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 shennawardana23/skillme --skill engineering-onboarding-designgit clone --depth 1 https://github.com/shennawardana23/skillmeWrote 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/shennawardana23/skillme/engineering-onboarding-design)<a href="https://agentmods.dev/skills/shennawardana23/skillme/engineering-onboarding-design"><img src="https://agentmods.dev/badge/skills/shennawardana23/skillme/engineering-onboarding-design/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/shennawardana23/skillme/engineering-onboarding-design"><img src="https://agentmods.dev/badge/skills/shennawardana23/skillme/engineering-onboarding-design.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 | $0.00095 | $0.01169 |
| Opus 5 | $0.00048 | $0.00584 |
| Sonnet 5 | $0.00019 | $0.00234 |
| Haiku 4.5 | $0.00010 | $0.00117 |
Grade A, and why
engineering-onboarding-design 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 11d 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 — 97 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Engineering Onboarding Design
Good onboarding has one job in the first week: prove, with a real (small, safe) production change, that the new engineer's access, environment, and mental model all actually work end to end. Everything else — reading docs, shadowing, architecture overviews — supports that goal; none of it substitutes for it.
Structure by phase
Day 1: accounts, repo access, local environment running, a assigned onboarding buddy (not their manager — someone who remembers what it's like to not know anything yet). Confirm they can actually run the app/tests locally before the day ends; environment setup that "should work" but hasn't been verified is the most common silent blocker for week one.
Week 1: ship one small, real, reviewed change to production. Not a toy repo, not a doc-only PR padded to look substantial — a genuinely small production change (a copy fix, a small bug fix, a well-scoped test addition) that exercises the actual review/CI/deploy pipeline. This validates access, tooling, and process simultaneously, and gives the new hire a concrete win instead of a week of passive reading.
First 30 days: own one task end-to-end — design, implement, test,
ship, and (if applicable) monitor after release — with support available
but not doing it for them. Pair this with directed codebase orientation:
have them trace one real user request through the system (a "FedEx tour,"
see exploratory-testing-techniques) rather than reading an architecture
diagram cold.
First 60 days: contribute to a project or feature independently,
including making a design decision of their own (see
technical-decision-records for how that decision should get written
down) and participating in on-call/incident response in a shadow or
paired capacity if the team carries on-call.
First 90 days: full productivity — owns an area or component, participates in code review for others (not just receiving it), and gives feedback on the onboarding process itself while it's still fresh (see below).
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 11d ago First seen · 97 lines · 95 tokens per session scan A 28fae94a697b
engineering-onboarding-design is a skill published in the GitHub repository shennawardana23/skillme (2 stars, last pushed 14d ago), licensed Apache-2.0. It adds 95 tokens to every session and 1,169 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 2026-08-31.
Other skills, from other repositories
writing-skills
Use when creating new skills, editing existing skills, or verifying skills work before deployment.
receiving-code-review
Use when receiving code review feedback, before implementing suggestions, especially if feedback seems unclear or technically questionable - requires technical rigor and verification, not performative agreement or blind implementation.
writing-plans
Use when you have a spec or requirements for a multi-step task, before touching code.
skill-creator
Create, improve, evaluate, benchmark skills. Use when authoring a new skill, updating an existing one, running evals, or optimizing a skill's description for triggering. Don't use for invoking skills, writing prose, or scaffolding Python projects.
skill-index-updater
Add GitHub skill repos to the ASM index: clone, audit, eval, regenerate index, rebuild catalog, open PR. Use when given GitHub URLs to onboard. Don't use for authoring (skill-creator), improving (skill-auto-improver), or install (asm install).
hello-world
A minimal test skill that greets the user and demonstrates the ASM publish workflow.