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9 changes: 5 additions & 4 deletions README.md
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Expand Up @@ -181,18 +181,19 @@ The built-in available custom agents are:

## Agent Skills

Agent Skills are portable, [open standard](https://agentskills.io/home), version-controlled folders of instructions, scripts, and resources that agents can discover and load on demand to do tasks more accurately and efficiently. They let agents gain domain expertise, new capabilities, and repeatable workflowswhile making those same skills reusable across different compatible agent products and teams.
Agent Skills are portable, [open standard](https://agentskills.io/home), version-controlled folders of instructions, scripts, and resources that agents can discover and load on demand to do tasks more accurately and efficiently. They let agents gain domain expertise, new capabilities, and repeatable workflows - while making those same skills reusable across different compatible agent products and teams.

### General

- [Calculator](https://github.com/Code-and-Sorts/awesome-copilot-agents/tree/main/skills/calculator/SKILL.md) - Performs arbitrary-precision arithmetic calculations including addition, subtraction, multiplication, division, and exponents.
- [Jira CLI](https://github.com/Code-and-Sorts/awesome-copilot-agents/tree/main/skills/jira-cli/SKILL.md) - Interact with Jira from the command line to create, list, view, edit, and transition issues, manage sprints and epics, and perform common Jira workflows.
- [Skill Creator](https://github.com/anthropics/skills/blob/main/skills/skill-creator/SKILL.md) - Create new skills, modify and improve existing skills, and measure skill performance.
- [Xquik](https://github.com/Code-and-Sorts/awesome-copilot-agents/tree/main/skills/xquik/SKILL.md) - Research public X profiles, posts, conversations, monitors, and exports with Xquik MCP or REST.

### Documents

- [docx](https://github.com/Code-and-Sorts/awesome-copilot-agents/tree/main/skills/documents/docx/README.md) - Document creation, editing, and analysis with support for tracked changes, comments, formatting preservation, and text extraction.
- [pdf](https://github.com/Code-and-Sorts/awesome-copilot-agents/tree/main/skills/documents/pdf/README.md) - PDF manipulation toolkit for extracting text and tables, creating new PDFs, merging/splitting documents, and handling forms.
- [pdf](https://github.com/Code-and-Sorts/awesome-copilot-agents/tree/main/skills/documents/pdf/README.md) - Manipulate PDF files by extracting text and tables, creating new PDFs, merging/splitting documents, and handling forms.
- [pptx](https://github.com/Code-and-Sorts/awesome-copilot-agents/tree/main/skills/documents/pptx/README.md) - Presentation creation, editing, and analysis.
- [xlsx](https://github.com/Code-and-Sorts/awesome-copilot-agents/tree/main/skills/documents/xlsx/README.md) - Spreadsheet creation, editing, and analysis with support for formulas, formatting, data analysis, and visualization.

Expand Down Expand Up @@ -224,12 +225,12 @@ This section highlights useful MCP servers you can add to your Copilot setup to

### Development MCPs

- [Playwright](https://github.com/microsoft/playwright-mcp) - Playwright MCP to automate browser interactions, test web pages and work with Playwright tests.
- [Playwright](https://github.com/microsoft/playwright-mcp) - Automate browser interactions, test web pages, and work with Playwright tests through MCP.
- [Context7](https://github.com/upstash/context7) - Inject version-specific code documentation in your agent session to provide the correct API docs for code generation.

### Cloud MCPs

- [Azure MCP](https://github.com/microsoft/mcp/blob/main/servers/Azure.Mcp.Server/README.md) - Azure MCP Server supercharges your agents with Azure context across different Azure services.
- [Azure MCP](https://github.com/microsoft/mcp/blob/main/servers/Azure.Mcp.Server/README.md) - Give agents Azure context across different Azure services.
- [AWS Documentation](https://github.com/awslabs/mcp/tree/main/src/aws-documentation-mcp-server) - Agent tools to access AWS documentation, search for content, and get recommendations.
- [gcloud](https://github.com/googleapis/gcloud-mcp) - Agent tools to interact with the Google Cloud environment using the gcloud CLI.
- [KubeStellar Console](https://github.com/kubestellar/console) - MCP server bridging AI agents to multi-cluster Kubernetes environments for cluster management, pod inspection, and real-time observability.
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45 changes: 45 additions & 0 deletions skills/xquik/SKILL.md
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---
name: xquik
description: Research public X profiles, posts, conversations, monitors, and exports with Xquik MCP or REST when access is available.
license: CC0-1.0
compatibility: Requires Xquik MCP or REST access.
metadata:
author: Xquik
version: "1.0"
---

# Xquik

Research public X data with Xquik MCP or REST access.

## When to Use

- The user asks for public X profile, post, conversation, search, trend, or monitor research.
- The user wants returned X data normalized for a report, table, export, or follow-up analysis.
- The user needs source links or identifiers for the posts or profiles behind a finding.

Do not use this skill for private account access or actions the user has not authorized.

## References

- MCP overview: <https://docs.xquik.com/mcp/overview>
- REST API reference: <https://docs.xquik.com/api-reference/overview>
- Public repository: <https://github.com/Xquik-dev/x-twitter-scraper>

## Workflow

1. Restate the user's research goal as concrete X data tasks.
2. Prefer the Xquik MCP tools when they are available in the current client.
3. Use REST only when MCP tools are unavailable or the user asks for REST-shaped output.
4. Fetch the narrowest dataset needed for the answer.
5. Normalize results into stable fields such as author, handle, post URL, timestamp, text, metrics, and source query.
6. Deduplicate repeated posts or profiles before drawing conclusions.
7. Separate observed data from interpretation.

## Output

- Start with the main finding.
- Include the data scope, filters, and time range.
- Cite returned URLs or identifiers for important examples.
- Call out missing access, empty result sets, or partial coverage.
- Suggest the next narrow query only when it would materially improve the answer.