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 commands/bhanu91221/sfdx-iq/debug-loggit clone --depth 1 https://github.com/bhanu91221/sfdx-iqWrote 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/commands/bhanu91221/sfdx-iq/debug-log)<a href="https://agentmods.dev/commands/bhanu91221/sfdx-iq/debug-log"><img src="https://agentmods.dev/badge/commands/bhanu91221/sfdx-iq/debug-log.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 | $0.00006 | $0.00723 |
| Opus 5 | $0.00003 | $0.00362 |
| Sonnet 5 | $0.00001 | $0.00145 |
| Haiku 4.5 | $0.00001 | $0.00072 |
Grade A, and why
debug-log 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 3d 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 — 89 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/debug-log
Retrieve debug logs from a Salesforce org, parse them for issues, and provide actionable analysis.
Workflow
-
Set up trace flag (if not active)
- Check for active trace flags on the running user:
sf data query --query "SELECT Id, ExpirationDate, DebugLevelId FROM TraceFlag WHERE TracedEntityId = '<userId>' AND ExpirationDate > NOW()" --target-org <org> - If no active trace flag, create one:
sf apex tail log --target-org <org>
- Check for active trace flags on the running user:
-
Retrieve logs
- Get the most recent log:
sf apex get log --number 1 --target-org <org> - Or list available logs and let user choose:
sf apex list log --target-org <org>
- Get the most recent log:
-
Parse the debug log Analyze the log for key sections:
Section What to Look For SOQL queries Count, timing, selectivity warnings DML operations Count, records affected, errors CPU time Total usage vs limit Heap allocation Peak usage vs limit Callouts HTTP status, response time, timeouts Exceptions Stack traces, error messages Flow interviews Flow execution path, element timing -
Governor limit summary Extract limits from the
LIMIT_USAGE_FOR_NSsection:Governor Limit Usage: SOQL Queries: 23/100 (23%) DML Statements: 8/150 (5%) CPU Time: 3,450/10,000 ms (35%) Heap Size: 1.2/6 MB (20%) Callouts: 2/100 (2%) -
Identify issues
- Flag any limit >70% usage as ⚠️ WARNING
- Flag any limit >90% as 🔴 CRITICAL
- Identify SOQL queries that appear in loops (check query count vs unique queries)
- Highlight slow queries (>100ms)
- Flag unhandled exceptions
-
Recommendations Based on analysis, suggest:
- Queries to optimize (slow or redundant)
- DML to consolidate
- Code paths to move to async
- Delegate to
apex-code-revieweragent if limits are concerning
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.
- 3d ago First seen · 89 lines · 6 tokens per session scan A 7ea4e1d1e9bc
debug-log is a command published in the GitHub repository bhanu91221/sfdx-iq (2 stars, last pushed 3mo ago), licensed MIT. It adds 6 tokens to every session and 723 once invoked, about $0.0000 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 2026-08-31.
Other commands, from other repositories
git
Git operations with intelligent commit messages and workflow optimization.
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.