skills/learn/SKILL.md
Use when you want to manually add new learnings or rules to memory.
npx skillsauth add seokan-jeong/team-shinchan team-shinchan:learnInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Teach Team-Shinchan something to remember.
/team-shinchan:learn "Always use TypeScript strict mode in this project"
/team-shinchan:learn "User prefers functional components over class components"
| Keywords | Category | Tier |
|----------|----------|------|
| prefer, like, want | preference | preference |
| pattern, approach, way | pattern | procedural |
| always, never, rule, use | convention | procedural |
| error, mistake, avoid | mistake | procedural |
| decided, architecture | decision | procedural |
| other | insight | procedural |
Note: tool tier is set when user explicitly passes --tier tool or AI detects tool-specific phrasing (CLI flags, tool config patterns).
.shinchan-docs/learnings.md (create if missing):### [{category}] {title}
- **Date**: {YYYY-MM-DD}
- **Source**: manual
- **Tier**: {auto-mapped tier}
- **Confidence**: high
- **Tags**: #{auto-generated tags}
- **Insight**: {content}
---
🧠 [Learn] Saved to memory:
Category: {category}
Learning: "{content}"
📁 Location: .shinchan-docs/learnings.md
.shinchan-docs/)load-kb hook/team-shinchan:forgetdevelopment
Deterministic adversarial code review for high-stakes scope — independent per-dimension review, a non-skippable per-finding refutation, a completeness critic, and a schema-validated rubric judge. Opt-in main-loop Workflow tier.
data-ai
Deterministic loop-until-done for high-stakes long-running tasks — a worker/verifier loop the script bounds by iteration cap, token budget, and stagnation, closed by an Action-Kamen gate. Opt-in main-loop Workflow tier.
testing
Deterministic adversarial debate for high-stakes or irreversible decisions — mandatory refutation plus a scored judge panel. Opt-in main-loop Workflow tier.
development
Deterministic competitive code tournament — N builders independently solve one task and return patches, an Action-Kamen judge scores them head-to-head, the winner is picked by score and applied. Opt-in main-loop Workflow tier.