A fast Rust-based headless browser automation CLI with Node.js fallback that enables AI agents to navigate, click, type, and snapshot pages via structured commands.
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Execute implementation plans using subagents. Three modes: single task, batch (sequential), parallel. Two-stage review for every task.
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실행 점수 미검증
설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Execute implementation plans using fresh subagents. Each task gets a new session. Every task goes through two-stage review (spec compliance + code quality).
Core principle: One subagent per task + two-stage review = high quality, fast iteration.
Delegate a single task to a subagent.
1. Prepare task text (full context + requirements)
2. Spawn an implementer subagent
3. When subagent completes, run spec review
4. When spec passes, run code quality review
5. Update task status
Execute tasks from a plan file in order. Checkpoint between each task.
1. Read the plan file, extract all tasks
2. Create $AEK_HOME/tasks/current-plan.md (for tracking)
3. Apply Mode A for each task
4. Update task status after each task
5. Run final review when all tasks complete
Run 2+ independent tasks simultaneously.
1. Verify independence (no shared files/state)
2. Spawn separate subagent for each task
3. Collect results when all subagents complete
4. Run spec + quality review for each
5. Resolve merge conflicts if any
Do NOT use parallel when:
Use $AEK_HOME/tasks/current-plan.md for plan tracking:
# Plan: [Feature Name]
Date: YYYY-MM-DD
Source: [plan file path]
## Task 1: [Name] -- COMPLETED
- Implementer: session_xxx
- Review: PASSED
- Commit: abc1234
## Task 2: [Name] -- IN PROGRESS
- Implementer: session_yyy
## Task 3: [Name] -- PENDING
Read plan file
|
v
Extract all tasks (full text + context)
|
v
Create current-plan.md
|
v
[For each task]
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v
Spawn implementer subagent
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v
Subagent asking questions? --YES--> Answer, provide context
| |
NO v
| Subagent implements
v |
Subagent implements <----------------------+
|
v
Spawn spec reviewer subagent
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v
Spec compliant? --NO--> Implementer fixes --> re-run spec review
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YES
|
v
Spawn code quality reviewer subagent
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v
Quality approved? --NO--> Implementer fixes --> re-run quality review
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YES
|
v
Mark task as COMPLETED
|
v
More tasks? --YES--> Next task
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NO
|
v
Final code review (entire implementation)
|
v
Pass through verification-gate
[spawn subagent]:
task="Implement Task N: [task name]
[FULL task text -- do not make the subagent read files, provide everything]
## Context
[Where this task fits, dependencies, architecture]
## Before You Start
If you have questions about requirements or approach, ASK FIRST.
## Task
1. Implement exactly what the spec says
2. Write tests (TDD)
3. Verify
4. Commit
5. Self-review
6. Report what was done"
model="[your model]"
[spawn subagent]:
task="Spec Compliance Review -- Task N
## What Was Requested
[FULL task requirements]
## Implementer's Claim
[From their report]
## CRITICAL: Do NOT Trust the Report
The implementer may have rushed. Verify everything INDEPENDENTLY.
- Do not accept their claims at face value
- Read the actual code
- Compare requirements line by line
- Look for missing parts, look for extra features
Report: PASS -- Spec compliant OR FAIL -- Issues [list]"
model="[your model]"
[spawn subagent]:
task="Code Quality Review -- Task N
## What Was Implemented
[Summary]
## Checklist
- OWASP Top 10 security
- Clean code principles
- Test quality (tests real behavior, not mocks)
- Error handling appropriateness
- Naming conventions
- YAGNI -- any unnecessary complexity?
Report: Strengths, Issues (Critical/Important/Minor), Verdict"
model="[your model]"
Never:
Required skills:
structured-brainstorming -- Creates the plan that this skill executestdd-workflow -- Subagents follow TDDverification-gate -- Final verification when all tasks completesystematic-debugging -- When a subagent hits an error아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.
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A fast Rust-based headless browser automation CLI with Node.js fallback that enables AI agents to navigate, click, type, and snapshot pages via structured commands.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Agent islemlerini policy'ye gore denetle. Hassas islem oncesi governance kontrolu calistir. Tetikleyiciler: destructive komut, dosya sistemi erisimi, rate limit, butce kontrolu, trust score yonetimi.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Evaluate and improve AgentSystem learning quality for memory/self-compound jobs. Use when bots forget, write filler summaries, fail to update learnings, or memory quality regresses over time.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Operate and validate AgentSystem live quality alarm (systemd timer + alert state) to keep memory/proactivity/consistency regressions visible in real time.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Audit and improve AgentSystem memory retention, proactive behavior, learning quality, and agent consistency. Use when bots forget context, skip memory writes, wait for explicit prompts, or repeat the same errors across runs.
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Audit and remediate AgentSystem cron output hygiene by detecting raw tool logs, stack traces, and noisy summaries. Use when jobs leak 403/429/internal details, summaries are not user-safe, or post-change validation is required.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.