business/enterprise-search/slack-messaging/SKILL.md
Guidance for composing well-formatted, effective Slack messages using mrkdwn syntax
npx skillsauth add harsh040506/claude-code-unified-skill-plugin-library slack-messagingInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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This skill provides guidance for composing well-formatted, effective Slack messages.
Apply this skill whenever composing, drafting, or helping the user write a Slack message — including when using slack_send_message, slack_send_message_draft, or slack_create_canvas.
Slack uses its own markup syntax called mrkdwn, which differs from standard Markdown. Always use mrkdwn when composing Slack messages:
| Format | Syntax | Notes |
|--------|--------|-------|
| Bold | *text* | Single asterisks, NOT double |
| Italic | _text_ | Underscores |
| Strikethrough | ~text~ | Tildes |
| Code (inline) | `code` | Backticks |
| Code block | ```code``` | Triple backticks |
| Quote | > text | Angle bracket |
| Link | <url\|display text> | Pipe-separated in angle brackets |
| User mention | <@U123456> | User ID in angle brackets |
| Channel mention | <#C123456> | Channel ID in angle brackets |
| Bulleted list | - item or • item | Dash or bullet character |
| Numbered list | 1. item | Number followed by period |
**bold** (double asterisks) — Slack uses *bold* (single asterisks)## headers — Slack does not support Markdown headers. Use *bold text* on its own line instead.[text](url) for links — Slack uses <url|text> format--- for horizontal rules — Slack does not render these*bold* for names, dates, deadlines, and action items so they stand out when scanning.reply_broadcast (also post to channel) only when the reply contains information everyone needs to see.#general is usually more formal than #random.testing
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tools
Deep learning for single-cell analysis using scvi-tools. This skill should be used when users need (1) data integration and batch correction with scVI/scANVI, (2) ATAC-seq analysis with PeakVI, (3) CITE-seq multi-modal analysis with totalVI, (4) multiome RNA+ATAC analysis with MultiVI, (5) spatial transcriptomics deconvolution with DestVI, (6) label transfer and reference mapping with scANVI/scArches, (7) RNA velocity with veloVI, or (8) any deep learning-based single-cell method. Triggers include mentions of scVI, scANVI, totalVI, PeakVI, MultiVI, DestVI, veloVI, sysVI, scArches, variational autoencoder, VAE, batch correction, data integration, multi-modal, CITE-seq, multiome, reference mapping, latent space.
testing
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development
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