add .claude config folder with Claude-equivalent agent and hook settings

Mirrors .cursor and .codex structure with Claude model assignments:
- haiku for lighter tasks (translator, browser-check, profiler)
- sonnet for standard tasks (code-quality, plan-implementer, etc.)

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
This commit is contained in:
Tommaso Casaburi
2026-03-29 16:35:21 +07:00
co-authored by Claude Opus 4.6
parent e7d175afa1
commit 189d1c2165
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---
name: implement-plan
description: Orchestrates implementation of a multi-task plan by spawning plan-implementer subagents in parallel. Use when the user provides a plan file or plan text and asks to implement it, execute it, or says "implement plan", "run plan", "execute plan".
---
# Implement Plan
You are the **orchestrator**. Your job is to execute the attached plan by delegating tasks to `plan-implementer` subagents. Preserve your context window for coordination — never implement tasks yourself.
## Workflow
### 1. Analyze the Plan
Read the plan the user attached. Identify:
- All discrete tasks/steps
- Dependencies between tasks (which must run sequentially vs. can run in parallel)
- Any ambiguous items that need clarification before starting
If anything is unclear, ask the user before proceeding.
### 2. Group Tasks for Parallelization
Partition tasks into **parallel batches** based on dependencies:
```
Batch 1 (parallel): [tasks with no dependencies]
Batch 2 (parallel): [tasks that depend on batch 1]
Batch 3 (parallel): [tasks that depend on batch 2]
...
```
**Rules:**
- Max 4 concurrent subagents (tool limitation)
- Tasks touching the same file(s) go in the same subagent or sequential batches — never parallel
- Small related tasks can be grouped into one subagent to reduce overhead
- Large independent tasks get their own subagent
### 3. Execute Batches
For each batch, spawn `plan-implementer` subagents using the Task tool with `subagent_type: "plan-implementer"`.
Each subagent prompt must include:
- **Exact tasks** to implement (copy from the plan, don't paraphrase loosely)
- **File paths** and context needed to work independently
- **Constraints** or edge cases from the plan
Use `model: "fast"` for straightforward tasks. Omit model for complex ones.
Wait for all subagents in a batch to complete before starting the next batch.
### 4. Handle Failures
When a subagent reports PARTIAL or FAILED:
- Read its report to understand what failed and why
- Decide: retry with more context, reassign to a different batch, or implement the fix yourself if trivial
- Don't retry blindly — adjust the prompt or approach
### 5. Verify
After all batches complete:
1. Run `yarn build` to confirm everything compiles
2. Run `yarn lint` and `yarn type-check`
3. If the plan touched React components/hooks, run `yarn doctor`
4. For UI changes, verify in the browser with playwright-cli
### 6. Report
Summarize to the user:
```
## Plan Execution Summary
### Completed
- Task 1 — files modified
- Task 2 — files modified
### Failed (if any)
- Task N — reason, what was tried
### Verification
- Build: PASS/FAIL
- Lint: PASS/FAIL
- Type-check: PASS/FAIL
```
## Key Principles
- **You orchestrate, subagents implement.** Don't code changes yourself unless it's a trivial one-liner fix for a subagent failure.
- **Context is precious.** Every build log and file read you do in the main thread is context you can't get back. Delegate liberally.
- **Parallelize aggressively.** The faster batches finish, the faster the plan is done. Only serialize when dependencies demand it.
- **Verify at the end, not in between.** Subagents run their own build checks. You do a final holistic verification.