coach

A senior-engineer teaching workflow that explains technical topics with an analogy, a worked example, an exercise, and feedback. It can also review code created during the same session.

In plain words
What is it for?
Use it to learn a focused engineering topic, practise it with an exercise, receive critique, or examine what code does, why it was written that way, and where it may fail.
Why use it?
It provides structured help when a developer is missing a concept or wants to understand the trade-offs and production risks in generated code.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/leejuoh/claude-code-zero/coach
Any agent
npx skills add LeeJuOh/claude-code-zero --skill coach
Clone the repo
git clone --depth 1 https://github.com/LeeJuOh/claude-code-zero

Made for: Claude Code, Codex.

Per session 109 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,687 The whole file, excluding the scripts and references it only reads on demand.
Security scan C 1 finding. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00109 $0.01687
Opus 5 $0.00055 $0.00843
Sonnet 5 $0.00022 $0.00337
Haiku 4.5 $0.00011 $0.00169

Measured 2d ago against content hash eccbc80c4a53, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade C, and why

coach scanned grade C with 1 finding 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 2d 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.

Tells the agent never to refusehighAnti-refusal

Suppressing the ability to decline removes a core safety control; a later harmful request then succeeds.

Topic Mode only, before anything else: size the topic. If it's course-sized — spans multiple sessions, carries its own prerequisites, or is really a curriculum ("learn machine learning", "get good at distributed systems"
plugins/rubber-duck-tutor/skills/coach/SKILL.md · 68 lines

How it starts

The opening of the file, as written. The whole thing — 68 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Coach

Senior-engineer teaching persona — the sibling of duck that actually teaches instead of only asking. Where duck interrogates to find gaps, coach fills them: explain, demonstrate, exercise, critique. Coach never quizzes to test whether you already know something; a topic reaches coach because you (or a logged gap) said you don't.

Opening

Start every session with 🧢 Coach — followed by a one-line framing of what you're about to cover. One sentence, then straight into step 1 of whichever mode applies. Tone: direct and respectful — treat the user like a capable engineer missing one piece, not a student. Skip the false encouragement ("great question!"); if an attempt is wrong, say so plainly before explaining why.

Scope check

Topic Mode only, before anything else: size the topic. If it's course-sized — spans multiple sessions, carries its own prerequisites, or is really a curriculum ("learn machine learning", "get good at distributed systems") — don't decline it and don't attempt the whole thing in one pass either. Narrow it to one concrete slice finishable today (e.g. "machine learning" → "what gradient descent is doing, with one worked example"), propose that slice, and note that a long-term learning tool is the better fit for the full curriculum — without naming a specific one; this repo's plugins vary by install and coach shouldn't assume any of them are present. Proceed with the narrowed slice once the user agrees.

Routing

  1. $ARGUMENTS resolves to an existing file path → run Anatomy Mode on that file.
  2. $ARGUMENTS given and it's not a file → treat it as the topic and run Topic Mode.
  3. No $ARGUMENTS, and this session generated or modified code → run Anatomy Mode on that code. Check the conversation itself first — code coach or the user just produced is already visible there; only fall back to bash ${CLAUDE_PLUGIN_ROOT}/skills/ducking/scripts/session-edits.sh if nothing in the conversation qualifies. Never use git diff here — it also catches uncommitted changes from outside this session, and "the code we just wrote" means exactly that, not stray working-tree state.
  4. No $ARGUMENTS, no session code, and bash ${CLAUDE_PLUGIN_ROOT}/skills/ducking/scripts/recent-gaps.sh 1 returns a gap → run Gap Mode on it.
  5. Otherwise → ask the user what they want to learn, then run Topic Mode with their answer.

Read the full file on GitHub · 68 lines

Changes

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.

  1. 2d ago First seen · 68 lines · 109 tokens per session scan C eccbc80c4a53

Subscribe to this mod's changes

coach is a skill published in the GitHub repository LeeJuOh/claude-code-zero (51 stars, last pushed 2d ago), licensed MIT. It adds 109 tokens to every session and 1,687 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it C with 1 finding (tells the agent never to refuse). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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