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 skills add tobihagemann/turbo --skill onboardgit clone --depth 1 https://github.com/tobihagemann/turboWrote 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/tobihagemann/turbo/onboard)<a href="https://agentmods.dev/skills/tobihagemann/turbo/onboard"><img src="https://agentmods.dev/badge/skills/tobihagemann/turbo/onboard/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/tobihagemann/turbo/onboard"><img src="https://agentmods.dev/badge/skills/tobihagemann/turbo/onboard.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Rogue Agent · line 12 Skill establishes unauthorized persistence across sessions via cron jobs, startup scripts, or state files. Session persistence allows an attacker to maintain access beyond the current interaction.Fix: Remove any persistence mechanisms (cron jobs, startup scripts, state files). Skills should not maintain state across sessions without explicit user consent.
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 | $0.00092 | $0.01655 |
| Opus 5 | $0.00046 | $0.00827 |
| Sonnet 5 | $0.00018 | $0.00331 |
| Haiku 4.5 | $0.00009 | $0.00166 |
Grade A, and why
onboard 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 12d 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 — 116 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Onboard
Developer onboarding pipeline. Composes /map-codebase, /review-tooling, and /review-agentic-setup with inline agents, then synthesizes everything into .turbo/onboarding.md and .turbo/onboarding.html. Analysis-only.
Task Tracking
At the start, use TaskCreate to create a task for each phase:
- Launch all agents
- Synthesize and generate markdown report
- Generate HTML report
Step 1: Launch All Agents
Emit all 6 Agent tool calls below in one assistant message. Each Agent call uses model: "opus" and no name. Wait for every agent to report before continuing. Do not begin the next step on a partial set, and do not relaunch an agent that has not yet reported. Each Composed Skills agent invokes its assigned skill via the Skill tool; each Inline Agent follows its exploration brief directly. Every agent's prompt must direct it to treat the shared working tree and its git index as read-only and to explore by reading and reasoning, except that each Composed Skills agent writes the report files its own skill defines. HEAD stays where it is: read other refs with git show <ref>:<path> rather than git checkout or git switch.
Composed Skills
Launch one Agent tool call per row. Each agent's prompt instructs it to invoke its assigned skill via the Skill tool.
| Skill | Onboarding role |
|---|---|
/map-codebase |
Architecture understanding: structure, tech stack, entry points, patterns, data flow, dependencies, testing |
/review-tooling |
Development workflow: linters, formatters, pre-commit hooks, test runners, CI/CD |
/review-agentic-setup |
Agentic coding: CLAUDE.md, AGENTS.md, skills, MCP servers, hooks, cross-tool compatibility |
Inline Agents
Launch one Agent tool call each with the exploration brief below.
| Agent | Exploration Brief |
|---|---|
| Prerequisites and Setup | Read README.md, CONTRIBUTING.md, and package manager configs (package.json, Gemfile, Cargo.toml, go.mod, pyproject.toml, Package.swift, etc.). Extract: required language runtimes and versions, system dependencies, environment variables, database or service requirements, first-time setup steps (install, build, run, seed), and any bootstrap or setup scripts. |
| Troubleshooting | Search for troubleshooting content in README.md, TROUBLESHOOTING.md, docs/ directory, FAQ files, and GitHub Discussions/Wiki if accessible. Extract common errors, known quirks, platform-specific gotchas, and debugging tips. If no troubleshooting docs exist, report that. |
| Next Steps | Run gh issue list --state open --json number,title,url,reactionGroups,comments,labels --limit 50. Identify: (1) issues labeled good-first-issue or good first issue, (2) top 5 issues by engagement score (sum of reactions weighted 2x for thumbs-up, plus comment count). If gh is not available or not in a GitHub repo, skip and note that. |
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.
- 12d ago First seen · 116 lines · 92 tokens per session scan A de0090c42757
onboard is a skill published in the GitHub repository tobihagemann/turbo (402 stars, last pushed 3d ago), licensed MIT. It adds 92 tokens to every session and 1,655 once invoked, about $0.0005 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 2026-08-30.
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