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Covers hypothesis testing, re...\n\nTags: latest:2.0.0\n\nVersion history:\n\nv2.0.0 | 2026-06-05T04:26:36.178Z | auto\n\ndata-analysis-plus 2.0.0 introduces major enhancements:\n\n- Expanded features: now includes code templates in both Python and R, a visualization gallery, a variety of statistical methods, and automated report generation.\n- Covers a broad set of analysis types: hypothesis testing, regression, clustering, and time series.\n- Adds a quick reference guide for key Python/R commands per analysis type.\n- Improved data validation with quality checks before analysis.\n- Provides ready-to-use templates for data cleaning, analysis, and visualization.\n- Includes report templates for executive summaries and technical documentation.\n\nArchive index:\n\nArchive v2.0.0: 3 files, 4841 bytes\n\nFiles: skill-card.md (1728b), SKILL.md (8535b), _meta.json (137b)\n\nFile v2.0.0:SKILL.md\n\n---\nname: data-analysis-plus\ndescription: \"Enhanced data analysis with Python/R code templates, visualization gallery, statistical tests, and automated report generation. Covers hypothesis testing, regression, clustering, time series, and more.\"\nmetadata:\n  author: opencode\n  version: 2.0\n  tags: data-analysis, statistics, visualization, python, r\n  compatibility: opencode\n  license: MIT\n---\n\n# Data Analysis Plus\n\nEnhanced data analysis with code templates, visualization gallery, and statistical methods.\n\n## Features\n\n- **Code Templates**: Python/R ready-to-use templates\n- **Visualization Gallery**: Charts for every analysis type\n- **Statistical Methods**: Hypothesis testing, regression, clustering\n- **Automated Reports**: Decision-ready output formats\n- **Data Validation**: Quality checks before analysis\n\n## Quick Reference\n\n| Analysis Type | Python Template | R Template |\n|---------------|-----------------|------------|\n| Descriptive | `df.describe()` | `summary(df)` |\n| Hypothesis | `scipy.stats.ttest_ind()` | `t.test()` |\n| Regression | `sklearn.linear_model` | `lm()` |\n| Clustering | `sklearn.cluster.KMeans` | `kmeans()` |\n| Time Series | `statsmodels.tsa` | `forecast::auto.arima()` |\n\n## Python Templates\n\n### Data Loading\n\n```python\nimport pandas as pd\nimport numpy as np\n\n# CSV\ndf = pd.read_csv(\"data.csv\")\n\n# Excel\ndf = pd.read_excel(\"data.xlsx\")\n\n# JSON\ndf = pd.read_json(\"data.json\")\n\n# Database\nimport sqlalchemy\nengine = sqlalchemy.create_engine(\"sqlite:///data.db\")\ndf = pd.read_sql(\"SELECT * FROM table\", engine)\n```\n\n### Descriptive Statistics\n\n```python\n# Basic stats\ndf.describe()\n\n# By group\ndf.groupby(\"category\").agg({\n    \"value\": [\"mean\", \"median\", \"std\", \"count\"]\n})\n\n# Correlation\ndf.corr()\n```\n\n### Data Cleaning\n\n```python\n# Missing values\ndf.isnull().sum()\ndf.fillna(df.mean())\ndf.dropna()\n\n# Duplicates\ndf.duplicated().sum()\ndf.drop_duplicates()\n\n# Outliers\nQ1 = df[\"value\"].quantile(0.25)\nQ3 = df[\"value\"].quantile(0.75)\nIQR = Q3 - Q1\ndf = df[(df[\"value\"] >= Q1 - 1.5*IQR) & (df[\"value\"] <= Q3 + 1.5*IQR)]\n```\n\n### Hypothesis Testing\n\n```python\nfrom scipy import stats\n\n# T-test\ngroup1 = df[df[\"group\"] == \"A\"][\"value\"]\ngroup2 = df[df[\"group\"] == \"B\"][\"value\"]\nstat, p_value = stats.ttest_ind(group1, group2)\n\n# Chi-square\ncontingency = pd.crosstab(df[\"cat1\"], df[\"cat2\"])\nchi2, p_value, dof, expected = stats.chi2_contingency(contingency)\n\n# ANOVA\nf_stat, p_value = stats.f_oneway(group1, group2, group3)\n```\n\n### Regression\n\n```python\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.model_selection import train_test_split\n\nX = df[[\"feature1\", \"feature2\"]]\ny = df[\"target\"]\n\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n\nmodel = LinearRegression()\nmodel.fit(X_train, y_train)\n\nprint(f\"R²: {model.score(X_test, y_test)}\")\nprint(f\"Coefficients: {model.coef_}\")\n```\n\n### Clustering\n\n```python\nfrom sklearn.cluster import KMeans\nfrom sklearn.preprocessing import StandardScaler\n\nscaler = StandardScaler()\nX_scaled = scaler.fit_transform(df[[\"feature1\", \"feature2\"]])\n\nkmeans = KMeans(n_clusters=3, random_state=42)\ndf[\"cluster\"] = kmeans.fit_predict(X_scaled)\n```\n\n### Time Series\n\n```python\nimport pandas as pd\nfrom statsmodels.tsa.seasonal import seasonal_decompose\n\n# Parse dates\ndf[\"date\"] = pd.to_datetime(df[\"date\"])\ndf = df.set_index(\"date\")\n\n# Decompose\ndecomposition = seasonal_decompose(df[\"value\"], model=\"additive\", period=12)\ndecomposition.plot()\n```\n\n## R Templates\n\n### Data Loading\n\n```r\nlibrary(readr)\nlibrary(readxl)\n\n# CSV\ndf <- read_csv(\"data.csv\")\n\n# Excel\ndf <- read_excel(\"data.xlsx\")\n\n# JSON\nlibrary(jsonlite)\ndf <- fromJSON(\"data.json\")\n```\n\n### Descriptive Statistics\n\n```r\n# Basic stats\nsummary(df)\n\n# By group\nlibrary(dplyr)\ndf %>%\n  group_by(category) %>%\n  summarise(\n    mean = mean(value, na.rm = TRUE),\n    median = median(value, na.rm = TRUE),\n    sd = sd(value, na.rm = TRUE),\n    n = n()\n  )\n\n# Correlation\ncor(df[, sapply(df, is.numeric)], use = \"complete.obs\")\n```\n\n### Hypothesis Testing\n\n```r\n# T-test\nt.test(value ~ group, data = df)\n\n# Chi-square\nchisq.test(table(df$cat1, df$cat2))\n\n# ANOVA\naov_result <- aov(value ~ group, data = df)\nsummary(aov_result)\n```\n\n### Regression\n\n```r\n# Linear regression\nmodel <- lm(target ~ feature1 + feature2, data = df)\nsummary(model)\n\n# Logistic regression\nmodel <- glm(binary_target ~ feature1 + feature2, data = df, family = \"binomial\")\nsummary(model)\n```\n\n### Clustering\n\n```r\n# K-means\nlibrary(cluster)\ndf_scaled <- scale(df[, c(\"feature1\", \"feature2\")])\nkmeans_result <- kmeans(df_scaled, centers = 3)\ndf$cluster <- kmeans_result$cluster\n```\n\n## Visualization Gallery\n\n### Chart Selection Guide\n\n| Question Type | Chart | Python | R |\n|---------------|-------|--------|---|\n| Trend over time | Line | `matplotlib` | `ggplot2` |\n| Comparison | Bar | `seaborn.barplot` | `ggplot2::geom_bar` |\n| Distribution | Histogram | `seaborn.histplot` | `ggplot2::geom_histogram` |\n| Relationship | Scatter | `seaborn.scatterplot` | `ggplot2::geom_point` |\n| Composition | Pie/Stacked Bar | `matplotlib.pyplot.pie` | `ggplot2::geom_bar(position=\"fill\")` |\n| Correlation | Heatmap | `seaborn.heatmap` | `pheatmap` |\n\n### Python Visualization\n\n```python\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\n# Line plot\nplt.figure(figsize=(10, 6))\nsns.lineplot(data=df, x=\"date\", y=\"value\", hue=\"category\")\nplt.title(\"Trend Over Time\")\nplt.savefig(\"trend.png\", dpi=300, bbox_inches=\"tight\")\n\n# Scatter plot\nplt.figure(figsize=(10, 6))\nsns.scatterplot(data=df, x=\"feature1\", y=\"feature2\", hue=\"target\")\nplt.title(\"Feature Relationship\")\nplt.savefig(\"scatter.png\", dpi=300, bbox_inches=\"tight\")\n\n# Heatmap\nplt.figure(figsize=(10, 8))\nsns.heatmap(df.corr(), annot=True, cmap=\"coolwarm\", center=0)\nplt.title(\"Correlation Matrix\")\nplt.savefig(\"heatmap.png\", dpi=300, bbox_inches=\"tight\")\n```\n\n### R Visualization\n\n```r\nlibrary(ggplot2)\n\n# Line plot\nggplot(df, aes(x = date, y = value, color = category)) +\n  geom_line() +\n  labs(title = \"Trend Over Time\") +\n  theme_minimal()\n\n# Scatter plot\nggplot(df, aes(x = feature1, y = feature2, color = target)) +\n  geom_point() +\n  labs(title = \"Feature Relationship\") +\n  theme_minimal()\n\n# Bar plot\nggplot(df, aes(x = category, fill = category)) +\n  geom_bar() +\n  labs(title = \"Category Distribution\") +\n  theme_minimal()\n```\n\n## Statistical Methods\n\n### Hypothesis Testing Decision Tree\n\n```\nIs the data categorical?\n├── Yes → Chi-square test\n└── No → Is the data normally distributed?\n    ├── Yes → T-test (2 groups) / ANOVA (3+ groups)\n    └── No → Mann-Whitney U (2 groups) / Kruskal-Wallis (3+ groups)\n```\n\n### Sample Size Calculator\n\n```python\ndef sample_size计算器(effect_size, alpha=0.05, power=0.8):\n    from statsmodels.stats.power import TTestIndPower\n    analysis = TTestIndPower()\n    n = analysis.solve_power(effect_size=effect_size, alpha=alpha, power=power)\n    return int(np.ceil(n * 2))  # Total sample size\n```\n\n## Report Templates\n\n### Executive Summary\n\n```markdown\n# [Analysis Title]\n\n## Key Findings\n1. [Finding 1 with evidence]\n2. [Finding 2 with evidence]\n3. [Finding 3 with evidence]\n\n## Methodology\n- Data: [source, timeframe, sample size]\n- Methods: [statistical tests used]\n- Limitations: [caveats]\n\n## Recommendations\n1. [Action 1]\n2. [Action 2]\n3. [Action 3]\n\n## Appendix\n- Charts: [list of visualizations]\n- Statistical tables: [p-values, confidence intervals]\n```\n\n### Technical Report\n\n```markdown\n# [Analysis Title] - Technical Report\n\n## Data\n- Source: [database/file]\n- Records: [count]\n- Variables: [list with types]\n- Missing values: [summary]\n\n## Methods\n- [Method 1]: [justification]\n- [Method 2]: [justification]\n\n## Results\n### [Test 1]\n- Statistic: [value]\n- p-value: [value]\n- Effect size: [value]\n- Confidence interval: [range]\n\n## Code\n[Reproducible code]\n```\n\n## Best Practices\n\n1. **Start with EDA** - Understand data before analysis\n2. **Validate data** - Check quality, missing values, outliers\n3. **Document assumptions** - State methodology choices\n4. **Report uncertainty** - Confidence intervals, p-values\n5. **Visualize results** - Charts communicate better\n6. **Make reproducible** - Save code, set seeds\n7. **Peer review** - Have someone check your work\n\n## Common Pitfalls\n\n| Pitfall | Solution |\n|---------|----------|\n| P-hacking | Pre-register hypotheses |\n| Correlation ≠ causation | Use experiments when possible |\n| Selection bias | Random sampling |\n| Survivorship bias | Include failures |\n| Simpson's paradox | Stratify analysis |\n\nFile v2.0.0:_meta.json\n\n{\n  \"ownerId\": \"kn71pk44ca87scz3pstt90r66n80xhaa\",\n  \"slug\": \"data-analysis-plus\",\n  \"version\": \"2.0.0\",\n  \"publishedAt\": 1780633596178\n}\n\nFile v2.0.0:skill-card.md\n\n## Description:\n\nEnhanced data analysis with Python/R code templates, visualization gallery, statistical tests, and automated report generation.\n\nThis skill is ready for commercial/non-commercial use.\n\n## Publisher:\n\n[534422530](https://clawhub.ai/user/534422530)\n\n### License/Terms of Use:\n\nMIT-0\n\n## Use Case:\n\nDevelopers, analysts, and data practitioners use this skill to generate Python and R templates for data loading, cleaning, statistical analysis, visualization, and report drafting.\n\n### Deployment Geography for Use:\n\nGlobal\n\n## Known Risks and Mitigations:\n\nRisk: Visualization snippets save PNG files such as trend.png, scatter.png, and heatmap.png in the working directory, which can overwrite existing files.\n\nMitigation: Review and change output paths or filenames before running copied snippets.\n\nRisk: Generated statistical templates can support misleading conclusions if data quality, assumptions, uncertainty, or sampling bias are not checked.\n\nMitigation: Validate the data, document assumptions, report uncertainty, and review results before using the analysis for decisions.\n\n## Reference(s):\n\n\n## Skill Output:\n\n**Output Type(s):** [Text, Markdown, Code, Guidance]\n\n**Output Format:** [Markdown with Python, R, and report-template code blocks]\n\n**Output Parameters:** [1D]\n\n**Other Properties Related to Output:** [May include copied examples that write local chart image files.]\n\n## Skill Version(s):\n\n2.0.0 (source: release evidence)\n\n## Ethical Considerations:\n\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.","readmeExcerpt":"Skill: Data Analysis Plus Owner: 534422530 Summary: Enhanced data analysis with Python/R code templates, visualization gallery, statistical tests, and automated report generation. Covers hypothesis testing, re... Tags: latest:2.0.0 Version history: v2.0.0 | 2026-06-05T04:26:36.178Z | auto data-analysis-plus 2.0.0 introduces major enhancements: - Expanded features: now includes code templates in both Python and R, a v","codeSnippets":[],"executableExamples":[{"language":"python","snippet":"import pandas as pd\nimport numpy as np\n\n# CSV\ndf = pd.read_csv(\"data.csv\")\n\n# Excel\ndf = pd.read_excel(\"data.xlsx\")\n\n# JSON\ndf = pd.read_json(\"data.json\")\n\n# Database\nimport sqlalchemy\nengine = sqlalchemy.create_engine(\"sqlite:///data.db\")\ndf = pd.read_sql(\"SELECT * FROM table\", engine)"},{"language":"python","snippet":"# Basic stats\ndf.describe()\n\n# By group\ndf.groupby(\"category\").agg({\n    \"value\": [\"mean\", \"median\", \"std\", \"count\"]\n})\n\n# Correlation\ndf.corr()"},{"language":"python","snippet":"# Missing values\ndf.isnull().sum()\ndf.fillna(df.mean())\ndf.dropna()\n\n# Duplicates\ndf.duplicated().sum()\ndf.drop_duplicates()\n\n# Outliers\nQ1 = df[\"value\"].quantile(0.25)\nQ3 = df[\"value\"].quantile(0.75)\nIQR = Q3 - Q1\ndf = df[(df[\"value\"] >= Q1 - 1.5*IQR) & (df[\"value\"] <= Q3 + 1.5*IQR)]"},{"language":"python","snippet":"from scipy import stats\n\n# T-test\ngroup1 = df[df[\"group\"] == \"A\"][\"value\"]\ngroup2 = df[df[\"group\"] == \"B\"][\"value\"]\nstat, p_value = stats.ttest_ind(group1, group2)\n\n# Chi-square\ncontingency = pd.crosstab(df[\"cat1\"], df[\"cat2\"])\nchi2, p_value, dof, expected = stats.chi2_contingency(contingency)\n\n# ANOVA\nf_stat, p_value = stats.f_oneway(group1, group2, group3)"},{"language":"python","snippet":"from sklearn.linear_model import LinearRegression\nfrom sklearn.model_selection import train_test_split\n\nX = df[[\"feature1\", \"feature2\"]]\ny = df[\"target\"]\n\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n\nmodel = LinearRegression()\nmodel.fit(X_train, y_train)\n\nprint(f\"R²: {model.score(X_test, y_test)}\")\nprint(f\"Coefficients: {model.coef_}\")"},{"language":"python","snippet":"from sklearn.cluster import KMeans\nfrom sklearn.preprocessing import StandardScaler\n\nscaler = StandardScaler()\nX_scaled = scaler.fit_transform(df[[\"feature1\", \"feature2\"]])\n\nkmeans = KMeans(n_clusters=3, random_state=42)\ndf[\"cluster\"] = kmeans.fit_predict(X_scaled)"}],"parameters":null,"dependencies":[],"permissions":[],"extractedFiles":[{"path":"SKILL.md","content":"---\nname: data-analysis-plus\ndescription: \"Enhanced data analysis with Python/R code templates, visualization gallery, statistical tests, and automated report generation. Covers hypothesis testing, regression, clustering, time series, and more.\"\nmetadata:\n  author: opencode\n  version: 2.0\n  tags: data-analysis, statistics, visualization, python, r\n  compatibility: opencode\n  license: MIT\n---\n\n# Data Analysis Plus\n\nEnhanced data analysis with code templates, visualization gallery, and statistical methods.\n\n## Features\n\n- **Code Templates**: Python/R ready-to-use templates\n- **Visualization Gallery**: Charts for every analysis type\n- **Statistical Methods**: Hypothesis testing, regression, clustering\n- **Automated Reports**: Decision-ready output formats\n- **Data Validation**: Quality checks before analysis\n\n## Quick Reference\n\n| Analysis Type | Python Template | R Template |\n|---------------|-----------------|------------|\n| Descriptive | `df.describe()` | `summary(df)` |\n| Hypothesis | `scipy.stats.ttest_ind()` | `t.test()` |\n| Regression | `sklearn.linear_model` | `lm()` |\n| Clustering | `sklearn.cluster.KMeans` | `kmeans()` |\n| Time Series | `statsmodels.tsa` | `forecast::auto.arima()` |\n\n## Python Templates\n\n### Data Loading\n\n```python\nimport pandas as pd\nimport numpy as np\n\n# CSV\ndf = pd.read_csv(\"data.csv\")\n\n# Excel\ndf = pd.read_excel(\"data.xlsx\")\n\n# JSON\ndf = pd.read_json(\"data.json\")\n\n# Database\nimport sqlalchemy\nengine = sqlalchemy.create_engine(\"sqlite:///data.db\")\ndf = pd.read_sql(\"SELECT * FROM table\", engine)\n```\n\n### Descriptive Statistics\n\n```python\n# Basic stats\ndf.describe()\n\n# By group\ndf.groupby(\"category\").agg({\n    \"value\": [\"mean\", \"median\", \"std\", \"count\"]\n})\n\n# Correlation\ndf.corr()\n```\n\n### Data Cleaning\n\n```python\n# Missing values\ndf.isnull().sum()\ndf.fillna(df.mean())\ndf.dropna()\n\n# Duplicates\ndf.duplicated().sum()\ndf.drop_duplicates()\n\n# Outliers\nQ1 = df[\"value\"].quantile(0.25)\nQ3 = df[\"value\"].quantile(0.75)\nIQR = Q3 - Q1\ndf = df[(df[\"value\"] >= Q1 - 1.5*IQR) & (df[\"value\"] <= Q3 + 1.5*IQR)]\n```\n\n### Hypothesis Testing\n\n```python\nfrom scipy import stats\n\n# T-test\ngroup1 = df[df[\"group\"] == \"A\"][\"value\"]\ngroup2 = df[df[\"group\"] == \"B\"][\"value\"]\nstat, p_value = stats.ttest_ind(group1, group2)\n\n# Chi-square\ncontingency = pd.crosstab(df[\"cat1\"], df[\"cat2\"])\nchi2, p_value, dof, expected = stats.chi2_contingency(contingency)\n\n# ANOVA\nf_stat, p_value = stats.f_oneway(group1, group2, group3)\n```\n\n### Regression\n\n```python\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.model_selection import train_test_split\n\nX = df[[\"feature1\", \"feature2\"]]\ny = df[\"target\"]\n\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n\nmodel = LinearRegression()\nmodel.fit(X_train, y_train)\n\nprint(f\"R²: {model.score(X_test, y_test)}\")\nprint(f\"Coefficients: {model.coef_}\")\n```\n\n### Clustering\n\n```python\nfrom sklearn.cluster import KMeans\nfrom sklearn.preprocessing import StandardScaler\n\nscaler = StandardScaler()\nX_sca"},{"path":"_meta.json","content":"{\n  \"ownerId\": \"kn71pk44ca87scz3pstt90r66n80xhaa\",\n  \"slug\": \"data-analysis-plus\",\n  \"version\": \"2.0.0\",\n  \"publishedAt\": 1780633596178\n}"},{"path":"skill-card.md","content":"## Description:\n\nEnhanced data analysis with Python/R code templates, visualization gallery, statistical tests, and automated report generation.\n\nThis skill is ready for commercial/non-commercial use.\n\n## Publisher:\n\n[534422530](https://clawhub.ai/user/534422530)\n\n### License/Terms of Use:\n\nMIT-0\n\n## Use Case:\n\nDevelopers, analysts, and data practitioners use this skill to generate Python and R templates for data loading, cleaning, statistical analysis, visualization, and report drafting.\n\n### Deployment Geography for Use:\n\nGlobal\n\n## Known Risks and Mitigations:\n\nRisk: Visualization snippets save PNG files such as trend.png, scatter.png, and heatmap.png in the working directory, which can overwrite existing files.\n\nMitigation: Review and change output paths or filenames before running copied snippets.\n\nRisk: Generated statistical templates can support misleading conclusions if data quality, assumptions, uncertainty, or sampling bias are not checked.\n\nMitigation: Validate the data, document assumptions, report uncertainty, and review results before using the analysis for decisions.\n\n## Reference(s):\n\n\n## Skill Output:\n\n**Output Type(s):** [Text, Markdown, Code, Guidance]\n\n**Output Format:** [Markdown with Python, R, and report-template code blocks]\n\n**Output Parameters:** [1D]\n\n**Other Properties Related to Output:** [May include copied examples that write local chart image files.]\n\n## Skill Version(s):\n\n2.0.0 (source: release evidence)\n\n## Ethical Considerations:\n\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment."}],"languages":[],"docsSourceLabel":"CLAWHUB","editorialOverview":"Enhanced data analysis with Python/R code templates, visualization gallery, statistical tests, and automated report generation. Covers hypothesis testing, re... Skill: Data Analysis Plus Owner: 534422530 Summary: Enhanced data analysis with Python/R code templates, visualization gallery, statistical tests, and automated report generation. Covers hypothesis testing, re... Tags: latest:2.0.0 Version history: v2.0.0 | 2026-06-05T04:26:36.178Z | auto data-analysis-plus 2.0.0 introduces major enhancements: - Expanded features: now includes code templates in both Python and R, a v","editorialQuality":{"score":100,"threshold":65,"status":"ready","wordCount":840,"uniquenessScore":52,"reasons":[]}},"media":{"evidence":{"source":"no-media","verified":false,"confidence":"low","updatedAt":"2026-10-11T00:59:09.930Z","emptyReason":"No screenshots, media assets, or demo links are available."},"primaryImageUrl":null,"mediaAssetCount":0,"assets":[],"demoUrl":null},"ownerResources":{"evidence":{"source":"unclaimed","verified":false,"confidence":"low","updatedAt":"2026-10-11T00:59:09.930Z","emptyReason":"This page has not been claimed by the agent owner."},"hasCustomPage":false,"customPageUpdatedAt":null,"customLinks":[],"structuredLinks":{"docsUrl":null,"demoUrl":null,"supportUrl":null,"pricingUrl":null,"statusUrl":null},"customPage":null},"relatedAgents":{"evidence":{"source":"protocol-neighbors","verified":false,"confidence":"medium","updatedAt":"2026-10-11T03:54:18.070Z","emptyReason":null},"items":[{"id":"8ebccd8e-3863-4187-8355-c3f14e1f9edf","entityType":"agent","canonicalPath":"/agent/iofficeai-aionui","slug":"iofficeai-aionui","name":"AionUi","description":"Free, local, open-source 24/7 Cowork app and OpenClaw for Gemini CLI, Claude Code, Codex, OpenCode, Qwen Code, Goose CLI, Auggie, and more | 🌟 Star if you like it!","url":"https://github.com/iOfficeAI/AionUi","homepage":"https://www.aionui.com","source":"GITHUB_REPOS","protocols":["MCP","OPENCLAW"],"capabilities":[],"safetyScore":100,"overallRank":70,"updatedAt":"2026-10-09T19:11:12.944Z","createdAt":"2026-02-25T03:38:16.584Z","downloads":null},{"id":"b917f68a-ebff-438e-84f8-3f4b2494c0bc","entityType":"agent","canonicalPath":"/agent/activepieces-activepieces","slug":"activepieces-activepieces","name":"activepieces","description":"AI Agents & MCPs & AI Workflow Automation • (~400 MCP servers for AI agents) • AI Automation / AI Agent with MCPs • AI Workflows & AI Agents • MCPs for AI Agents","url":"https://github.com/activepieces/activepieces","homepage":"https://www.activepieces.com","source":"GITHUB_REPOS","protocols":["OPENCLAW"],"capabilities":[],"safetyScore":100,"overallRank":70,"updatedAt":"2026-04-15T02:22:12.426Z","createdAt":"2026-02-25T03:38:12.412Z","downloads":null},{"id":"5cb26759-3a39-483f-94cf-276a98c13bb8","entityType":"agent","canonicalPath":"/agent/cherryhq-cherry-studio","slug":"cherryhq-cherry-studio","name":"cherry-studio","description":"AI productivity studio with smart chat, autonomous agents, and 300+ assistants. 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