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skill-name▌

resciencelab/opc-skills · updated Apr 8, 2026

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$npx skills add https://github.com/resciencelab/opc-skills --skill skill-name
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summary

Brief description of the skill and its purpose.

skill.md

Skill Name

Brief description of the skill and its purpose.

Prerequisites

List any setup requirements:

  • Environment variables needed
  • API keys required
  • Dependencies (already listed in frontmatter above)

Example setup:

export SKILL_API_KEY="your_api_key"

Quick Start

How to use the skill quickly:

cd <skill_directory>
python3 scripts/command.py --option value

Usage Examples

Example 1: Basic usage

python3 scripts/script.py "input"

Output:

Expected output here

Example 2: Advanced usage

python3 scripts/script.py "input" --flag --option value

Commands

All commands run from the skill directory.

Command 1

python3 scripts/script1.py --help
python3 scripts/script1.py "param1" --option value

Command 2

python3 scripts/script2.py "param1" "param2"

Scripts

  • script1.py - Description of what this script does
  • script2.py - Description of what this script does

API Info

  • Base URL: (if applicable)
  • Rate Limits: (if applicable)
  • Auth: (how authentication works)
  • Docs: Link to official documentation

Troubleshooting

Issue 1

Symptom: Description of the problem

Solution:

  1. Step 1
  2. Step 2

Issue 2

Symptom: Description of the problem

Solution:

  1. Step 1
  2. Step 2

Examples

See examples/ directory for full workflow examples.

References

Notes

  • Important note 1
  • Important note 2

Frontmatter Guide

The YAML frontmatter at the top of this file is required:

Field Type Required Description
name string ✓ Unique identifier (kebab-case)
description string ✓ What the skill does and when to use it. Include trigger keywords and "Use when..." contexts inline.

Creating Your Skill

  1. Copy this template to skills/your-skill-name/
  2. Update the YAML frontmatter
  3. Write your SKILL.md documentation
  4. Add Python/shell scripts in scripts/
  5. Add usage examples in examples/
  6. Update skills.json with your skill entry
  7. Test with your agent before submitting PR
how to use skill-name

How to use skill-name 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 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 skill-name
2

Execute installation command

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

$npx skills add https://github.com/resciencelab/opc-skills --skill skill-name

The skills CLI fetches skill-name from GitHub repository resciencelab/opc-skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/skill-name

Reload or restart Cursor to activate skill-name. Access the skill through slash commands (e.g., /skill-name) 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.

Additional Resources

›View source code on GitHub›Skills CLI documentation›Learn more about Cursor›What are agent skills?

List & Monetize Your Skill

Submit your Claude Code skill and start earning

GET_STARTED →

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

  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

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.7★★★★★34 reviews
  • ★★★★★Omar Nasser· Dec 4, 2024

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

  • ★★★★★Dhruvi Jain· Nov 23, 2024

    We added skill-name from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • ★★★★★Ama Khanna· Nov 23, 2024

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

  • ★★★★★Oshnikdeep· Oct 14, 2024

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

  • ★★★★★Diego Iyer· Oct 14, 2024

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

  • ★★★★★Mia Torres· Sep 25, 2024

    skill-name reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • ★★★★★Aanya Chen· Sep 5, 2024

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

  • ★★★★★Shikha Mishra· Sep 1, 2024

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

  • ★★★★★Hana Liu· Aug 24, 2024

    skill-name reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • ★★★★★Rahul Santra· Aug 20, 2024

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

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