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.
git 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/org-health)<a href="https://agentmods.dev/commands/bhanu91221/sfdx-iq/org-health"><img src="https://agentmods.dev/badge/commands/bhanu91221/sfdx-iq/org-health/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/commands/bhanu91221/sfdx-iq/org-health"><img src="https://agentmods.dev/badge/commands/bhanu91221/sfdx-iq/org-health.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00013 | $0.01165 |
| Opus 5 | $0.00006 | $0.00583 |
| Sonnet 5 | $0.00003 | $0.00233 |
| Haiku 4.5 | $0.00001 | $0.00117 |
Grade A, and why
org-health 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 12d 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 — 110 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/org-health
Run a comprehensive health check on a Salesforce org assessing security settings, governor limits usage, code coverage, unused components, permission complexity, and overall technical debt.
Workflow
-
Validate org connection
- Confirm an org is connected: run
sf org display - If no default org is set, prompt the user to specify one
- Identify org type: production, sandbox, scratch, developer edition
- Record org limits baseline for comparison
- Confirm an org is connected: run
-
Delegate to specialized agents
- metadata-analyst: Scan all metadata for unused components, complexity metrics, and dependency issues
- security-reviewer: Assess security posture across code and configuration
-
Security assessment
- Check
with sharingusage across all Apex classes — flag classes usingwithout sharingor with no sharing declaration - Verify CRUD/FLS enforcement in SOQL queries:
WITH SECURITY_ENFORCEDorSchema.SObjectTypechecks - Scan for SOQL injection vulnerabilities: string concatenation in queries
- Check for hardcoded credentials, API keys, or sensitive data in Apex code
- Review session settings: timeout, IP restrictions, login hours
- Assess password policies if accessible
- Check for overly permissive profiles or permission sets
- Check
-
Governor limits assessment
- Run:
sf org list limits --target-org <org>to get current limits usage - Flag limits approaching thresholds:
- Over 80%: Warning
- Over 90%: Critical
- Key limits to check:
- Data Storage and File Storage usage
- API Request limit (daily)
- Streaming API events
- Custom metadata types count
- Custom objects count
- Identify Apex classes most likely to hit governor limits based on code patterns
- Run:
-
Code coverage analysis
- Run:
sf apex get test --code-coverage --target-org <org>or queryApexCodeCoverageAggregate - Report overall org code coverage percentage
- Identify classes below 75% coverage (deployment risk)
- Identify classes with 0% coverage (no tests at all)
- List classes between 75%-90% (improvement candidates)
- Flag any test classes that are themselves failing
- Run:
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.
- 12d ago First seen · 110 lines · 13 tokens per session scan A dce1ef0022ec
org-health is a command published in the GitHub repository bhanu91221/sfdx-iq (2 stars, last pushed 4mo ago), licensed MIT. It adds 13 tokens to every session and 1,165 once invoked, about $0.0001 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
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.
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.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.