learn — for Claude Code nyaomaru-portfolio, community, for Claude Code, ide skills, browser-game, feature-sliced-design, gamedev, portfolio, rungame, typescript

v1.0.0

이 스킬 정보

적합한 상황: Ideal for AI agents that need /learn - extract reusable patterns. 현지화된 요약: Auto-Activation Criteria Consider auto-activating this skill when: A complex bug was fixed (non-obvious root cause, multi-step diagnosis). It covers browser-game, feature-sliced-design, game workflows. This AI agent skill supports Claude Code, Cursor, and Windsurf workflows.

기능

/learn - Extract Reusable Patterns
Analyze the current session and capture patterns worth reusing in this Remix + React + TypeScript
Auto-Activation Criteria
Consider auto-activating this skill when:
A complex bug was fixed (non-obvious root cause, multi-step diagnosis).

# Core Topics

nyaomaru nyaomaru
[4]
[1]
Updated: 4/26/2026

Killer-Skills Review

Decision support comes first. Repository text comes second.

Reference-Only Page Review Score: 10/11

This page remains useful for teams, but Killer-Skills treats it as reference material instead of a primary organic landing page.

Original recommendation layer Concrete use-case guidance Explicit limitations and caution Quality floor passed for review
Review Score
10/11
Quality Score
70
Canonical Locale
en
Detected Body Locale
en

적합한 상황: Ideal for AI agents that need /learn - extract reusable patterns. 현지화된 요약: Auto-Activation Criteria Consider auto-activating this skill when: A complex bug was fixed (non-obvious root cause, multi-step diagnosis). It covers browser-game, feature-sliced-design, game workflows. This AI agent skill supports Claude Code, Cursor, and Windsurf workflows.

이 스킬을 사용하는 이유

추천 설명: learn helps agents /learn - extract reusable patterns. Auto-Activation Criteria Consider auto-activating this skill when: A complex bug was fixed (non-obvious root cause, multi-step diagnosis). This AI agent

최적의 용도

적합한 상황: Ideal for AI agents that need /learn - extract reusable patterns.

실행 가능한 사용 사례 for learn

사용 사례: Applying /learn - Extract Reusable Patterns
사용 사례: Applying Analyze the current session and capture patterns worth reusing in this Remix + React + TypeScript
사용 사례: Applying Auto-Activation Criteria

! 보안 및 제한 사항

  • 제한 사항: Do not activate for:
  • 제한 사항: Requires repository-specific context from the skill documentation
  • 제한 사항: Works best when the underlying tools and dependencies are already configured

Why this page is reference-only

  • - Current locale does not satisfy the locale-governance contract.

Source Boundary

The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.

After The Review

Decide The Next Action Before You Keep Reading Repository Material

Killer-Skills should not stop at opening repository instructions. It should help you decide whether to install this skill, when to cross-check against trusted collections, and when to move into workflow rollout.

Labs Demo

Browser Sandbox Environment

⚡️ Ready to unleash?

Experience this Agent in a zero-setup browser environment powered by WebContainers. No installation required.

Boot Container Sandbox

FAQ & Installation Steps

These questions and steps mirror the structured data on this page for better search understanding.

? Frequently Asked Questions

What is learn?

적합한 상황: Ideal for AI agents that need /learn - extract reusable patterns. 현지화된 요약: Auto-Activation Criteria Consider auto-activating this skill when: A complex bug was fixed (non-obvious root cause, multi-step diagnosis). It covers browser-game, feature-sliced-design, game workflows. This AI agent skill supports Claude Code, Cursor, and Windsurf workflows.

How do I install learn?

Run the command: npx killer-skills add nyaomaru/nyaomaru-portfolio/learn. It works with Cursor, Windsurf, VS Code, Claude Code, and 19+ other IDEs.

What are the use cases for learn?

Key use cases include: 사용 사례: Applying /learn - Extract Reusable Patterns, 사용 사례: Applying Analyze the current session and capture patterns worth reusing in this Remix + React + TypeScript, 사용 사례: Applying Auto-Activation Criteria.

Which IDEs are compatible with learn?

This skill is compatible with Cursor, Windsurf, VS Code, Trae, Claude Code, OpenClaw, Aider, Codex, OpenCode, Goose, Cline, Roo Code, Kiro, Augment Code, Continue, GitHub Copilot, Sourcegraph Cody, and Amazon Q Developer. Use the Killer-Skills CLI for universal one-command installation.

Are there any limitations for learn?

제한 사항: Do not activate for:. 제한 사항: Requires repository-specific context from the skill documentation. 제한 사항: Works best when the underlying tools and dependencies are already configured.

How To Install

  1. 1. Open your terminal

    Open the terminal or command line in your project directory.

  2. 2. Run the install command

    Run: npx killer-skills add nyaomaru/nyaomaru-portfolio/learn. The CLI will automatically detect your IDE or AI agent and configure the skill.

  3. 3. Start using the skill

    The skill is now active. Your AI agent can use learn immediately in the current project.

! Reference-Only Mode

This page remains useful for installation and reference, but Killer-Skills no longer treats it as a primary indexable landing page. Read the review above before relying on the upstream repository instructions.

Upstream Repository Material

The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.

Upstream Source

learn

Install learn, an AI agent skill for AI agent workflows and automation. Review the use cases, limitations, and setup path before rollout.

SKILL.md
Readonly
Upstream Repository Material
The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.
Supporting Evidence

/learn - Extract Reusable Patterns

Analyze the current session and capture patterns worth reusing in this Remix + React + TypeScript portfolio project.

Auto-Activation Criteria

Consider auto-activating this skill when:

  1. A complex bug was fixed (non-obvious root cause, multi-step diagnosis).
  2. A new implementation pattern was discovered and validated.
  3. A workaround was implemented for a library/framework/platform limitation.
  4. The session is ending and meaningful technical learning was produced (ask user before writing).

Do not activate for:

  • trivial typo fixes
  • one-line obvious refactors
  • temporary external incidents (e.g., transient API outage)
  • patterns already documented in .codex/skills/learn/learned/

Manual Trigger

Run /learn after resolving a non-trivial issue.

What To Extract

1. Error Resolution Patterns

  • What failed?
  • What was the real root cause?
  • What exact fix worked?
  • How can the same pattern be reused?

Project-relevant examples:

  • React state/ref synchronization issues in game loops.
  • Collision box mismatches vs. rendered visuals.
  • TypeScript literal type narrowing issues in refs/state.
  • Asset-driven animation state bugs (timing/order/race).

2. Debugging Techniques

  • Non-obvious debugging sequence that worked.
  • Useful command/tool combinations.
  • Fast diagnosis patterns.

Project-relevant examples:

  • Using pnpm typecheck + pnpm build after each gameplay logic change.
  • Comparing style-driven transforms with getBoundingClientRect() collision logic.
  • Isolating hooks (useGameLoop, useObstacles, useJump) to pinpoint regressions.

3. Workarounds

  • Framework/library constraints and mitigations.
  • Version-specific fixes.
  • Practical tradeoffs that were chosen and why.

Project-relevant examples:

  • Sprite/background animation fallback instead of SVG internals.
  • Responsive scaling using legacy baseline ratios.
  • Clear-state sequence orchestration with phased timers.

4. Project-Specific Patterns

  • Codebase conventions discovered during implementation.
  • Architecture decisions and integration boundaries.

Project-relevant examples:

  • Feature-based structure (features/jump-game/...) and hook boundaries.
  • Runtime DOM element creation pattern in obstacle systems.
  • Separation of render state (UI) vs. simulation state (loop refs).

Output Format

Create one file per pattern at:

.codex/skills/learn/learned/[pattern-name].md

Template:

markdown
1# [Descriptive Pattern Name] 2 3**Captured:** YYYY-MM-DD 4**Context:** [When this pattern applies] 5**Tags:** react, remix, typescript, game-loop, collision, animation, etc. 6 7## Problem 8 9[Specific recurring problem this pattern solves] 10 11## Solution 12 13[Reusable approach, key decisions, and constraints] 14 15## Example 16 17```ts 18// Minimal real example from this codebase 19```

When To Use

[Trigger conditions for applying this pattern]

  • features/jump-game/model/useGameLoop.ts
  • features/jump-game/model/useObstacles.ts
  • features/jump-game/ui/JumpGame.tsx

## Process

1. Review the session for candidate learnings.
2. Select the highest-value reusable pattern(s).
3. Draft the pattern file.
4. Ask user confirmation before saving.
5. Save to `.codex/skills/learn/learned/`.
6. Update `.codex/skills/learn/LEARNED_INDEX.md` with a one-line entry (required).

Index format:

`- **[pattern-name](learned/pattern-name.md)** - One-line summary.`

## Common Pattern Categories For This Project

- Game loop timing and state synchronization
- Collision detection vs. rendered position alignment
- Responsive scaling from fixed-pixel legacy assumptions
- Sprite animation sequencing and transitions
- Boss/obstacle/fish spawn balancing and fail-state prevention
- TypeScript safety for refs, unions, and hook contracts

## Notes

- Capture only non-trivial, reusable patterns.
- Keep one pattern per file.
- Prefer concrete examples from this repository.
- Keep entries short, searchable, and implementation-focused.

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