sf-deploy

jaganpro/sf-skills · updated Apr 8, 2026

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

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.

skill.md

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:


Critical Operating Rules

  • Use sf CLI v2 only.
  • On non-source-tracking orgs, deploy/retrieve commands require an explicit scope such as --source-dir, --metadata, or --manifest.
  • Prefer --dry-run first before real deploys.
  • For Flows, deploy safely and activate only after validation.
  • Keep test-data creation guidance delegated to sf-data after 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:

  1. deploy now
  2. assign permission sets
  3. create test data via sf-data
  4. run tests / smoke checks
  5. 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:

  1. authenticate
  2. validate repo / org state
  3. static analysis
  4. dry-run deploy
  5. tests + coverage gates
  6. deploy
  7. verify + notify
  • When org policy and release risk allow it, consider --test-level RunRelevantTests for Apex-heavy deployments.
  • Pair this with modern Apex test annotations such as @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


Agentforce Deployment Note

Use this skill to orchestrate deployment/publish sequencing around agents, but use the agent-specific skills for authoring decisions:

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

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

How to use sf-deploy 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-deploy
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-deploy

The skills CLI fetches sf-deploy 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-deploy

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. 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

4.758 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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