Use this skill when the user needs root-cause analysis from debug logs: governor-limit diagnosis, stack-trace interpretation, slow-query investigation, heap / CPU pressure analysis, or a reproduction-to-fix loop based on log evidence.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionsf-debugExecute the skills CLI command in your project's root directory to begin installation:
Fetches sf-debug 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-debug. Access via /sf-debug 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 root-cause analysis from debug logs: governor-limit diagnosis, stack-trace interpretation, slow-query investigation, heap / CPU pressure analysis, or a reproduction-to-fix loop based on log evidence.
Use sf-debug when the work involves:
.log files from SalesforceDelegate elsewhere when the user is:
Ask for or infer:
sf apex list log --target-org <alias> --json
sf apex get log --log-id <id> --target-org <alias>
sf apex tail log --target-org <alias> --color
Prefer fixes that are:
Expanded workflow: references/analysis-playbook.md
| Issue | Primary signal | Default fix direction |
|---|---|---|
| SOQL in loop | repeating SOQL_EXECUTE_BEGIN in a repeated call path |
query once, use maps / grouped collections |
| DML in loop | repeated DML_BEGIN patterns |
collect rows, bulk DML once |
| Non-selective query | high rows scanned / poor selectivity | add indexed filters, reduce scope |
| CPU pressure | CPU usage approaching sync limit | reduce algorithmic complexity, cache, async where valid |
| Heap pressure | heap usage approaching sync limit | stream with SOQL for-loops, reduce in-memory data |
| Null pointer / fatal error | EXCEPTION_THROWN / FATAL_ERROR |
guard null assumptions, fix empty-query handling |
Expanded examples: references/common-issues.md
When finishing analysis, report in this order:
Suggested shape:
Issue: <summary>
Location: <class / line / transaction>
Root cause: <explanation>
Severity: Critical | Warning | Info
Fix: <specific action>
Verify: <test or rerun step>
| Need | Delegate to | Reason |
|---|---|---|
| Implement Apex fix | sf-apex | code change generation / review |
| Reproduce via tests | sf-testing | test execution and coverage loop |
| Deploy fix | sf-deploy | deployment orchestration |
| Create debugging data | sf-data | targeted seed / repro data |
| Score | Meaning |
|---|---|
| 90+ | Expert analysis with strong fix guidance |
| 80–89 | Good analysis with minor gaps |
| 70–79 | Acceptable but may miss secondary issues |
| 60–69 | Partial diagnosis only |
| < 60 | Incomplete analysis |
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
sf-debug reduced setup friction for our internal harness; good balance of opinion and flexibility.
Solid pick for teams standardizing on skills: sf-debug is focused, and the summary matches what you get after install.
sf-debug fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
sf-debug is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
sf-debug is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
sf-debug fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
I recommend sf-debug for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Registry listing for sf-debug matched our evaluation — installs cleanly and behaves as described in the markdown.
Keeps context tight: sf-debug is the kind of skill you can hand to a new teammate without a long onboarding doc.
We added sf-debug from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
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