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sf-debug▌

jaganpro/sf-skills · updated Apr 8, 2026

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

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.

skill.md

sf-debug: Salesforce Debug Log Analysis & Troubleshooting

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.

When This Skill Owns the Task

Use sf-debug when the work involves:

  • .log files from Salesforce
  • stack traces and exception analysis
  • governor limits
  • SOQL / DML / CPU / heap troubleshooting
  • query-plan or performance evidence extracted from logs

Delegate elsewhere when the user is:

Required Context to Gather First

Ask for or infer:

  • org alias
  • failing transaction / user flow / test name
  • approximate timestamp or transaction window
  • user / record / request ID if known
  • whether the goal is diagnosis only or diagnosis + fix loop

Recommended Workflow

1. Retrieve logs

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

2. Analyze in this order

  1. entry point and transaction type
  2. exceptions / fatal errors
  3. governor limits
  4. repeated SOQL / DML patterns
  5. CPU / heap hotspots
  6. callout timing and external failures

3. Classify severity

  • Critical — runtime failure, hard limit, corruption risk
  • Warning — near-limit, non-selective query, slow path
  • Info — optimization opportunity or hygiene issue

4. Recommend the smallest correct fix

Prefer fixes that are:

  • root-cause oriented
  • bulk-safe
  • testable
  • easy to verify with a rerun

Expanded workflow: references/analysis-playbook.md

High-Signal Issue Patterns

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

Output Format

When finishing analysis, report in this order:

  1. What failed
  2. Where it failed (class / method / line / transaction stage)
  3. Why it failed (root cause, not just symptom)
  4. How severe it is
  5. Recommended fix
  6. Verification step

Suggested shape:

Issue: <summary>
Location: <class / line / transaction>
Root cause: <explanation>
Severity: Critical | Warning | Info
Fix: <specific action>
Verify: <test or rerun step>

Cross-Skill Integration

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

Reference Map

Start here

Deep references

Rubric

Score Guide

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
how to use sf-debug

How to use sf-debug 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-debug
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-debug

The skills CLI fetches sf-debug 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-debug

Reload or restart Cursor to activate sf-debug. Access the skill through slash commands (e.g., /sf-debug) 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.

Additional Resources

›View source code on GitHub›Skills CLI documentation›Learn more about Cursor›What are agent skills?

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

4.4★★★★★72 reviews
  • ★★★★★Isabella Haddad· Dec 28, 2024

    sf-debug reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • ★★★★★Min Haddad· Dec 20, 2024

    Solid pick for teams standardizing on skills: sf-debug is focused, and the summary matches what you get after install.

  • ★★★★★Dhruvi Jain· Dec 16, 2024

    sf-debug fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • ★★★★★Mateo Mensah· Dec 16, 2024

    sf-debug is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • ★★★★★Nia White· Dec 12, 2024

    sf-debug is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • ★★★★★Luis Huang· Dec 4, 2024

    sf-debug fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • ★★★★★Anaya Tandon· Dec 4, 2024

    I recommend sf-debug for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • ★★★★★Min Khan· Nov 23, 2024

    Registry listing for sf-debug matched our evaluation — installs cleanly and behaves as described in the markdown.

  • ★★★★★Camila Choi· Nov 23, 2024

    Keeps context tight: sf-debug is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • ★★★★★Nikhil Torres· Nov 19, 2024

    We added sf-debug from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

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