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/forcedotcom/sf-skills/welcomegit 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/commands/forcedotcom/sf-skills/welcome)<a href="https://agentmods.dev/commands/forcedotcom/sf-skills/welcome"><img src="https://agentmods.dev/badge/commands/forcedotcom/sf-skills/welcome.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 | $0.00033 | $0.00227 |
| Opus 5 | $0.00016 | $0.00113 |
| Sonnet 5 | $0.00007 | $0.00045 |
| Haiku 4.5 | $0.00003 | $0.00023 |
Grade A, and why
welcome 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 today.
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.
What it actually says
The plugin paints the full Salesforce session banner — the HEADLESS logo lockup, connected org, edition and API, project metadata counts, platform MCP server status, and the position rail — directly to the user on the visible channel, in color: the same banner the session-start surface shows, so it is already shown. Do NOT run a command, redraw it, reproduce it in a fenced block, reformat it into a table, or restate it line by line. Add only a brief, welcoming one- or two-line read that orients the user to the single most useful next step given what the banner shows. Every hard fact — org, edition, API version, metadata counts, MCP status, current stage — comes only from that painted surface: present these facts faithfully, never invent, recompute, or substitute a remembered value, and when the surface omits a fact, say it is unknown.
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.
- today First seen · 8 lines · 33 tokens per session scan A 8f59c1ba3dff
welcome is a command published in the GitHub repository forcedotcom/sf-skills (961 stars, last pushed yesterday), licensed Apache-2.0. It adds 33 tokens to every session and 227 once invoked, about $0.0002 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 2026-09-03.
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.