[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M71895CH8R3Q76P1XG5H2K7W":3,"skill-same:01M71895CH8R3Q76P1XG5H2K7W":92},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":16,"sourceHtmlUrl":17,"categories":18,"keywords":19,"publishedAt":40,"linkOnly":41,"markdownContent":42,"markdownBody":43,"frontMatter":44,"refName":45,"commitSha":46,"skillDirectory":47,"forksCount":48,"repositoryHtmlUrl":49,"repositoryHomepageUrl":6,"ownerHtmlUrl":50,"ownerBlogUrl":51,"ownerDescription":6,"updatedAt":52,"install":53,"resources":66},"01M71895CH8R3Q76P1XG5H2K7W","single-cell-rna-qc",null,"Performs quality control on single-cell RNA-seq data (.h5ad or .h5 files) using scverse best practices with MAD-based filtering and comprehensive visualizations. Use when users request QC analysis, filtering low-quality cells, assessing data quality, or following scverse\u002Fscanpy best practices for single-cell analysis.","anthropics\u002Fknowledge-work-plugins","anthropics","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F76263028?v=4","01M697XGDPXJAVQ8391V2BCYTE","Anthropic","01M7188MPC48111TN566QNQE3X",35660,"Apache-2.0","https:\u002F\u002Fgithub.com\u002Fanthropics\u002Fknowledge-work-plugins\u002Fblob\u002Fmain\u002Fbio-research\u002Fskills\u002Fsingle-cell-rna-qc\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fanthropics\u002Fknowledge-work-plugins\u002Fblob\u002Fmain\u002Fbio-research\u002Fskills\u002Fsingle-cell-rna-qc\u002FSKILL.md",[],[20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39],"single cell rna qc","single","single cell","RNA","cell","qc","data quality","control","scanpy","with","users","analysis","best practices","when","use","filtering","files","for","h5ad","visualizations","2026-11-10 10:12:30",false,"---\nname: single-cell-rna-qc\ndescription: Performs quality control on single-cell RNA-seq data (.h5ad or .h5 files) using scverse best practices with MAD-based filtering and comprehensive visualizations. Use when users request QC analysis, filtering low-quality cells, assessing data quality, or following scverse\u002Fscanpy best practices for single-cell analysis.\n---\n\n# Single-Cell RNA-seq Quality Control\n\nAutomated QC workflow for single-cell RNA-seq data following scverse best practices.\n\n## When to Use This Skill\n\nUse when users:\n- Request quality control or QC on single-cell RNA-seq data\n- Want to filter low-quality cells or assess data quality\n- Need QC visualizations or metrics\n- Ask to follow scverse\u002Fscanpy best practices\n- Request MAD-based filtering or outlier detection\n\n**Supported input formats:**\n- `.h5ad` files (AnnData format from scanpy\u002FPython workflows)\n- `.h5` files (10X Genomics Cell Ranger output)\n\n**Default recommendation**: Use Approach 1 (complete pipeline) unless the user has specific custom requirements or explicitly requests non-standard filtering logic.\n\n## Approach 1: Complete QC Pipeline (Recommended for Standard Workflows)\n\nFor standard QC following scverse best practices, use the convenience script `scripts\u002Fqc_analysis.py`:\n\n```bash\npython3 scripts\u002Fqc_analysis.py input.h5ad\n# or for 10X Genomics .h5 files:\npython3 scripts\u002Fqc_analysis.py raw_feature_bc_matrix.h5\n```\n\nThe script automatically detects the file format and loads it appropriately.\n\n**When to use this approach:**\n- Standard QC workflow with adjustable thresholds (all cells filtered the same way)\n- Batch processing multiple datasets\n- Quick exploratory analysis\n- User wants the \"just works\" solution\n\n**Requirements:** anndata, scanpy, scipy, matplotlib, seaborn, numpy\n\n**Parameters:**\n\nCustomize filtering thresholds and gene patterns using command-line parameters:\n- `--output-dir` - Output directory\n- `--mad-counts`, `--mad-genes`, `--mad-mt` - MAD thresholds for counts\u002Fgenes\u002FMT%\n- `--mt-threshold` - Hard mitochondrial % cutoff\n- `--min-cells` - Gene filtering threshold\n- `--mt-pattern`, `--ribo-pattern`, `--hb-pattern` - Gene name patterns for different species\n\nUse `--help` to see current default values.\n\n**Outputs:**\n\nAll files are saved to `\u003Cinput_basename>_qc_results\u002F` directory by default (or to the directory specified by `--output-dir`):\n- `qc_metrics_before_filtering.png` - Pre-filtering visualizations\n- `qc_filtering_thresholds.png` - MAD-based threshold overlays\n- `qc_metrics_after_filtering.png` - Post-filtering quality metrics\n- `\u003Cinput_basename>_filtered.h5ad` - Clean, filtered dataset ready for downstream analysis\n- `\u003Cinput_basename>_with_qc.h5ad` - Original data with QC annotations preserved\n\nIf copying outputs for user access, copy individual files (not the entire directory) so users can preview them directly.\n\n### Workflow Steps\n\nThe script performs the following steps:\n\n1. **Calculate QC metrics** - Count depth, gene detection, mitochondrial\u002Fribosomal\u002Fhemoglobin content\n2. **Apply MAD-based filtering** - Permissive outlier detection using MAD thresholds for counts\u002Fgenes\u002FMT%\n3. **Filter genes** - Remove genes detected in few cells\n4. **Generate visualizations** - Comprehensive before\u002Fafter plots with threshold overlays\n\n## Approach 2: Modular Building Blocks (For Custom Workflows)\n\nFor custom analysis workflows or non-standard requirements, use the modular utility functions from `scripts\u002Fqc_core.py` and `scripts\u002Fqc_plotting.py`:\n\n```python\n# Run from scripts\u002F directory, or add scripts\u002F to sys.path if needed\nimport anndata as ad\nfrom qc_core import calculate_qc_metrics, detect_outliers_mad, filter_cells\nfrom qc_plotting import plot_qc_distributions  # Only if visualization needed\n\nadata = ad.read_h5ad('input.h5ad')\ncalculate_qc_metrics(adata, inplace=True)\n# ... custom analysis logic here\n```\n\n**When to use this approach:**\n- Different workflow needed (skip steps, change order, apply different thresholds to subsets)\n- Conditional logic (e.g., filter neurons differently than other cells)\n- Partial execution (only metrics\u002Fvisualization, no filtering)\n- Integration with other analysis steps in a larger pipeline\n- Custom filtering criteria beyond what command-line params support\n\n**Available utility functions:**\n\nFrom `qc_core.py` (core QC operations):\n- `calculate_qc_metrics(adata, mt_pattern, ribo_pattern, hb_pattern, inplace=True)` - Calculate QC metrics and annotate adata\n- `detect_outliers_mad(adata, metric, n_mads, verbose=True)` - MAD-based outlier detection, returns boolean mask\n- `apply_hard_threshold(adata, metric, threshold, operator='>', verbose=True)` - Apply hard cutoffs, returns boolean mask\n- `filter_cells(adata, mask, inplace=False)` - Apply boolean mask to filter cells\n- `filter_genes(adata, min_cells=20, min_counts=None, inplace=True)` - Filter genes by detection\n- `print_qc_summary(adata, label='')` - Print summary statistics\n\nFrom `qc_plotting.py` (visualization):\n- `plot_qc_distributions(adata, output_path, title)` - Generate comprehensive QC plots\n- `plot_filtering_thresholds(adata, outlier_masks, thresholds, output_path)` - Visualize filtering thresholds\n- `plot_qc_after_filtering(adata, output_path)` - Generate post-filtering plots\n\n**Example custom workflows:**\n\n**Example 1: Only calculate metrics and visualize, don't filter yet**\n```python\nadata = ad.read_h5ad('input.h5ad')\ncalculate_qc_metrics(adata, inplace=True)\nplot_qc_distributions(adata, 'qc_before.png', title='Initial QC')\nprint_qc_summary(adata, label='Before filtering')\n```\n\n**Example 2: Apply only MT% filtering, keep other metrics permissive**\n```python\nadata = ad.read_h5ad('input.h5ad')\ncalculate_qc_metrics(adata, inplace=True)\n\n# Only filter high MT% cells\nhigh_mt = apply_hard_threshold(adata, 'pct_counts_mt', 10, operator='>')\nadata_filtered = filter_cells(adata, ~high_mt)\nadata_filtered.write('filtered.h5ad')\n```\n\n**Example 3: Different thresholds for different subsets**\n```python\nadata = ad.read_h5ad('input.h5ad')\ncalculate_qc_metrics(adata, inplace=True)\n\n# Apply type-specific QC (assumes cell_type metadata exists)\nneurons = adata.obs['cell_type'] == 'neuron'\nother_cells = ~neurons\n\n# Neurons tolerate higher MT%, other cells use stricter threshold\nneuron_qc = apply_hard_threshold(adata[neurons], 'pct_counts_mt', 15, operator='>')\nother_qc = apply_hard_threshold(adata[other_cells], 'pct_counts_mt', 8, operator='>')\n```\n\n## Best Practices\n\n1. **Be permissive with filtering** - Default thresholds intentionally retain most cells to avoid losing rare populations\n2. **Inspect visualizations** - Always review before\u002Fafter plots to ensure filtering makes biological sense\n3. **Consider dataset-specific factors** - Some tissues naturally have higher mitochondrial content (e.g., neurons, cardiomyocytes)\n4. **Check gene annotations** - Mitochondrial gene prefixes vary by species (mt- for mouse, MT- for human)\n5. **Iterate if needed** - QC parameters may need adjustment based on the specific experiment or tissue type\n\n## Reference Materials\n\nFor detailed QC methodology, parameter rationale, and troubleshooting guidance, see `references\u002Fscverse_qc_guidelines.md`. This reference provides:\n- Detailed explanations of each QC metric and why it matters\n- Rationale for MAD-based thresholds and why they're better than fixed cutoffs\n- Guidelines for interpreting QC visualizations (histograms, violin plots, scatter plots)\n- Species-specific considerations for gene annotations\n- When and how to adjust filtering parameters\n- Advanced QC considerations (ambient RNA correction, doublet detection)\n\nLoad this reference when users need deeper understanding of the methodology or when troubleshooting QC issues.\n\n## Next Steps After QC\n\nTypical downstream analysis steps:\n- Ambient RNA correction (SoupX, CellBender)\n- Doublet detection (scDblFinder)\n- Normalization (log-normalize, scran)\n- Feature selection and dimensionality reduction\n- Clustering and cell type annotation\n","# Single-Cell RNA-seq Quality Control\n\nAutomated QC workflow for single-cell RNA-seq data following scverse best practices.\n\n## When to Use This Skill\n\nUse when users:\n- Request quality control or QC on single-cell RNA-seq data\n- Want to filter low-quality cells or assess data quality\n- Need QC visualizations or metrics\n- Ask to follow scverse\u002Fscanpy best practices\n- Request MAD-based filtering or outlier detection\n\n**Supported input formats:**\n- `.h5ad` files (AnnData format from scanpy\u002FPython workflows)\n- `.h5` files (10X Genomics Cell Ranger output)\n\n**Default recommendation**: Use Approach 1 (complete pipeline) unless the user has specific custom requirements or explicitly requests non-standard filtering logic.\n\n## Approach 1: Complete QC Pipeline (Recommended for Standard Workflows)\n\nFor standard QC following scverse best practices, use the convenience script `scripts\u002Fqc_analysis.py`:\n\n```bash\npython3 scripts\u002Fqc_analysis.py input.h5ad\n# or for 10X Genomics .h5 files:\npython3 scripts\u002Fqc_analysis.py raw_feature_bc_matrix.h5\n```\n\nThe script automatically detects the file format and loads it appropriately.\n\n**When to use this approach:**\n- Standard QC workflow with adjustable thresholds (all cells filtered the same way)\n- Batch processing multiple datasets\n- Quick exploratory analysis\n- User wants the \"just works\" solution\n\n**Requirements:** anndata, scanpy, scipy, matplotlib, seaborn, numpy\n\n**Parameters:**\n\nCustomize filtering thresholds and gene patterns using command-line parameters:\n- `--output-dir` - Output directory\n- `--mad-counts`, `--mad-genes`, `--mad-mt` - MAD thresholds for counts\u002Fgenes\u002FMT%\n- `--mt-threshold` - Hard mitochondrial % cutoff\n- `--min-cells` - Gene filtering threshold\n- `--mt-pattern`, `--ribo-pattern`, `--hb-pattern` - Gene name patterns for different species\n\nUse `--help` to see current default values.\n\n**Outputs:**\n\nAll files are saved to `\u003Cinput_basename>_qc_results\u002F` directory by default (or to the directory specified by `--output-dir`):\n- `qc_metrics_before_filtering.png` - Pre-filtering visualizations\n- `qc_filtering_thresholds.png` - MAD-based threshold overlays\n- `qc_metrics_after_filtering.png` - Post-filtering quality metrics\n- `\u003Cinput_basename>_filtered.h5ad` - Clean, filtered dataset ready for downstream analysis\n- `\u003Cinput_basename>_with_qc.h5ad` - Original data with QC annotations preserved\n\nIf copying outputs for user access, copy individual files (not the entire directory) so users can preview them directly.\n\n### Workflow Steps\n\nThe script performs the following steps:\n\n1. **Calculate QC metrics** - Count depth, gene detection, mitochondrial\u002Fribosomal\u002Fhemoglobin content\n2. **Apply MAD-based filtering** - Permissive outlier detection using MAD thresholds for counts\u002Fgenes\u002FMT%\n3. **Filter genes** - Remove genes detected in few cells\n4. **Generate visualizations** - Comprehensive before\u002Fafter plots with threshold overlays\n\n## Approach 2: Modular Building Blocks (For Custom Workflows)\n\nFor custom analysis workflows or non-standard requirements, use the modular utility functions from `scripts\u002Fqc_core.py` and `scripts\u002Fqc_plotting.py`:\n\n```python\n# Run from scripts\u002F directory, or add scripts\u002F to sys.path if needed\nimport anndata as ad\nfrom qc_core import calculate_qc_metrics, detect_outliers_mad, filter_cells\nfrom qc_plotting import plot_qc_distributions  # Only if visualization needed\n\nadata = ad.read_h5ad('input.h5ad')\ncalculate_qc_metrics(adata, inplace=True)\n# ... custom analysis logic here\n```\n\n**When to use this approach:**\n- Different workflow needed (skip steps, change order, apply different thresholds to subsets)\n- Conditional logic (e.g., filter neurons differently than other cells)\n- Partial execution (only metrics\u002Fvisualization, no filtering)\n- Integration with other analysis steps in a larger pipeline\n- Custom filtering criteria beyond what command-line params support\n\n**Available utility functions:**\n\nFrom `qc_core.py` (core QC operations):\n- `calculate_qc_metrics(adata, mt_pattern, ribo_pattern, hb_pattern, inplace=True)` - Calculate QC metrics and annotate adata\n- `detect_outliers_mad(adata, metric, n_mads, verbose=True)` - MAD-based outlier detection, returns boolean mask\n- `apply_hard_threshold(adata, metric, threshold, operator='>', verbose=True)` - Apply hard cutoffs, returns boolean mask\n- `filter_cells(adata, mask, inplace=False)` - Apply boolean mask to filter cells\n- `filter_genes(adata, min_cells=20, min_counts=None, inplace=True)` - Filter genes by detection\n- `print_qc_summary(adata, label='')` - Print summary statistics\n\nFrom `qc_plotting.py` (visualization):\n- `plot_qc_distributions(adata, output_path, title)` - Generate comprehensive QC plots\n- `plot_filtering_thresholds(adata, outlier_masks, thresholds, output_path)` - Visualize filtering thresholds\n- `plot_qc_after_filtering(adata, output_path)` - Generate post-filtering plots\n\n**Example custom workflows:**\n\n**Example 1: Only calculate metrics and visualize, don't filter yet**\n```python\nadata = ad.read_h5ad('input.h5ad')\ncalculate_qc_metrics(adata, inplace=True)\nplot_qc_distributions(adata, 'qc_before.png', title='Initial QC')\nprint_qc_summary(adata, label='Before filtering')\n```\n\n**Example 2: Apply only MT% filtering, keep other metrics permissive**\n```python\nadata = ad.read_h5ad('input.h5ad')\ncalculate_qc_metrics(adata, inplace=True)\n\n# Only filter high MT% cells\nhigh_mt = apply_hard_threshold(adata, 'pct_counts_mt', 10, operator='>')\nadata_filtered = filter_cells(adata, ~high_mt)\nadata_filtered.write('filtered.h5ad')\n```\n\n**Example 3: Different thresholds for different subsets**\n```python\nadata = ad.read_h5ad('input.h5ad')\ncalculate_qc_metrics(adata, inplace=True)\n\n# Apply type-specific QC (assumes cell_type metadata exists)\nneurons = adata.obs['cell_type'] == 'neuron'\nother_cells = ~neurons\n\n# Neurons tolerate higher MT%, other cells use stricter threshold\nneuron_qc = apply_hard_threshold(adata[neurons], 'pct_counts_mt', 15, operator='>')\nother_qc = apply_hard_threshold(adata[other_cells], 'pct_counts_mt', 8, operator='>')\n```\n\n## Best Practices\n\n1. **Be permissive with filtering** - Default thresholds intentionally retain most cells to avoid losing rare populations\n2. **Inspect visualizations** - Always review before\u002Fafter plots to ensure filtering makes biological sense\n3. **Consider dataset-specific factors** - Some tissues naturally have higher mitochondrial content (e.g., neurons, cardiomyocytes)\n4. **Check gene annotations** - Mitochondrial gene prefixes vary by species (mt- for mouse, MT- for human)\n5. **Iterate if needed** - QC parameters may need adjustment based on the specific experiment or tissue type\n\n## Reference Materials\n\nFor detailed QC methodology, parameter rationale, and troubleshooting guidance, see `references\u002Fscverse_qc_guidelines.md`. 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With Shopify connected it also runs a proactive order-triage mode that surfaces orders needing attention — unfulfilled past the promised window, payment problems, pending refunds, stuck shipments — and drafts the next action for each before the customer has to ask. 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US federal tax; a non-US business gets its closed-books packet instead. Two modes: (1) quarterly estimated tax from YTD net income in the ledger (MYOB, NetSuite, QuickBooks, Xero, or Zoho Books); (2) year-end 1099 prep, scanning the ledger, PayPal, and Stripe for contractors paid over USD 600 into a 1099-NEC list with missing W-9 flags.\nAny tax request routes first to \u002Ftax-prep, which confirms the books are closed and reconciled before running this skill. Use this skill directly only when the owner says the period's books are already closed: \"books are closed, now do the 1099s,\" \"run the quarterly estimate off the closed numbers,\" or \"just the contractor W-9 list.\"","https:\u002F\u002Fgithub.com\u002Fanthropics\u002Fknowledge-work-plugins\u002Fblob\u002Fmain\u002Fsmall-business\u002Fskills\u002Ftax-season-organizer\u002FSKILL.md",[],[126,127,128,129,130,131,132,133,134,135,33,136,137,138,139,140,118,141,142,143],"tax season organizer","tax","us federal","which","netsuite","paid","end","usd","only","now","books","xero","scanning","run","request","flags","list","stripe",{"skillNo":145,"name":146,"nameZh":6,"description":147,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":148,"categories":149,"keywords":150,"publishedAt":40},"01M718Y13VVPHDD9NTDBVFJ2AZ","tax-prep","Prepares tax materials as a two-link chain — month-end-prep confirms the books are closed and reconciled first, then tax-season-organizer calculates the quarterly estimated payment or builds the year-end 1099-NEC list and accountant packet from those closed numbers. 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An owner who says the period's books are already closed routes to tax-season-organizer directly.","https:\u002F\u002Fgithub.com\u002Fanthropics\u002Fknowledge-work-plugins\u002Fblob\u002Fmain\u002Fsmall-business\u002Fskills\u002Ftax-prep\u002FSKILL.md",[],[151,127,152,153,128,154,155,130,156,157,158,159,160,37,161,132,162,163,164,165],"tax prep","prep","SET","as","ready","never","zoho books","get","chain","profit","trigger","material","question","paypal","business",{"skillNo":167,"name":168,"nameZh":6,"description":169,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":170,"categories":171,"keywords":177,"publishedAt":40},"01M718XWTWQZ01GYSG9T8RW8TW","speed-to-lead","Makes sure no inbound lead goes unanswered. Watches web forms, the shared inbox, and the CRM for new inquiries, qualifies each one against the owner's criteria, drafts a reply in the owner's voice with real meeting times for the owner to approve and send, routes anything hot to a human right away, and logs the whole thing to the CRM. Never sends on its own. Built to run on a schedule so overnight inquiries are drafted and sorted by morning, and works from forwarded or pasted inquiries when no connector is available. Use this whenever the owner is worried about inbound leads or response time — including phrasings like \"leads are going cold,\" \"did anyone answer that inquiry,\" \"I keep missing form submissions,\" \"nothing falls through the cracks,\" \"handle my inbound,\" \"respond to this lead,\" or \"we lose people because nobody gets back fast enough.\" Reach for it for a single unanswered inquiry as readily as for a standing setup.","https:\u002F\u002Fgithub.com\u002Fanthropics\u002Fknowledge-work-plugins\u002Fblob\u002Fmain\u002Fsmall-business\u002Fskills\u002Fspeed-to-lead\u002FSKILL.md",[172],{"categoryNo":173,"categoryName":174,"taxonomyType":175,"taxonomyTypeDesc":176},"01JBSEEDCAT000000000000034","行政与办公人员","PROFESSION","适用职业",[178,179,180,181,182,183,156,184,185,186,187,188,189,190,33,191,192,109,193,194],"speed to lead","lead","speed","shared","back","cold","time","inbox","send","respond","new","web forms","people","schedule","logs","sure","human",{"skillNo":196,"name":197,"nameZh":6,"description":198,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":199,"categories":200,"keywords":204,"publishedAt":40},"01M718XQYMN1KY3TPS33967FK0","social-content-engine","Runs the owner's content operation as a standing thing rather than a one-off: keeps a rolling posting calendar, learns the owner's voice, generates on-brand Canva graphics, writes captions that sound like them, repurposes one asset into every channel format, and stages posts for approval instead of publishing on its own. Pulls product photos and prices from Shopify when connected; with no connectors, drafts the calendar and asset briefs for manual posting. Use this whenever the owner talks about posting, content, or social — including \"I need to post more,\" \"we've gone quiet on Instagram,\" \"what should we post this month,\" \"make the content,\" \"give me a month of posts,\" \"write this in my voice,\" \"repurpose this,\" or \"set up our content calendar.\" Reach for it for a single post as readily as for a full quarter. One handoff: a finished, approved campaign brief goes to canva-creator; this skill owns the standing operation.","https:\u002F\u002Fgithub.com\u002Fanthropics\u002Fknowledge-work-plugins\u002Fblob\u002Fmain\u002Fsmall-business\u002Fskills\u002Fsocial-content-engine\u002FSKILL.md",[201],{"categoryNo":202,"categoryName":203,"taxonomyType":175,"taxonomyTypeDesc":176},"01JBSEEDCAT000000000000036","内容创作者",[205,206,207,208,209,210,153,211,212,213,214,103,215,33,216,217,218,118,219,220],"social","social content engine","engine","content engine","social content","content","brief","posts","Captions","prices","campaign","me","product","calendar","write","drafts",{"skillNo":222,"name":223,"nameZh":6,"description":224,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":225,"categories":226,"keywords":227,"publishedAt":40},"01M718XMPJVH0EVRVP998BVZPC","smb-router","The front door to the Small Business plugin. Listens to what the owner needs right now — vague or specific — and routes them to the best skill or slash command for the moment. Also serves as a guide: explains what's available, suggests what to try next, and adapts recommendations based on stored business context. Trigger whenever the owner asks \"what can you do,\" \"help me with my business,\" \"what should I focus on,\" \"I don't know where to start,\" or any open-ended business request that doesn't clearly match a single skill.","https:\u002F\u002Fgithub.com\u002Fanthropics\u002Fknowledge-work-plugins\u002Fblob\u002Fmain\u002Fsmall-business\u002Fskills\u002Fsmb-router\u002FSKILL.md",[],[228,229,230,154,231,37,161,232,105,21,135,165,233,234,235,29,236,216,237,113],"router","smb","smb router","do","front","you","vague","can","plugin","focus"]