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 kumaran-is/claude-code-onboarding --skill code-explainergit clone --depth 1 https://github.com/kumaran-is/claude-code-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/kumaran-is/claude-code-onboarding/code-explainer)<a href="https://agentmods.dev/skills/kumaran-is/claude-code-onboarding/code-explainer"><img src="https://agentmods.dev/badge/skills/kumaran-is/claude-code-onboarding/code-explainer/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/kumaran-is/claude-code-onboarding/code-explainer"><img src="https://agentmods.dev/badge/skills/kumaran-is/claude-code-onboarding/code-explainer.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 | $0.00087 | $0.01266 |
| Opus 5.5 | $0.00035 | $0.00506 |
| Sonnet 5.5 | $0.00017 | $0.00253 |
| Haiku 4.5 | $0.00009 | $0.00127 |
Grade A, and why
code-explainer 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 2mo 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 — 113 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Code Explainer
Iron Law
TWO LEVELS EVERY TIME — NEVER COLLAPSE THEM INTO ONE
Level 1 is for someone who uses the code. Level 2 is for someone who changes it. They need different information. Mixing them makes both audiences worse off.
When to Use
- Inheriting code you did not write
- Onboarding a new developer to an existing module
- Preparing to modify something you haven't touched in months
- Handing off ownership to another team
- Writing documentation from code
The Four Outputs (All Mandatory)
Level 1 — For the User (what it does)
Explain what this code accomplishes in plain language. No jargon. A non-technical stakeholder should be able to read this and understand:
- What problem the code solves
- What inputs it needs
- What it produces or causes to happen
Write this as if you're explaining to a product manager, not a developer.
Level 2 — For the Modifier (why it is built this way)
Explain the architectural decisions. A senior engineer about to touch this code needs to know:
- Why is it structured this way? (not just what it does)
- What would break if you changed X?
- What are the dependencies — internal and external?
- Where are the edge cases? What inputs cause unexpected behavior?
- What assumptions are baked in that aren't obvious from reading?
- What would a senior engineer want to verify before touching this?
Do not dumb this down. Level 2 readers are technical.
The Fragile Part (1 sentence, mandatory)
The section of this code most likely to break under unexpected conditions — unusual inputs, concurrent access, external failures, edge-case data. Name the specific location (function, line, block), not a general area.
The Assumption (1 sentence, mandatory)
The key assumption baked into this code that, if wrong, causes it to fail. This is not an edge case — it's a design premise. Examples: "assumes the caller always passes a non-null user object", "assumes the upstream API returns items in chronological order", "assumes this runs in a single-threaded context".
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.
- 2mo ago First seen · 113 lines · 0 tokens per session scan A 13527f471e4d
code-explainer is a skill published in the GitHub repository kumaran-is/claude-code-onboarding (36 stars, last pushed 4mo ago), licensed MIT. It adds 87 tokens to every session and 1,266 once invoked, about $0.0003 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 2026-09-03.
Other skills, from other repositories
hr-onboarding
A new-hire onboarding plan as a single page — first week schedule, buddy + manager intro, learning track, equipment checklist, and "you're set when…" outcomes. Use when the brief mentions "onboarding", "new hire", "first week plan", or "入职".
book-mirror
Take any book (EPUB/PDF), produce a personalized chapter-by-chapter analysis. Each chapter is preserved in detail (The Chapter) and mirrored back to the reader's actual life (The Mirror) using brain context. The mirror observes and resonates — a friend pointing out parallels, NOT a consultant rearranging the reader's…
exam-forecast
Analyze past exams from the same professor to surface patterns — subject weighting, recurring issue-spot traps, favored hypo types, policy-vs-doctrine mix — and forecast likely emphases for the upcoming exam. Use when the user says "what's on the exam", "analyze past exams", "predict the exam", or shares past exams.
lean-formalize
Develop and verify a mathematical proof in Lean, continue an incomplete Lean project, or audit whether it proves the original statement. Connect actual inputs to intermediate lemmas, assemble the target theorem, check its transitive axioms, and provide a reproducible handoff. Use when Lean is requested or a specific…
eli5
Explain research, papers, or technical ideas in plain English with minimal jargon, concrete analogies, and clear takeaways. Use when the user says "ELI5 this", asks for a simple explanation of a paper or research result, wants jargon removed, or asks what something technically dense actually means.
deck-course-module
A course or workshop slide template with persistent learning goals, teaching pages, multiple-choice self-tests, and a wrap-up.