sf-diagram-mermaid▌
jaganpro/sf-skills · updated Apr 8, 2026
MDX-style export adds YAML metadata + attribution linking explainx.ai and this canonical listing URL.
Use this skill when the user needs text-based diagrams: Mermaid diagrams for architecture, OAuth, integration flows, ERDs, or Agentforce structure, plus ASCII fallback when plain-text compatibility matters.
sf-diagram-mermaid: Salesforce Diagram Generation
Use this skill when the user needs text-based diagrams: Mermaid diagrams for architecture, OAuth, integration flows, ERDs, or Agentforce structure, plus ASCII fallback when plain-text compatibility matters.
When This Skill Owns the Task
Use sf-diagram-mermaid when the user wants:
- Mermaid output
- ASCII fallback diagrams
- architecture, sequence, flowchart, or ERD views in markdown-friendly form
- diagrams that can live directly in docs, READMEs, or issues
Delegate elsewhere when the user wants:
- rendered PNG/SVG images or polished mockups → sf-diagram-nanobananapro
- non-Salesforce systems only → use a more general diagramming skill
- object discovery before an ERD → sf-metadata
Supported Diagram Families
| Type | Preferred Mermaid form | Typical use |
|---|---|---|
| OAuth / auth flows | sequenceDiagram |
Authorization Code, JWT, PKCE, Device Flow |
| ERD / data model | flowchart LR |
object relationships and sharing context |
| integration sequence | sequenceDiagram |
request/response or event choreography |
| system landscape | flowchart |
high-level architecture |
| role / access hierarchy | flowchart |
users, profiles, permissions |
| Agentforce behavior map | flowchart |
agent → topic → action relationships |
Required Context to Gather First
Ask for or infer:
- diagram type
- scope and entities / systems involved
- output preference: Mermaid only, ASCII only, or both
- whether styling should be minimal, documentation-first, or presentation-friendly
- for ERDs: whether org metadata is available for grounding
Recommended Workflow
1. Pick the right diagram structure
- use
sequenceDiagramfor time-ordered interactions - use
flowchart LRfor ERDs and capability maps - keep a single primary story per diagram when possible
2. Gather data
For ERDs and grounded diagrams:
- use sf-metadata when real schema discovery is needed
- optionally use the local metadata helper script for counts / relationship context when appropriate
3. Generate Mermaid first
Apply:
- accurate labels
- simple readable node text
- consistent relationship notation
- restrained styling that renders cleanly in markdown viewers
4. Add ASCII fallback when useful
Provide an ASCII version when the user wants terminal compatibility or plaintext documentation.
5. Explain the diagram briefly
Call out the key relationships, flow direction, and any assumptions.
High-Signal Rules
For sequence diagrams
- use
autonumberwhen step order matters - distinguish requests vs responses clearly
- use notes sparingly for protocol detail
For ERDs
- prefer
flowchart LR - keep object cards simple
- use clear relationship arrows
- avoid field overload unless the user explicitly asks for field-level detail
- color-code object types only when it improves readability
For ASCII output
- keep width reasonable
- align arrows and boxes consistently
- optimize for readability over decoration
Output Format
## <Diagram Title>
### Mermaid Diagram
```mermaid
<diagram>
```
### ASCII Fallback
```text
<ascii>
```
### Notes
- <key point>
- <assumption or limitation>
Cross-Skill Integration
| Need | Delegate to | Reason |
|---|---|---|
| real object / field definitions | sf-metadata | grounded ERD generation |
| rendered diagram / image output | sf-diagram-nanobananapro | visual polish beyond Mermaid |
| connected-app auth setup context | sf-connected-apps | accurate OAuth flows |
| Agentforce logic visualization | sf-ai-agentscript | source-of-truth behavior details |
| Flow behavior diagrams | sf-flow | actual Flow logic grounding |
Reference Map
Start here
Styling / ERD specifics
Preview
Score Guide
| Score | Meaning |
|---|---|
| 72–80 | production-ready diagram |
| 60–71 | clear and useful with minor polish left |
| 48–59 | functional but could be clearer |
| 35–47 | needs structural improvement |
| < 35 | inaccurate or incomplete |
How to use sf-diagram-mermaid 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-diagram-mermaid
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches sf-diagram-mermaid 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-diagram-mermaid. Access the skill through slash commands (e.g., /sf-diagram-mermaid) 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
Submit your Claude Code skill and start earning
Use Cases▌
Task Automation & Efficiency
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Knowledge Enhancement
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Quality Improvement
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client with skill support
- ›Clear understanding of task or problem to solve
- ›Willingness to iterate and refine outputs
Time Estimate
15-45 minutes depending on use case complexity
Installation Steps
- 1.Install skill using provided installation command
- 2.Test with simple use case relevant to your work
- 3.Evaluate output quality and relevance
- 4.Iterate on prompts to improve results
- 5.Integrate into regular workflow if valuable
Common Pitfalls
- ⚠Expecting perfect results without iteration
- ⚠Not providing enough context in prompts
- ⚠Using skill for tasks outside its intended scope
- ⚠Accepting outputs without review and validation
Best Practices▌
✓ Do
- +Start with clear, specific prompts
- +Provide relevant context and constraints
- +Review and refine all outputs before using
- +Iterate to improve output quality
- +Document successful prompt patterns
✗ Don't
- −Don't use without understanding skill limitations
- −Don't skip validation of outputs
- −Don't share sensitive information in prompts
- −Don't expect skill to replace human judgment
💡 Pro Tips
- ★Be specific about desired format and style
- ★Ask for multiple options to choose from
- ★Request explanations to understand reasoning
- ★Combine AI efficiency with human expertise
When to Use This▌
✓ Use When
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid When
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
Learning Path▌
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.8★★★★★59 reviews- ★★★★★Charlotte Sanchez· Dec 12, 2024
sf-diagram-mermaid has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Chinedu Farah· Dec 12, 2024
Useful defaults in sf-diagram-mermaid — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Dev Martin· Dec 12, 2024
Solid pick for teams standardizing on skills: sf-diagram-mermaid is focused, and the summary matches what you get after install.
- ★★★★★Li Patel· Dec 8, 2024
sf-diagram-mermaid reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Diya Perez· Dec 8, 2024
sf-diagram-mermaid fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Shikha Mishra· Dec 4, 2024
We added sf-diagram-mermaid from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Emma Menon· Dec 4, 2024
We added sf-diagram-mermaid from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Diya Choi· Nov 27, 2024
We added sf-diagram-mermaid from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Yash Thakker· Nov 23, 2024
sf-diagram-mermaid reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Diya Lopez· Nov 23, 2024
sf-diagram-mermaid reduced setup friction for our internal harness; good balance of opinion and flexibility.
showing 1-10 of 59