sf-metadata

jaganpro/sf-skills · updated Apr 8, 2026

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$npx skills add https://github.com/jaganpro/sf-skills --skill sf-metadata
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summary

Use this skill when the user needs metadata definition or org metadata discovery: custom objects, fields, validation rules, record types, page layouts, permission sets, or schema inspection with sf CLI.

skill.md

sf-metadata: Salesforce Metadata Generation and Org Querying

Use this skill when the user needs metadata definition or org metadata discovery: custom objects, fields, validation rules, record types, page layouts, permission sets, or schema inspection with sf CLI.

When This Skill Owns the Task

Use sf-metadata when the work involves:

  • object, field, validation rule, record type, layout, profile, or permission-set metadata
  • .object-meta.xml, .field-meta.xml, .profile-meta.xml, and related metadata files
  • describing schema before coding or Flow work
  • generating metadata XML from requirements

Delegate elsewhere when the user is:


Required Context to Gather First

Ask for or infer:

  • whether the user wants generation or querying
  • metadata type(s) involved
  • target object / field / package directory
  • target org alias if querying is required
  • whether new custom objects or fields should also include permission-set / FLS generation

Unless the user explicitly opts out, assume new custom objects or fields need permission-set follow-up.


Recommended Workflow

1. Choose the mode

Mode Use when
generation the user wants new or updated metadata XML
querying the user needs object / field / metadata discovery

2. Start from templates or CLI describe data

For generation, use the assets under:

  • assets/objects/
  • assets/fields/
  • assets/permission-sets/
  • assets/profiles/
  • assets/record-types/
  • assets/validation-rules/
  • assets/layouts/

For querying, prefer sf metadata and sobject describe commands.

3. Validate metadata quality

Check:

  • naming conventions
  • structural correctness
  • field-type fit
  • security / FLS implications
  • downstream deployment dependencies

4. Plan permission impact by default

When new custom fields or objects are created:

  • default to generating or updating a Permission Set unless the user opts out
  • include fieldPermissions for eligible custom fields
  • note any metadata categories that are excluded because Salesforce treats them as system-managed or always-available
  • remember that object CRUD alone does not make custom fields visible

5. Hand off deployment

Use sf-deploy when the user needs the metadata rolled out.


High-Signal Rules

  • field-level security is often the hidden blocker after deployment
  • object permissions ≠ field permissions
  • prefer permission sets over profile-centric access patterns
  • generate Permission Set follow-up by default for new custom objects and fields
  • include fieldPermissions for eligible custom fields instead of leaving FLS as a manual afterthought
  • avoid hardcoded IDs in formulas or metadata logic
  • validation rules should have intentional bypass strategy when operationally necessary
  • create metadata before attempting Flow or data tasks that depend on it

Output Format

When finishing, report in this order:

  1. Metadata created or queried
  2. Files created or updated
  3. Key schema/security decisions
  4. Permission / layout follow-ups
  5. Deploy next step

Suggested shape:

Metadata task: <generate / query>
Items: <objects, fields, rules, layouts, permsets>
Files: <paths>
Notes: <naming, field types, security, dependencies>
Next step: <deploy, assign permset, or verify in Setup>

Cross-Skill Integration

Need Delegate to Reason
deploy metadata sf-deploy rollout and validation
build Flows on new schema sf-flow declarative automation
build Apex on new schema sf-apex code against metadata
analyze permission access after creation sf-permissions access auditing
seed data after deploy sf-data test data creation

Reference Map

Start here

Security / scoring / examples


Score Guide

Score Meaning
108+ strong production-ready metadata
96–107 good metadata with minor review items
84–95 acceptable but validate carefully
< 84 block deployment until corrected
how to use sf-metadata

How to use sf-metadata 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 sf-metadata
2

Execute installation command

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

$npx skills add https://github.com/jaganpro/sf-skills --skill sf-metadata

The skills CLI fetches sf-metadata from GitHub repository jaganpro/sf-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/sf-metadata

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

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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

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)
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general reviews

Ratings

4.552 reviews
  • Hana Shah· Dec 24, 2024

    Useful defaults in sf-metadata — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Shikha Mishra· Dec 8, 2024

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

  • Advait Gupta· Dec 8, 2024

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

  • Liam Martinez· Dec 4, 2024

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

  • Yash Thakker· Nov 27, 2024

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

  • Liam Gill· Nov 27, 2024

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

  • Liam Ghosh· Nov 23, 2024

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

  • Sakshi Patil· Nov 19, 2024

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

  • Advait Ghosh· Nov 15, 2024

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

  • Dhruvi Jain· Oct 18, 2024

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

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