kkirikkiri

English | 한국어

kkirikkiri (끼리끼리)

One sentence. A team of AI agents, assembled and running.

Describe what you want in plain language. kkirikkiri interviews you with 2–3 questions, scans your environment, proposes a team, and executes — all within Claude Code.

Quick Start • Why kkirikkiri? • How it works • Features • Requirements

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Quick Start

1. Add the marketplace

/plugin marketplace add https://github.com/fivetaku/gptaku_plugins.git

2. Install

/plugin install kkirikkiri

3. Enable Agent Teams

// ~/.claude/settings.json
{
  "env": {
    "CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS": "1"
  }
}

4. Run

/kkirikkiri build me a research team

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Why kkirikkiri?

• Natural language in, running team out — no YAML, no agent definitions to write by hand
• Interview-driven — 2–3 targeted questions replace a long configuration form
• Environment-aware — detects installed tools (Codex CLI, Antigravity CLI agy, .claude/agents/) and builds the best team from what you actually have
• Multi-model — Claude, Codex CLI (code & large-scale analysis), and Antigravity CLI (design/UI) can each take different roles in the same team
• Two execution substrates — you pick: a live collaborating team (Agent Teams) or a deterministic agent pipeline (Workflows) for high-volume fan-out work
• Validation loop — if round 1 output falls short, the team is automatically retried or rebuilt (up to 3 rounds)
• Shared memory — .kkirikkiri/teams/{team_name}/ files persist across rounds so a replacement team picks up context immediately; each session gets its own directory to prevent multi-session collisions
• Reusable agents — save team members to .claude/agents/ for use in future projects

The name comes from the Korean idiom 끼리끼리 — like-minded people naturally gathering together. Every team is assembled around a shared purpose.

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How it works

Natural language input
    → Step 1: Intent detection + preset matching
    → Step 2: Environment scan (parallel)
    → Step 3: Interview — 2–3 AskUserQuestion prompts
    → Step 4: Dynamic team composition
    → Step 5: Team proposal + your confirmation
    → Step 6: Shared memory init + team execution
    → Step 7: Quality validation loop (up to 3 rounds)
    → Step 8: Result collection + report

Team leader rules:

• Leader is always the most capable model available (Opus by default)
• Leader plans, delegates, and validates — never writes code directly
• Each member has a strictly scoped role

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Features

Presets

Five built-in presets with natural-language trigger matching:

Preset	Trigger words	Default team
Research	research, find, look up, compare	Leader + 2 researchers
Development	build, implement, code, add feature	Leader + 2 developers
Analysis	analyze, review, inspect, audit	Leader + 2 explorers
Content	write, document, README, blog post	Leader + writer + reviewer
Product/PM	PRD, strategy, roadmap, OKR, GTM	Leader + PM + researcher

Presets are a starting point. The interview and environment scan shape the final team every time.

Shared memory

The team writes to .kkirikkiri/teams/{team_name}/ in your project root (session-scoped, no collision between concurrent sessions):

File	Purpose
TEAM_PLAN.md	Task plan, role assignments, goals
TEAM_PROGRESS.md	Live progress — completed and pending items
TEAM_FINDINGS.md	Discoveries, dead ends (DEAD_ENDS)
report.md	Canonical final report for this session

Saved teams are stored cross-session under .kkirikkiri/shared/saved-teams/. If a team member is replaced mid-task, the new member reads these files and catches up immediately.

Validation loop

Round	Strategy
Round 1	Original team executes
Round 2	Auto-judge: keep (A) / full replacement (B) / partial swap (C)
Round 3	Full team rebuild, unconditionally

Multi-model support

Claude + Codex CLI (code & large-scale analysis, cross-model review) + Antigravity CLI agy (design/UI) can each take different roles in the same team. kkirikkiri auto-detects what is installed and optimizes accordingly. Claude-only works fine if no external CLIs are present.

Agent auto-detection and reuse

If .claude/agents/ contains agent definitions, kkirikkiri detects them and recommends relevant ones per preset:

Preset	Example agents
Research	insane-research, data-analyst
Development	code-reviewer, architect
Analysis	code-analyzer, security-reviewer
Content	writer, translator

After a successful run, you can save well-performing team members back to .claude/agents/ for reuse in other projects.

Spawn stability