sf-deploy▌
jaganpro/sf-skills · updated Apr 8, 2026
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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.
sf-deploy: Comprehensive Salesforce DevOps Automation
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.
When This Skill Owns the Task
Use sf-deploy when the work involves:
sf project deploy start,quick,report, or retrieval workflows- release sequencing across objects, permission sets, Apex, and Flows
- CI/CD gates, test-level selection, or deployment reports
- troubleshooting deployment failures and dependency ordering
Delegate elsewhere when the user is:
- authoring Apex or LWC code → sf-apex, sf-lwc
- creating metadata definitions → sf-metadata
- building Flows → sf-flow
- doing org data operations → sf-data
- authoring Agent Script logic → sf-ai-agentscript
Critical Operating Rules
- Use
sfCLI v2 only. - On non-source-tracking orgs, deploy/retrieve commands require an explicit scope such as
--source-dir,--metadata, or--manifest. - Prefer
--dry-runfirst before real deploys. - For Flows, deploy safely and activate only after validation.
- Keep test-data creation guidance delegated to
sf-dataafter metadata is validated or deployed.
Default deployment order
| 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.
Required Context to Gather First
Ask for or infer:
- target org alias and environment type
- deployment scope: source-dir, metadata list, or manifest
- whether this is validate-only, deploy, quick deploy, retrieve, or CI/CD guidance
- required test level and rollback expectations
- whether special metadata types are involved (Flow, permission sets, agents, packages)
Preflight checks:
sf --version
sf org list
sf org display --target-org <alias> --json
test -f sfdx-project.json
Recommended Workflow
1. Preflight
Confirm auth, repo shape, package directories, and target scope.
2. Validate first
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.
3. If validation succeeds, offer the next safe workflow
After a successful validation, guide the user to the correct next action:
- deploy now
- assign permission sets
- create test data via sf-data
- run tests / smoke checks
- orchestrate multiple post-deploy steps in order
4. Deploy the smallest correct scope
# 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
5. Verify
sf project deploy report --job-id <job-id> --target-org <alias> --json
Then verify tests, Flow state, permission assignments, and smoke-test behavior.
6. Report clearly
Summarize what deployed, what failed, what was skipped, and what the next safe action is.
Output template: references/deployment-report-template.md
High-Signal Failure Patterns
| 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
CI/CD Guidance
Default pipeline shape:
- authenticate
- validate repo / org state
- static analysis
- dry-run deploy
- tests + coverage gates
- deploy
- verify + notify
- When org policy and release risk allow it, consider
--test-level RunRelevantTestsfor Apex-heavy deployments. - Pair this with modern Apex test annotations such as
@IsTest(testFor=...)and@IsTest(isCritical=true)as documented insf-apex.
Static analysis now uses Code Analyzer v5 (sf code-analyzer), not retired sf scanner.
Deep reference: references/deployment-workflows.md
Agentforce Deployment Note
Use this skill to orchestrate deployment/publish sequencing around agents, but use the agent-specific skills for authoring decisions:
- sf-ai-agentscript for
.agentauthoring and validation - sf-ai-agentforce for Agent Builder / Prompt Builder / metadata config
For full agent DevOps details, including Agent: pseudo metadata, publish/activate, and sync-between-orgs, see:
Cross-Skill Integration
| 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 |
Reference Map
Start here
- references/orchestration.md
- references/deployment-workflows.md
- references/deployment-report-template.md
Specialized deployment safety
Score Guide
| 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 |
Completion Format
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>
How to use sf-deploy on Cursor
AI-first code editor with Composer
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-deploy
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches sf-deploy from GitHub repository jaganpro/sf-skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate sf-deploy. Access the skill through slash commands (e.g., /sf-deploy) 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.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 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▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.7★★★★★58 reviews- ★★★★★Noah Thompson· Dec 28, 2024
Useful defaults in sf-deploy — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Carlos Brown· Dec 28, 2024
Keeps context tight: sf-deploy is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Harper Dixit· Dec 24, 2024
sf-deploy fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Harper Flores· Dec 24, 2024
We added sf-deploy from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Chaitanya Patil· Dec 12, 2024
I recommend sf-deploy for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Harper Desai· Dec 8, 2024
sf-deploy has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Nia Tandon· Dec 8, 2024
Solid pick for teams standardizing on skills: sf-deploy is focused, and the summary matches what you get after install.
- ★★★★★Noah Gill· Nov 27, 2024
I recommend sf-deploy for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Ira Mehta· Nov 19, 2024
We added sf-deploy from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Carlos Tandon· Nov 19, 2024
sf-deploy is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
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