Use this skill when the user needs deployment orchestration: dry-run validation, targeted or manifest-based deploys, CI/CD workflow advice, scratch-org management, failure triage, or safe rollout sequencing for Salesforce metadata.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionsf-deployExecute the skills CLI command in your project's root directory to begin installation:
Fetches sf-deploy 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-deploy. Access via /sf-deploy 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 deployment orchestration: dry-run validation, targeted or manifest-based deploys, CI/CD workflow advice, scratch-org management, failure triage, or safe rollout sequencing for Salesforce metadata.
Use sf-deploy when the work involves:
sf project deploy start, quick, report, or retrieval workflowsDelegate elsewhere when the user is:
sf CLI v2 only.--source-dir, --metadata, or --manifest.--dry-run first before real deploys.sf-data after metadata is validated or deployed.| Phase | Metadata |
|---|---|
| 1 | Custom objects / fields |
| 2 | Permission sets |
| 3 | Apex |
| 4 | Flows as Draft |
| 5 | Flow activation / post-verify |
This ordering prevents many dependency and FLS failures.
Ask for or infer:
Preflight checks:
sf --version
sf org list
sf org display --target-org <alias> --json
test -f sfdx-project.json
Confirm auth, repo shape, package directories, and target scope.
sf project deploy start --dry-run --source-dir force-app --target-org <alias> --wait 30 --json
Use manifest- or metadata-scoped validation when the change set is targeted.
After a successful validation, guide the user to the correct next action:
# source-dir deploy
sf project deploy start --source-dir force-app --target-org <alias> --wait 30 --json
# manifest deploy
sf project deploy start --manifest manifest/package.xml --target-org <alias> --test-level RunLocalTests --wait 30 --json
# manifest deploy with Spring '26 relevant-test selection
sf project deploy start --manifest manifest/package.xml --target-org <alias> --test-level RunRelevantTests --wait 30 --json
# quick deploy after successful validation
sf project deploy quick --job-id <validation-job-id> --target-org <alias> --json
sf project deploy report --job-id <job-id> --target-org <alias> --json
Then verify tests, Flow state, permission assignments, and smoke-test behavior.
Summarize what deployed, what failed, what was skipped, and what the next safe action is.
Output template: references/deployment-report-template.md
| Error / symptom | Likely cause | Default fix direction |
|---|---|---|
FIELD_CUSTOM_VALIDATION_EXCEPTION |
validation rule or bad test data | adjust data or rule timing |
INVALID_CROSS_REFERENCE_KEY |
missing dependency | include referenced metadata first |
CANNOT_INSERT_UPDATE_ACTIVATE_ENTITY |
trigger / Flow / validation side effect | inspect automation stack and failing logic |
| tests fail during deploy | broken code or fragile tests | run targeted tests, fix root cause, revalidate |
| field/object not found in permset | wrong order | deploy objects/fields before permission sets |
| Flow invalid / version conflict | dependency or activation problem | deploy as Draft, verify, then activate |
Full workflows: references/orchestration.md, references/trigger-deployment-safety.md
Default pipeline shape:
--test-level RunRelevantTests for Apex-heavy deployments.@IsTest(testFor=...) and @IsTest(isCritical=true) as documented in sf-apex.Static analysis now uses Code Analyzer v5 (sf code-analyzer), not retired sf scanner.
Deep reference: references/deployment-workflows.md
Use this skill to orchestrate deployment/publish sequencing around agents, but use the agent-specific skills for authoring decisions:
.agent authoring and validationFor full agent DevOps details, including Agent: pseudo metadata, publish/activate, and sync-between-orgs, see:
| Need | Delegate to | Reason |
|---|---|---|
| custom object / field creation | sf-metadata | define metadata before deploy |
| Apex compile / review / fixes | sf-apex | code authoring and repair |
| Flow creation / repair | sf-flow | Flow authoring and activation guidance |
| test data or seed records | sf-data | describe-first data setup and cleanup |
| Agent Script build/publish readiness | sf-ai-agentscript | agent-specific correctness |
| Score | Meaning |
|---|---|
| 90+ | strong deployment plan and execution guidance |
| 75–89 | good deploy guidance with minor review items |
| 60–74 | partial coverage of deployment risk |
| < 60 | insufficient confidence; tighten plan before rollout |
Deployment goal: <validate / deploy / retrieve / pipeline>
Target org: <alias>
Scope: <source-dir / metadata / manifest>
Result: <passed / failed / partial>
Key findings: <errors, ordering, tests, skipped items>
Next step: <safe follow-up action>
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
ailabs-393/ai-labs-claude-skills
Useful defaults in sf-deploy — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Keeps context tight: sf-deploy is the kind of skill you can hand to a new teammate without a long onboarding doc.
sf-deploy fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
We added sf-deploy from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
I recommend sf-deploy for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
sf-deploy has been reliable in day-to-day use. Documentation quality is above average for community skills.
Solid pick for teams standardizing on skills: sf-deploy is focused, and the summary matches what you get after install.
I recommend sf-deploy for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
We added sf-deploy from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
sf-deploy is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
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