draft-offer

If you see unfamiliar placeholders or need to check which tools are connected, see CONNECTORS.md.

Works with

Claude CodeCursorClineWindsurfCodexGooseGitHub CopilotZed

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Install Skill

Run in your terminal

$npx skills add https://github.com/anthropics/knowledge-work-plugins --skill draft-offer

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this week

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Installation Guide

How to use draft-offer on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your machine
  • Node.js 16+ with npm — verify with node --version
  • Active project directory where you want to add draft-offer
2

Run the install command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/anthropics/knowledge-work-plugins --skill draft-offer

Fetches draft-offer from anthropics/knowledge-work-plugins and configures it for Cursor.

3

Select Cursor when prompted

The CLI shows a list of agents. Use arrow keys and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ────────────────
│ · Cline · Codex · Goose · Windsurf
│ ●Cursor(selected)
│ · Cursor · Aider · Continue
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/draft-offer

Restart Cursor to activate draft-offer. Access via /draft-offer in your agent's command palette.

Security 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 environment. Always review source, verify the publisher, and test in isolation before production.

Documentation

/draft-offer

If you see unfamiliar placeholders or need to check which tools are connected, see CONNECTORS.md.

Draft a complete offer letter for a new hire.

Usage

/draft-offer $ARGUMENTS

What I Need From You

  • Role and title: What position?
  • Level: Junior, Mid, Senior, Staff, etc.
  • Location: Where will they be based? (affects comp and benefits)
  • Compensation: Base salary, equity, signing bonus (if applicable)
  • Start date: When should they start?
  • Hiring manager: Who will they report to?

If you don't have all details, I'll help you think through them.

Output

## Offer Letter Draft: [Role] — [Level]

### Compensation Package
| Component | Details |
|-----------|---------|
| **Base Salary** | $[X]/year |
| **Equity** | [X shares/units], [vesting schedule] |
| **Signing Bonus** | $[X] (if applicable) |
| **Target Bonus** | [X]% of base (if applicable) |
| **Total First-Year Comp** | $[X] |

### Terms
- **Start Date**: [Date]
- **Reports To**: [Manager]
- **Location**: [Office / Remote / Hybrid]
- **Employment Type**: [Full-time, Exempt]

### Benefits Summary
[Key benefits highlights relevant to the candidate]

### Offer Letter Text

Dear [Candidate Name],

We are pleased to offer you the position of [Title] at [Company]...

[Complete offer letter text]

### Notes for Hiring Manager
- [Negotiation guidance if needed]
- [Comp band context]
- [Any flags or considerations]

If Connectors Available

If ~~HRIS is connected:

  • Pull comp band data for the level/role
  • Verify headcount approval
  • Auto-populate benefits details

If ~~ATS is connected:

  • Pull candidate details from the application
  • Update offer status in the pipeline

Tips

  1. Include total comp — Candidates compare total compensation, not just base.
  2. Be specific about equity — Share count, current valuation method, vesting schedule.
  3. Personalize — Reference something from the interview process to make it warm.

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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

Steps

  1. 1Install product management skill
  2. 2Start with user story generation for known feature
  3. 3Progress to competitive analysis: research 2-3 competitors
  4. 4Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5Draft stakeholder communications and refine based on feedback
  6. 6Build template library for recurring PM tasks
  7. 7Share 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

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Related Skills

Reviews

4.649 reviews
  • M
    Maya DesaiDec 28, 2024

    draft-offer has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • A
    Anaya MartinezDec 24, 2024

    draft-offer is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • G
    Ganesh MohaneDec 20, 2024

    Registry listing for draft-offer matched our evaluation — installs cleanly and behaves as described in the markdown.

  • S
    Sophia AgarwalDec 20, 2024

    draft-offer fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • I
    Isabella AgarwalDec 8, 2024

    Keeps context tight: draft-offer is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • A
    Anika SethiDec 8, 2024

    Registry listing for draft-offer matched our evaluation — installs cleanly and behaves as described in the markdown.

  • D
    Dev KhannaNov 27, 2024

    draft-offer is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • A
    Arya RaoNov 27, 2024

    Solid pick for teams standardizing on skills: draft-offer is focused, and the summary matches what you get after install.

  • A
    Anaya HarrisNov 15, 2024

    Keeps context tight: draft-offer is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • R
    Rahul SantraNov 11, 2024

    Solid pick for teams standardizing on skills: draft-offer is focused, and the summary matches what you get after install.

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