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.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionsf-metadataExecute the skills CLI command in your project's root directory to begin installation:
Fetches sf-metadata from jaganpro/sf-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate sf-metadata. Access via /sf-metadata in your agent's command palette.
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.
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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
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
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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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.
Use sf-metadata when the work involves:
.object-meta.xml, .field-meta.xml, .profile-meta.xml, and related metadata filesDelegate elsewhere when the user is:
Ask for or infer:
Unless the user explicitly opts out, assume new custom objects or fields need permission-set follow-up.
| Mode | Use when |
|---|---|
| generation | the user wants new or updated metadata XML |
| querying | the user needs object / field / metadata discovery |
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.
Check:
When new custom fields or objects are created:
fieldPermissions for eligible custom fieldsUse sf-deploy when the user needs the metadata rolled out.
fieldPermissions for eligible custom fields instead of leaving FLS as a manual afterthoughtWhen finishing, report in this order:
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>
| 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 |
| 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 |
Make data-driven prioritization decisions faster
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
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ 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.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
mattpocock/skills
Useful defaults in sf-metadata — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
sf-metadata has been reliable in day-to-day use. Documentation quality is above average for community skills.
sf-metadata fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Registry listing for sf-metadata matched our evaluation — installs cleanly and behaves as described in the markdown.
Keeps context tight: sf-metadata is the kind of skill you can hand to a new teammate without a long onboarding doc.
We added sf-metadata from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
sf-metadata reduced setup friction for our internal harness; good balance of opinion and flexibility.
Solid pick for teams standardizing on skills: sf-metadata is focused, and the summary matches what you get after install.
sf-metadata is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
We added sf-metadata from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
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