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:

• running or repairing Apex tests → platform-apex-test-run
• generating or implementing the code fix → platform-apex-generate
• debugging Agentforce session traces / parquet telemetry → agentforce-observe

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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

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

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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:

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>

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Rules / Constraints

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

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Gotchas

Pitfall	Resolution
Log truncated at 2 MB	Reduce debug levels (e.g., ApexCode: INFO, ApexProfiling: FINE) and re-capture
Same issue appears as both SOQL and CPU problem	Fix SOQL-in-loop first — it typically drives the CPU spike as a secondary effect
No logs appear after trace flag is set	Verify the trace flag ExpirationDate is in the future and the correct user is traced
Async context changes limit values	CPU 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 code	Walk up the call stack past trigger handlers to find the originating user code

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Cross-Skill Integration

Need	Delegate to	Reason
Implement Apex fix	platform-apex-generate	code change generation / review
Reproduce via tests	platform-apex-test-run	test execution and coverage loop
Deploy fix	platform-metadata-deploy	deployment orchestration
Create debugging data	platform-data-manage	targeted seed / repro data

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Reference File Index

File	When to read
references/analysis-playbook.md	Start here — expanded step-by-step workflow for any debugging session
references/common-issues.md	Quick lookup for SOQL in loop, DML in loop, CPU/heap pressure, null pointer patterns
references/cli-commands.md	SF CLI commands for retrieving, streaming, and managing debug logs
references/debug-log-reference.md	Full event type catalog, log levels, and governor limit reference values
references/log-analysis-tools.md	Tool guide: Apex Log Analyzer, Developer Console, CLI grep patterns
references/benchmarking-guide.md	Performance benchmarking techniques, benchmark data, and anti-patterns
references/scoring-rubric.md	100-point scoring rubric for evaluating analysis quality
assets/benchmarking-template.cls	Copy-paste Anonymous Apex template for running performance benchmarks
assets/cpu-heap-optimization.cls	Apex patterns for reducing CPU time and heap allocation
assets/dml-in-loop-fix.cls	Before/after example for resolving DML-in-loop violations
assets/soql-in-loop-fix.cls	Before/after example for resolving SOQL-in-loop violations
assets/null-pointer-fix.cls	Patterns for guarding against null pointer exceptions

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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