sf-permissions

jaganpro/sf-skills · updated Apr 8, 2026

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

Use this skill when the user needs permission analysis and access auditing: Permission Set / Permission Set Group hierarchy views, “who has access to X?” investigations, user-permission analysis, or permission-set metadata review.

skill.md

sf-permissions

Use this skill when the user needs permission analysis and access auditing: Permission Set / Permission Set Group hierarchy views, “who has access to X?” investigations, user-permission analysis, or permission-set metadata review.

When This Skill Owns the Task

Use sf-permissions when the work involves:

  • permission set / permission set group analysis
  • user access investigation
  • finding which permission grants object / field / Apex / flow / tab / custom-permission access
  • auditing or exporting permission configuration
  • reviewing permission metadata impacts

Delegate elsewhere when the user is:

  • creating new metadata definitions → sf-metadata
  • deploying permission sets → sf-deploy
  • analyzing Apex-managed sharing logic → sf-apex

Required Context to Gather First

Ask for or infer:

  • target org alias
  • whether the question is about an object, field, Apex class, flow, tab, custom permission, or specific user
  • whether the goal is hierarchy visualization, access detection, export, or metadata generation
  • whether the output should be terminal-focused or documentation-friendly

Recommended Workflow

1. Classify the request

Request shape Default capability
“who has access to X?” permission detector
“what does this user have?” user analyzer
“show me the hierarchy” hierarchy viewer
“export this permset” exporter
“generate metadata from analysis” generator or handoff

2. Connect to the correct org

Verify sf auth before running permission analysis.

3. Use the narrowest useful query

Prefer focused analysis over broad org-wide scans unless the user explicitly wants a full audit.

When choosing identifiers, prefer stable metadata names first:

  • PermissionSet.Name
  • PermissionSetGroup.DeveloperName
  • CustomPermission.DeveloperName
  • object and field API names such as Account or Account.AnnualRevenue
  • Assignee.Username / email for user-centric checks

Use Salesforce record IDs only when:

  • the underlying object model requires ParentId or SetupEntityId, or
  • you are drilling into records returned by a prior read-only query in the same investigation

4. Render findings clearly

Use:

  • ASCII tree or table output for terminal work
  • Mermaid only when documentation benefit is clear
  • concise summaries of which permission source grants access

5. Hand off creation or deployment work

Use:


High-Signal Rules

  • distinguish direct Permission Set grants from grants via Permission Set Groups
  • prefer Name / DeveloperName / API names over org-specific record IDs for first-pass investigation queries
  • be explicit about whether access is object-level, field-level, class-level, flow-level, or custom-permission-based
  • use Tooling API where required for setup entities and advanced visibility questions
  • for agent access questions, verify exact agent-name matching in permission metadata
  • when a follow-up child query requires ParentId or SetupEntityId, resolve the ID from a prior result instead of starting with copied IDs

Output Format

When finishing, report in this order:

  1. What was analyzed
  2. Org / subject scope
  3. Which permissions grant access
  4. Whether access is direct or inherited
  5. Recommended follow-up

Suggested shape:

Permission analysis: <hierarchy / detect / user / export>
Scope: <org, user, permission target>
Findings: <permsets / groups / access level>
Source: <direct assignment or via group>
Next step: <export, generate metadata, or deploy changes>

Cross-Skill Integration

Need Delegate to Reason
generate or modify permission metadata sf-metadata metadata authoring
deploy permission changes sf-deploy rollout
identify Apex classes needing grants sf-apex implementation context
bulk user assignment analysis sf-data larger data operations

Reference Map

Start here

Specialized analysis


Score Guide

Score Meaning
90+ strong permission analysis with clear access sourcing
75–89 useful audit with minor gaps
60–74 partial visibility only
< 60 insufficient evidence; expand analysis
how to use sf-permissions

How to use sf-permissions 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-permissions
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-permissions

The skills CLI fetches sf-permissions 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-permissions

Reload or restart Cursor to activate sf-permissions. Access the skill through slash commands (e.g., /sf-permissions) 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)
  • No comments yet — start the thread.
general reviews

Ratings

4.539 reviews
  • Mateo Gonzalez· Dec 28, 2024

    I recommend sf-permissions for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Shikha Mishra· Dec 20, 2024

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

  • Arjun Chawla· Dec 20, 2024

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

  • Camila Khanna· Nov 27, 2024

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

  • Sakshi Patil· Nov 19, 2024

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

  • Noor Li· Nov 19, 2024

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

  • Yash Thakker· Nov 11, 2024

    I recommend sf-permissions for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Ishan Choi· Nov 11, 2024

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

  • Diego Jackson· Oct 18, 2024

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

  • Chaitanya Patil· Oct 10, 2024

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

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