workiq-copilot▌
github/awesome-copilot · updated Apr 8, 2026
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Query Microsoft 365 data with natural language to surface emails, meetings, documents, Teams messages, and people insights.
- ›Supports five data sources: emails, meetings, documents, Teams channels, and people/projects with natural-language prompts
- ›Install via Copilot CLI plugin (preferred) or standalone npm package; requires Microsoft 365 tenant admin consent on first use
- ›Core workflow: clarify intent, craft precise prompts with timeframe/source, run workiq ask --question \"...\" , an
WorkIQ Copilot Skill
Overview
WorkIQ (Public Preview) lets Copilot query Microsoft 365 data with natural language. It supports schedules, documents, Teams messages, email threads, follow-up tracking, stakeholder summaries, and more. Use this skill whenever a task needs live organizational intelligence beyond the local repository.
Supported Data & Sample Prompts
- Emails – “Summarize emails from Sarah about the budget.”
- Meetings – “What are my upcoming meetings this week?”
- Documents – “Find recent documents about Q4 planning.”
- Teams – “Summarize messages in the Engineering channel today.”
- People/Projects – “Who is working on Project Alpha?”
Getting Access
- Copilot CLI plugin (preferred)
copilot/plugin marketplace add github/copilot-plugins/plugin install workiq@copilot-plugins- Restart Copilot CLI.
- Standalone CLI / MCP server
npm install -g @microsoft/workiq(ornpx -y @microsoft/workiq mcp).- Run
workiq mcpto expose MCP tools if needed.
- Tenant consent
- First use prompts for Microsoft 365 admin consent (EULA + permissions). Non-admins must contact tenant admin to approve per the Tenant Administrator Enablement Guide.
Pre-flight Checklist
- Run
Get-Command workiqto ensure the binary is available. - Accept the EULA once via
workiq accept-eula. - Confirm the correct tenant (
-t <tenant-id>if different from defaultcommon). - Be ready to complete device login in the browser when prompted.
Core Workflow
- Clarify intent – agenda, action items, document lookup, people search, risk summary, etc.
- Craft precise prompt – include timeframe, source, or topic (e.g., “Summarize Teams posts in #eng for today”).
- Run command –
workiq ask --question "<prompt>"(use-qfor shorthand if desired). - Monitor execution – long answers may stream; wait for the response to finish before issuing additional requests.
- Summarize & redact – highlight insights, note conflicts/tasks, avoid pasting raw links unless required.
- Offer follow-ups – blocking time, drafting notes, deeper queries, etc.
Command Reference
| Command | Purpose |
|---|---|
workiq --help |
Show global options. |
workiq version |
Display installed version. |
workiq accept-eula |
Accept license (first use). |
workiq ask |
Interactive mode. |
workiq ask --question "..." |
Ask a specific question (use -q shorthand if preferred). |
workiq ask -t <tenant> -q "..." |
Target a specific tenant. |
workiq mcp |
Start MCP stdio server (expose WorkIQ tools to other agents). |
Prompt Patterns
- Agenda: “What’s on my calendar tomorrow?”
- Action items: “Summarize follow-ups from today’s customer sync.”
- Documents: “List PowerPoints about Contoso FY26 roadmap.”
- Communications: “What did my manager say about the deadline?”
- Insights: “What blockers came up in the last three meetings?”
- Planning: “Suggest focus blocks for Tuesday afternoon.”
Response Guidelines
- Keep summaries concise (2–3 sentences) calling out load, priorities, blockers, and optional next steps.
- Refer to meetings/documents generically unless the user specifically needs links.
- Mention if WorkIQ can continue (e.g., “WorkIQ can show Thu–Sun if needed”).
- Map WorkIQ’s suggested actions to clear offers (block time, send follow-up, request recording, run deeper query).
Best Practices
- Prefer narrow prompts to reduce noise; run multiple queries if needed.
- Combine outputs logically (agenda + conflicts + action items) before responding.
- Respect privacy: do not expose attendee lists or confidential snippets unless explicitly requested.
- Log which commands were run so future steps can reference them (“Asked WorkIQ for agenda + conflicts”).
- Use MCP mode (
workiq mcp) when another agent/workflow needs direct tool access.
Troubleshooting
- Missing CLI – install via npm or ensure PATH is set; notify user if unavailable.
- Consent/auth errors – re-run command after admin grants permissions or after completing device login.
- Long/incomplete output – rerun with refined scope or ask for specific data slices (per day/project/person).
- Command hanging – cancel the running command in your terminal (for example, with Ctrl+C) or restart the Copilot CLI session, then retry; ensure browser login completed.
Follow-up Actions to Offer
- Block focus/overflow holds at suggested times.
- Draft reschedule/decline messages referencing WorkIQ guidance.
- Request recordings or summaries for overlapping sessions.
- Capture action items into task trackers.
- Run additional WorkIQ queries (by project, stakeholder, time range) for deeper analysis.
How to use workiq-copilot on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add workiq-copilot
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches workiq-copilot from GitHub repository github/awesome-copilot and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate workiq-copilot. Access the skill through slash commands (e.g., /workiq-copilot) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
User Story & Requirements Generation
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Competitive Analysis
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Roadmap Prioritization
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
Make data-driven prioritization decisions faster
Stakeholder Communication
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Installation Steps
- 1.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 7.Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices▌
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This▌
✓ Use When
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid When
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.5★★★★★58 reviews- ★★★★★Advait Chawla· Dec 28, 2024
workiq-copilot reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Sofia Martinez· Dec 24, 2024
Keeps context tight: workiq-copilot is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Zaid Robinson· Dec 20, 2024
Useful defaults in workiq-copilot — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Alexander Verma· Dec 16, 2024
workiq-copilot has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Pratham Ware· Dec 12, 2024
Solid pick for teams standardizing on skills: workiq-copilot is focused, and the summary matches what you get after install.
- ★★★★★Olivia Singh· Dec 8, 2024
workiq-copilot has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Xiao Taylor· Dec 4, 2024
We added workiq-copilot from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Zaid Jackson· Nov 27, 2024
workiq-copilot fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Anaya Li· Nov 23, 2024
Solid pick for teams standardizing on skills: workiq-copilot is focused, and the summary matches what you get after install.
- ★★★★★Chen Nasser· Nov 19, 2024
workiq-copilot is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
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