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/explaingit 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/explain)<a href="https://agentmods.dev/commands/bhanu91221/sfdx-iq/explain"><img src="https://agentmods.dev/badge/commands/bhanu91221/sfdx-iq/explain.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 | $0.00027 | $0.01249 |
| Opus 5 | $0.00014 | $0.00624 |
| Sonnet 5 | $0.00005 | $0.00250 |
| Haiku 4.5 | $0.00003 | $0.00125 |
Grade A, and why
explain 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 4d 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 — 142 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/explain
Produce a human-readable explanation of any Salesforce code artifact. Understands what the code does end-to-end: data sources, user interactions, business logic, side effects, and integration points.
Usage
/explain Explain the active/specified file
/explain --apex <file or name> Explain an Apex class or trigger
/explain --lwc <component name> Explain an LWC component
/explain --flow <flow name> Explain a Salesforce Flow
/explain --deep Deep behavioral analysis: trace fields, cross-file data flow
Use Cases
- "What does this Apex class do?"
- "Explain this LWC component to me"
- "Walk me through this Flow step by step"
- "What happens when this trigger fires?"
- "Trace how this field gets set — from the UI all the way to the database"
- "What code touches the Opportunity when it closes?"
Workflow
Step 1: Identify What to Explain
- If
--apex <path or name>, searchforce-app/**/classes/<name>.clsor**/triggers/<name>.trigger - If
--lwc <name>, searchforce-app/**/lwc/<name>/— read.js,.html,.csstogether - If
--flow <name>, searchforce-app/**/flows/<name>.flow-meta.xml - If no flag, detect the file from context (path provided in the message)
- If nothing provided, ask: "Which file would you like me to explain? You can provide a path, a class name, or a component name."
Step 2: Read All Related Files
For Apex classes: read the class + related selector classes referenced by name.
For triggers: read the trigger + its handler class + handler's dependencies.
For LWC: read all files in the component directory (.js, .html, .css).
For Flows: read the .flow-meta.xml fully.
Step 3: Apply Domain-Specific Explanation Strategy
For Apex Classes (.cls):
- Summarize the class role (Service, Selector, Controller, Batch, Utility, etc.)
- Explain each public/global method: inputs → logic → outputs
- Identify SOQL queries: what object, what filters, what's returned
- Identify DML operations: what records are affected
- Note external callouts, platform events, or async jobs triggered
- Highlight security:
with sharingusage, CRUD/FLS enforcement
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.
- 4d ago First seen · 142 lines · 27 tokens per session scan A 64d39d58ce45
explain is a command published in the GitHub repository bhanu91221/sfdx-iq (2 stars, last pushed 3mo ago), licensed MIT. It adds 27 tokens to every session and 1,249 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
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.