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:
.logfiles 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:
- running or repairing Apex tests → sf-testing
- implementing the code fix → sf-apex
- debugging Agentforce session traces / parquet telemetry → sf-ai-agentforce-observability
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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
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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> --color2. Analyze in this order
- entry point and transaction type
- exceptions / fatal errors
- governor limits
- repeated SOQL / DML patterns
- CPU / heap hotspots
- 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
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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
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Output Format
When finishing analysis, report in this order:
- What failed
- Where it failed (class / method / line / transaction stage)
- Why it failed (root cause, not just symptom)
- How severe it is
- Recommended fix
- 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 |
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Reference Map
Start here
- references/analysis-playbook.md
- references/common-issues.md
- references/cli-commands.md
Deep references
- references/debug-log-reference.md
- references/log-analysis-tools.md
- references/benchmarking-guide.md
Rubric
- references/scoring-rubric.md
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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 |
