Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/jaganpro/sf-skills/sf-debugnpx skills add Jaganpro/sf-skills --skill sf-debuggit clone --depth 1 https://github.com/Jaganpro/sf-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/jaganpro/sf-skills/sf-debug)<a href="https://agentmods.dev/skills/jaganpro/sf-skills/sf-debug"><img src="https://agentmods.dev/badge/skills/jaganpro/sf-skills/sf-debug.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00081 | $0.01232 |
| Opus 5 | $0.00041 | $0.00616 |
| Sonnet 5 | $0.00016 | $0.00246 |
| Haiku 4.5 | $0.00008 | $0.00123 |
Grade A, and why
sf-debug scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 156 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
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
- 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
High-Signal Issue Patterns
What ships with it
14 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- assets/benchmarking-template.cls 15 KB
- assets/cpu-heap-optimization.cls 12 KB
- assets/dml-in-loop-fix.cls 8.6 KB
- assets/null-pointer-fix.cls 10 KB
- assets/soql-in-loop-fix.cls 6.3 KB
- CREDITS.md 3.9 KB
- README.md 2.3 KB
- references/analysis-playbook.md 1.3 KB
- references/benchmarking-guide.md 7.9 KB
- references/cli-commands.md 8.8 KB
- references/common-issues.md 1.2 KB
- references/debug-log-reference.md 8.4 KB
- references/log-analysis-tools.md 7.1 KB
- references/scoring-rubric.md 821 B
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 6d ago First seen · 156 lines · 81 tokens per session scan A 6be01c44b0b8
sf-debug is a skill published in the GitHub repository Jaganpro/sf-skills (423 stars, last pushed 4mo ago), licensed MIT. It adds 81 tokens to every session and 1,232 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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