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/BanibrataChatterjee/AwesomeSalesforceSkillsWrote 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/banibratachatterjee/awesomesalesforceskills/build-agentforce-action)<a href="https://agentmods.dev/commands/banibratachatterjee/awesomesalesforceskills/build-agentforce-action"><img src="https://agentmods.dev/badge/commands/banibratachatterjee/awesomesalesforceskills/build-agentforce-action/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/banibratachatterjee/awesomesalesforceskills/build-agentforce-action"><img src="https://agentmods.dev/badge/commands/banibratachatterjee/awesomesalesforceskills/build-agentforce-action.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 | $0.00000 | $0.00575 |
| Opus 5 | $0.00000 | $0.00287 |
| Sonnet 5 | $0.00000 | $0.00115 |
| Haiku 4.5 | $0.00000 | $0.00057 |
Grade A, and why
build-agentforce-action 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 10d 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 — 80 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/build-agentforce-action — Scaffold a complete Agentforce action
Wraps agents/agentforce-builder/AGENT.md. Produces Apex @InvocableMethod + topic YAML + agent definition + test class + starter eval.
Step 1 — Collect inputs (ask all five upfront)
Ask:
1. Action name (user-facing label)?
Example: "Summarize Account Cases"
2. Primary sObject the action grounds on?
Example: Account
3. Actor invoking the action?
Example: Service Agent / Sales Rep / Customer
4. Intent (what should the action do)?
One or two sentences. What data does it retrieve / what does it change?
5. Trust constraints?
Pick any that apply: no-pii-in-prompt, mask-email, no-external-callout,
require-user-confirmation, audit-every-invocation, rate-limit-per-actor.
Add free-form constraints as needed.
If any of the five is missing, STOP and ask.
Step 2 — Load the agent
Read agents/agentforce-builder/AGENT.md fully + the Agentforce skills + evals/framework.md + the templates under templates/agentforce/.
Step 3 — Execute
Follow the 6-step plan:
- Classify the action (read-only / write / composite / callout)
- Generate the Apex action class (subclass
AgentActionSkeleton, CRUD/FLS viaSecurityUtils, logging viaApplicationLogger) - Generate the topic YAML (classifier prompt, scope boundary, grounding sources, confirmation flag)
- Generate the agent definition JSON
- Generate the test class (including bulk + runAs + wrong-actor tests)
- Generate the starter golden eval
Step 4 — Deliver
- Action summary
- Generated files, each as a fenced block labelled with its target path:
- Apex action class + meta
- Test class + meta
- Topic YAML
- Agent meta XML
- Golden eval markdown
- Trust checklist (each constraint → where it's enforced)
- Citations
Step 5 — Recommend follow-ups
/gen-testsif additional coverage beyond the scaffold is needed/scan-securityon the action Apex before promoting to production- Deploy the eval file under
evals/golden/and runevals/scripts/run_evals.py
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.
- 10d ago First seen · 80 lines · 0 tokens per session scan A 81068f249f2e
build-agentforce-action is a command published in the GitHub repository BanibrataChatterjee/AwesomeSalesforceSkills (3 stars, last pushed 4mo ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 575 tokens. 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.
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