Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/ulises-jeremias/agent-toolkitnpx agentmods add skills/ulises-jeremias/agent-toolkit/onboardingWrote 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/skills/ulises-jeremias/agent-toolkit/onboarding)<a href="https://agentmods.dev/skills/ulises-jeremias/agent-toolkit/onboarding"><img src="https://agentmods.dev/badge/skills/ulises-jeremias/agent-toolkit/onboarding/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/skills/ulises-jeremias/agent-toolkit/onboarding"><img src="https://agentmods.dev/badge/skills/ulises-jeremias/agent-toolkit/onboarding.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00054 | $0.00867 |
| Opus 5.5 | $0.00022 | $0.00347 |
| Sonnet 5.5 | $0.00011 | $0.00173 |
| Haiku 4.5 | $0.00005 | $0.00087 |
Grade A, and why
onboarding 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 1mo 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 — 123 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Onboarding
Welcome to agent-toolkit. This skill guides new users through initial setup and daily usage.
When to use
- User asks "where do I start?" or "how does this work?"
- First-time setup after
chezmoi apply - Troubleshooting a broken installation
- Onboarding a teammate
Step 1 — Validate setup
agent-toolkit doctor
Expected: result: COMPLIANT. If any check fails, doctor shows the fix command.
For persistent issues:
agent-toolkit --version # check for available updates (or: agent-toolkit update-check)
agent-toolkit inventory # verify skills are installed (or: agent-toolkit skills list)
agent-toolkit install --force # re-sync profiles to AI tools (replaces legacy `skills sync`)
Step 2 — Understand the hierarchy
User message
↓
Orchestrator (assistant)
↓ routes to
Workflow skill (WHAT — decides scope and gates)
e.g. workflow-generic-project
↓ delegates to
Tool skill (HOW — executes CLI commands)
e.g. clickup-cli, github-cli-workflow
Skills are invoked via the skill tool (e.g. /planning).
Agents are invoked via @mention (e.g. @planner). Never use the skill tool for agents.
Step 3 — First commands by role
Developer
# Start any task
/workflow-generic-project
# After code changes — review before PR
@code-reviewer
# CI failing
/gh-fix-ci
Product Manager / Tech Lead
# Plan a feature
@planner
# Create a ClickUp task
/clickup-cli # then: clickup task create --current --name "..."
# Project health check
/project-assessment
Anyone
# Check workstation health
agent-toolkit doctor
# Find which skill to use
# → ask the orchestrator: "what skill should I use for X?"
# Load client/project context
agent-toolkit workspace load packs/<client>.yaml # canonical; harness alias: ./bin/workspace-context load
Key concepts in 60 seconds
| Concept | What it means |
|---|---|
| Pack | Context bundle for a client/project (repos, constraints, commands) |
| Skill | Reusable AI prompt for a specific task (WHAT or HOW) |
| Agent | Specialized AI persona with restricted tool access (invoked via @mention) |
| Loop | Recurring autonomous process with state, tiers, and cost budgets |
| L1 / L2 / L3 | Loop autonomy tiers: L1=observe-only (read-only), L2=PR-gated (draft PR), L3=allowlisted (scoped writes) — see AGENTS.md loop.yaml tier/allowlist/deny |
| knowledge/ | Persistent memory across sessions |
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.
- 1mo ago First seen · 123 lines · 54 tokens per session scan A 05fb24221145
onboarding is a skill published in the GitHub repository ulises-jeremias/agent-toolkit (19 stars, last pushed today), licensed MIT. It adds 54 tokens to every session and 867 once invoked, about $0.0002 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 2026-09-04.
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