Borrowing it
Nothing to install: this file belongs to yash-gadodia/claude-init. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/yash-gadodia/claude-init/main/.claude/skills/onboard/SKILL.mdgit clone --depth 1 https://github.com/yash-gadodia/claude-initWrote 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/yash-gadodia/claude-init/onboard)<a href="https://agentmods.dev/skills/yash-gadodia/claude-init/onboard"><img src="https://agentmods.dev/badge/skills/yash-gadodia/claude-init/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/yash-gadodia/claude-init/onboard"><img src="https://agentmods.dev/badge/skills/yash-gadodia/claude-init/onboard.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 | $0.00045 | $0.00650 |
| Opus 5 | $0.00023 | $0.00325 |
| Sonnet 5 | $0.00009 | $0.00130 |
| Haiku 4.5 | $0.00005 | $0.00065 |
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 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 — 89 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Onboard: Get Up to Speed Fast
You are onboarding to this codebase. Your goal is to build a complete mental model and share it concisely.
If $ARGUMENTS is provided, focus on that area (e.g., "auth", "payments", "API layer").
Process
Phase 1: Structural Scan (use subagents in parallel)
Agent 1 — Architecture:
- Read CLAUDE.md, ARCHITECTURE.md, README, any docs/ directory
- Map the directory tree (top 3 levels)
- Identify framework, language, key dependencies
Agent 2 — Data Layer:
- Find schema files (Prisma, migrations, models)
- Map entities and relationships
- Identify the ORM/query layer
Agent 3 — API Surface:
- Find route definitions, controllers, resolvers
- Map endpoints/operations to handlers
- Identify auth middleware, validation
Agent 4 — Frontend (if applicable):
- Map pages/routes to components
- Identify state management
- Find shared UI components
Phase 1.5: ARCHITECTURE.md Freshness Check
If ARCHITECTURE.md exists:
- Compare its Directory Structure section against the actual top-level directories
- Check if modules/services mentioned still exist
- Check if new top-level directories have appeared that aren't documented
- If stale sections are found, flag them in the output and offer to update ARCHITECTURE.md
If ARCHITECTURE.md does NOT exist:
- Note this as a gap in the output
- Suggest running
/claude-initor/updateto generate one
Phase 2: Pattern Extraction
From the codebase, identify:
- The happy path: How does a typical request flow from user action to database and back?
- Error handling pattern: How are errors caught, logged, and surfaced?
- Auth pattern: How is authentication/authorization enforced?
- Testing pattern: What's tested, what's not, how to run tests?
- Deploy pattern: How does code get to production?
Phase 3: Output
Present a concise onboarding doc:
# [Project Name] — Onboarding
## What This Does
<1-2 sentences>
## Stack
<language, framework, DB, key deps>
## Architecture
<ASCII diagram or bullet-point flow>
## Key Patterns
<The 3-5 most important patterns to follow>
## Where Things Live
<Map of important directories/files>
## How to Run
<Dev server, tests, build, deploy commands>
## Gotchas
<Non-obvious things that will trip you up>
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 · 89 lines · 45 tokens per session scan A f799f0b8a2cd
onboard is a skill published in the GitHub repository yash-gadodia/claude-init (5 stars, last pushed 3d ago), licensed MIT. It adds 45 tokens to every session and 650 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 2026-08-31.
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