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platform-apex-logs-debug

Salesforce debug log analysis and troubleshooting with 100-point scoring.

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platform-apex-logs-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 platform-apex-logs-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

Use the commands in references/cli-commands.md to list, download, or stream logs for the target org.

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

IssuePrimary signalDefault fix direction
SOQL in looprepeating SOQL_EXECUTE_BEGIN in a repeated call pathquery once, use maps / grouped collections
DML in looprepeated DML_BEGIN patternscollect rows, bulk DML once
Non-selective queryhigh rows scanned / poor selectivityadd indexed filters, reduce scope
CPU pressureCPU usage approaching sync limitreduce algorithmic complexity, cache, async where valid
Heap pressureheap usage approaching sync limitstream with SOQL for-loops, reduce in-memory data
Null pointer / fatal errorEXCEPTION_THROWN / FATAL_ERRORguard 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:

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

Rules / Constraints

RuleRationale
Always base fix recommendations on log evidenceAvoid speculative diagnosis — root cause must be traceable in the log
Report all six output fields for every issue foundEnsures actionable, complete findings for each problem
Classify every finding as Critical, Warning, or InfoHelps the user prioritize which issues to address first
Delegate code generation to platform-apex-generateThis skill diagnoses; it does not rewrite Apex code
Delegate test execution to platform-apex-test-runThis skill does not run or repair test classes
Never assume limits are safe without reading LIMIT_USAGE eventsLimits may be consumed by earlier operations not visible in the failure point

Gotchas

PitfallResolution
Log truncated at 2 MBReduce debug levels (e.g., ApexCode: INFO, ApexProfiling: FINE) and re-capture
Same issue appears as both SOQL and CPU problemFix SOQL-in-loop first — it typically drives the CPU spike as a secondary effect
No logs appear after trace flag is setVerify the trace flag ExpirationDate is in the future and the correct user is traced
Async context changes limit valuesCPU limit is 60,000 ms async vs 10,000 ms sync — check transaction type before flagging limits
Stack trace points to framework line, not user codeWalk up the call stack past trigger handlers to find the originating user code

Cross-Skill Integration

NeedDelegate toReason
Implement Apex fixplatform-apex-generatecode change generation / review
Reproduce via testsplatform-apex-test-runtest execution and coverage loop
Deploy fixplatform-metadata-deploydeployment orchestration
Create debugging dataplatform-data-managetargeted seed / repro data

Reference File Index

FileWhen to read
references/analysis-playbook.mdStart here — expanded step-by-step workflow for any debugging session
references/common-issues.mdQuick lookup for SOQL in loop, DML in loop, CPU/heap pressure, null pointer patterns
references/cli-commands.mdSF CLI commands for retrieving, streaming, and managing debug logs
references/debug-log-reference.mdFull event type catalog, log levels, and governor limit reference values
references/log-analysis-tools.mdTool guide: Apex Log Analyzer, Developer Console, CLI grep patterns
references/benchmarking-guide.mdPerformance benchmarking techniques, benchmark data, and anti-patterns
references/scoring-rubric.md100-point scoring rubric for evaluating analysis quality
assets/benchmarking-template.clsCopy-paste Anonymous Apex template for running performance benchmarks
assets/cpu-heap-optimization.clsApex patterns for reducing CPU time and heap allocation
assets/dml-in-loop-fix.clsBefore/after example for resolving DML-in-loop violations
assets/soql-in-loop-fix.clsBefore/after example for resolving SOQL-in-loop violations
assets/null-pointer-fix.clsPatterns for guarding against null pointer exceptions

Score Guide

ScoreMeaning
90+Expert analysis with strong fix guidance
80–89Good analysis with minor gaps
70–79Acceptable but may miss secondary issues
60–69Partial diagnosis only
< 60Incomplete analysis
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