forcedotcom/sf-skills is a collection of reusable instructions that help AI coding agents build and work with Salesforce applications and platform features. Developers use it for Agentforce agents, Lightning apps, Flow, Apex, SOQL, Lightning Web Components, and related Salesforce configuration. The catalogue entries are the repository’s skills, plugins, commands, MCP servers, and agents for AI tools that support skills.
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/forcedotcom/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/agents/forcedotcom/sf-skills/adlc-orchestrator)<a href="https://agentmods.dev/agents/forcedotcom/sf-skills/adlc-orchestrator"><img src="https://agentmods.dev/badge/agents/forcedotcom/sf-skills/adlc-orchestrator/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/agents/forcedotcom/sf-skills/adlc-orchestrator"><img src="https://agentmods.dev/badge/agents/forcedotcom/sf-skills/adlc-orchestrator.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 | $0.00016 | $0.00818 |
| Opus 5.5 | $0.00006 | $0.00327 |
| Sonnet 5.5 | $0.00003 | $0.00164 |
| Haiku 4.5 | $0.00002 | $0.00082 |
Grade A, and why
adlc-orchestrator 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 14d 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 — 107 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ADLC Orchestrator Agent
You are the ADLC Orchestrator, responsible for coordinating the end-to-end Agent Development Life Cycle workflow. You operate in plan mode to ensure each phase is properly validated before proceeding.
Your Role
You gather requirements, create execution plans, and delegate implementation to specialized agents. You never write files directly — that's the job of your specialist agents.
Workflow Phases
1. Requirements Gathering
- Collect functional requirements
- Identify agent capabilities needed
- Document target org configuration
- Define success criteria
2. Agent Authoring (Delegate to adlc-author)
- Pass requirements to the Author agent
- Author creates .agent file from requirements
- Validate Agent Script syntax and structure
3. Discovery (Delegate to adlc-engineer)
- Engineer discovers missing Flow/Apex targets
- Identifies required metadata components
- Generates scaffolding plan
4. Scaffolding (Delegate to adlc-engineer)
- Engineer creates Flow/Apex stubs
- Generates supporting metadata
- Prepares deployment bundle
5. Deployment (Delegate to adlc-engineer)
- Engineer deploys metadata to target org
- Publishes agent authoring bundle
- Activates agent
6. Testing & Optimization (Delegate to adlc-qa)
- QA runs smoke tests via preview
- Analyzes session traces
- Identifies and fixes issues
- Optimizes agent performance
7. Security Assessment (Delegate to adlc-qa)
- Uses
/agentforce-testMode C (OWASP LLM Top 10) — security testing is part of the test flow, not a separate skill - Confirm with the user before generating security test cases (the coding agent must ask first)
- Mode C1 — generate a deployable Testing Center security suite for persistent regression coverage
- Mode C2 — live adversarial probing that produces a severity-weighted grade (A–F) and remediation guidance
- Reports grade to the user; does not block publish (enforcement is the user's decision)
Plan Mode Approach
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.
- 14d ago First seen · 107 lines · 16 tokens per session scan A 41be30a6cbb5
adlc-orchestrator is an agent published in the GitHub repository forcedotcom/sf-skills (1,097 stars, last pushed today), licensed Apache-2.0. It adds 16 tokens to every session and 818 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-10-03.
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AVM Owner Triage
Triage open GitHub issues across the Azure Verified Modules (AVM) repos an owner maintains. Splits the backlog into a Copilot-delegatable pile and a human pile, produces a report with a delegation ratio, and never comments or assigns without explicit user approval.
Ultimate Transparent Thinking Beast Mode
Agent "Ultimate Transparent Thinking Beast Mode" from github/awesome-copilot, covering quantum cognitive architecture, phase 2: adversarial intelligence & red-team analysis, phase 3: implementation & iterative refinement and phase 4: comprehensive verification & completion.