20\n    # Technical factors\n    ma_deviation: 0.30\n    momentum: 0.20\n  normalize: zscore\n```\n\n### Step 4: Generate Strategy File\n\nI will generate a complete strategy YAML file for you:\n\n```yaml\nname: Your Strategy Name\nversion: 1.0.0\ndescription: Strategy description\n\n# Stage 1: Fundamental screening\nscreening:\n  conditions:\n    - \"roe > 0.10\"\n    - \"pe \u003C 30\"\n  limit: 200\n\n# Stage 2: Technical ranking\nranking:\n  weights:\n    roe: 0.30\n    pe: 0.20\n    ma_deviation: 0.30\n    momentum: 0.20\n  normalize: zscore\n\noutput:\n  columns: [symbol, score, rank, roe, pe, momentum]\n  limit: 30\n```\n\n### Step 5: Run & Evaluate\n\n**Run strategy**:\n```bash\nquantcli filter run -f your_strategy.yaml --top 30\n```\n\n**Evaluation points**:\n1. **Selected stock count**: Check if screening conditions are reasonable\n2. **Factor distribution**: Distribution of factor scores\n3. **Industry diversification**: Avoid over-concentration\n\n## FAQ\n\n**Q: How to allocate factor weights?**\nA: Core factors 0.3-0.4, auxiliary factors 0.1-0.2, ensure weights sum close to 1\n\n**Q: Screening conditions too strict resulting in empty results?**\nA: Gradually relax conditions, first see how many stocks meet each condition\n\n**Q: What expression syntax is supported?**\nA: Supports 40+ built-in functions: `ma()`, `ema()`, `delay()`, `rolling_sum()`, `rsi()`, `rank()`, `zscore()`, etc.\n",{"name":5,"description":7},"main","f4cb2d49f7667e79ef7804f50396d5aaffdf1f56","Git和GitHub\u002Fmulti-factor-strategy",1,"https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification","https:\u002F\u002Fgithub.com\u002FKernel8901","a little little little dobe in life but science geek","2026-04-05 16:43:56",{"source":8,"skillName":5,"commands":47,"prompt":57,"zipAvailable":58},[48,51,54],{"tool":49,"command":50},"npx","npx skills add Kernel8901\u002Fai-agent-skills-classification --skill multi-factor-strategy",{"tool":52,"command":53},"bunx","bunx skills add Kernel8901\u002Fai-agent-skills-classification --skill multi-factor-strategy",{"tool":55,"command":56},"pnpm","pnpm dlx skills add Kernel8901\u002Fai-agent-skills-classification --skill multi-factor-strategy","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002FGit和GitHub\u002Fmulti-factor-strategy\u002Fskill.md",true,[60],{"path":61,"resourceType":62,"resourceTypeDesc":63,"sizeBytes":64,"sourceHtmlUrl":65},"_meta.json","REFERENCE","参考文档",296,"https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002FGit和GitHub\u002Fmulti-factor-strategy\u002F_meta.json",[67,91,127,138,148,178],{"skillNo":68,"name":69,"nameZh":6,"description":70,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":71,"categories":72,"keywords":73,"publishedAt":33},"01M6Q5R6WZ06RRB9RQTW54B34B","zyfai","Earn yield on any Ethereum wallet on Base, Arbitrum, and Plasma. Use when a user wants passive DeFi yield on their funds. 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