跳到正文
cccskills
免费在 GitHub 公开

tech-decision

This skill should be used when the user asks about "technical decision", "what to use", "A vs B", "comparison analysis", "library selection", "architecture decision", "which one to use", "tradeoffs", "tech selection", "implementation approach", or needs deep analysis for technical decisions. Provides systematic multi-source research and synthesized recommendations.

中文简介正在准备中。当前显示原文说明。

集成工具Reddit
查看安装方法

包含的文件(3)

  • SKILL.md6.0 KB
  • references/evaluation-criteria.md4.9 KB
  • references/report-template.md4.5 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

Tech Decision - Deep Technical Decision Analysis

Skill for systematically analyzing technical decisions and deriving comprehensive conclusions.

Core Principle

Conclusion First: All reports present conclusion first, then provide evidence.

Use Cases

  • Library/framework selection (React vs Vue, Prisma vs TypeORM)
  • Architecture pattern decisions (Monolith vs Microservices, REST vs GraphQL)
  • Implementation approach selection (Server-side vs Client-side, Polling vs WebSocket)
  • Tech stack decisions (language, database, infrastructure, etc.)

Decision Workflow

Phase 1: Problem Definition

Clarify decision topic and context:

  1. Identify Topic: What needs to be decided?
  2. Identify Options: What are the choices to compare?
  3. Establish Criteria: What criteria to evaluate by?
    • Performance, learning curve, ecosystem, maintainability, cost, etc.
    • Set priority based on project characteristics
    • See references/evaluation-criteria.md for detailed criteria

Phase 2: Parallel Information Gathering

Gather information from multiple sources simultaneously. Must run in parallel:

┌─────────────────────────────────────────────────────────────┐
│  Run simultaneously (parallel with Task tool)               │
├─────────────────────────────────────────────────────────────┤
│  1. codebase-explorer agent                                 │
│     → Analyze existing codebase, identify patterns/constraints│
│                                                             │
│  2. docs-researcher agent                                   │
│     → Research official docs, guides, best practices        │
│                                                             │
│  3. Skill: dev-scan                                         │
│     → Gather community opinions (Reddit, HN, Dev.to, etc.)  │
│                                                             │
│  4. Skill: agent-council                                    │
│     → Gather various AI expert perspectives                 │
│                                                             │
│  5. [Optional] Context7 MCP                                 │
│     → Query latest docs per library                         │
└─────────────────────────────────────────────────────────────┘

Phase 3: Synthesis Analysis

Run tradeoff-analyzer agent with gathered information:

  • Organize pros/cons per option
  • Score by evaluation criteria
  • Organize conflicting opinions
  • Evaluate reliability (source-based)

Phase 4: Final Report Generation

Generate conclusion-first comprehensive report with decision-synthesizer agent (detailed template: references/report-template.md):

# Technical Decision Report: [Topic]

## Conclusion (Executive Summary)
**Recommendation: [Option X]**
[1-2 sentence key reason]

## Evaluation Criteria and Weights
| Criteria | Weight | Description |
|------|--------|------|
| Performance | 30% | ... |
| Learning Curve | 20% | ... |

## Option Analysis

### Option A: [Name]
**Pros:**
- [Pro 1] (Source: official docs)
- [Pro 2] (Source: Reddit r/webdev)

**Cons:**
- [Con 1] (Source: HN discussion)

**Good fit for:** [Scenario]

## Comprehensive Comparison
| Criteria | Option A | Option B | Option C |
|------|----------|----------|----------|
| Performance | ⭐⭐⭐ | ⭐⭐ | ⭐⭐⭐⭐ |
| **Total** | **X pts** | **Y pts** | **Z pts** |

## Recommendation Rationale
1. [Key reason 1 with source]
2. [Key reason 2 with source]

## Risks and Considerations
- [Consideration 1]
- [Consideration 2]

Resources Used

Agents (this plugin)

AgentRole
codebase-explorerAnalyze existing codebase, identify patterns/constraints
docs-researcherResearch official docs, guides, best practices
tradeoff-analyzerOrganize pros/cons, comparative analysis
decision-synthesizerGenerate conclusion-first final report

Existing Skills (call via Skill tool)

SkillPurposeHow to Call
dev-scanCommunity opinions from Reddit, HN, Dev.toSkill: dev-scan
agent-councilGather various AI expert perspectivesSkill: agent-council

Quick Execution Guide

1. Simple Comparison (A vs B)

User: "React vs Vue which is better?"

Execute:
1. Task docs-researcher + Task codebase-explorer (parallel)
2. Skill: dev-scan
3. Task tradeoff-analyzer
4. Task decision-synthesizer

2. Deep Analysis (complex decision)

User: "Thinking about which state management library to use"

Execute:
1. Task codebase-explorer (analyze current state)
2. Parallel:
   - Task docs-researcher (Redux, Zustand, Jotai, Recoil, etc.)
   - Skill: dev-scan
   - Skill: agent-council
3. Task tradeoff-analyzer
4. Task decision-synthesizer

Notes

  1. Provide Context: More accurate analysis with project characteristics, team size, existing tech stack
  2. Confirm Criteria: First confirm what criteria matter to user
  3. Show Reliability: Mark unclear or outdated sources
  4. Conclusion First: Always present conclusion first

评价

还没有评价。欢迎分享你的使用感受。

评论

无需登录即可发表。欢迎分享使用感受、提问,或说明在哪些环境下无法运行。

还没有评论。来写第一条吧。

提交后会立即公开。请勿填写邮箱、电话等个人信息。URL 最多 2 个。

0 / 1000 字

此表单受 reCAPTCHA 保护,适用 Google 的隐私权政策和服务条款。

同一仓库的技能

Analyze an open-source project from What/Why perspective (not how-it's-implemented). Use when the user says "/analyze-oss", "분석해줘 이 오픈소스", "이 레포 뭐하는거야", "analyze this repo", "what does X do", "이거 왜 쓰는거야", "이 라이브러리 분석", provides a GitHub URL and wants understanding, or asks to deeply understand an OSS project's purpose, value, target users, and usage flow. Clones the repo to ~/opensource-analysis/<repo-name>/ (git pull if already exists), dispatches parallel subagents per analysis lens, then synthesizes a What/Why-focused report in chat. Supports optional user-specific follow-up questions.

中文简介正在准备中。当前显示原文说明。

team-attention/hoyeon1732026年5月21日 更新

blueprint

免费

"/blueprint", "blueprint", "task graph", "contract derivation", "execution plan", "plan tasks from requirements", "contract-first planning" Turn requirements.md into an executable blueprint (plan.json + contracts.md). Five phases: Contracts → Tasks → Journeys → Verify Plan → Commit. Sits between /specify and /execute. Scope-adaptive (greenfield → bugfix). Uses hoyeon-cli (plan.json only; requirements.md is read as-is via Read tool).

中文简介正在准备中。当前显示原文说明。

team-attention/hoyeon1732026年5月21日 更新

Recon-first browser automation. Orchestrator explores the site first via chromux, saves a guide file with insights, then delegates execution to browser-explorer agent. Use when: "/browser-work", "브라우저 작업", "사이트에서 해줘", "웹에서 해줘", "LinkedIn에서", "크롬으로", "browser task", "automate this site".

中文简介正在准备中。当前显示原文说明。

team-attention/hoyeon1732026年5月21日 更新

bugfix

免费

Root cause based one-shot bug fix. debugger diagnosis → requirements.md generation → /execute. /bugfix "error description" Full investigation pipeline: debugger + gap-analyzer + standard verify. QA suggestion after successful fix.

中文简介正在准备中。当前显示原文说明。

team-attention/hoyeon1732026年5月21日 更新

check

免费

This skill should be used when the user wants to verify their changes before pushing, or update the project's rule checklists. Phase 1: validate changed files against .hoyeon/rules/ checklists and report PASS/WARN. Phase 2 (conditional): propose rule additions when unmatched patterns are detected. Essential before git push. Trigger phrases: "check", "checklist", "verify changes", "what did I miss", "pre-push check", "cascading changes", "any more changes needed", "update checklist", "update rules", "rules update", "체크", "체크리스트", "변경 확인", "빠뜨린 거 없나", "push 전 확인", "뭐 더 건드려야 해?", "연쇄 변경 확인", "규칙 갱신".

中文简介正在准备中。当前显示原文说明。

team-attention/hoyeon1732026年5月21日 更新

clarify

免费

"/clarify", "clarify this", "keep asking until clear", "remove ambiguity", "clarify requirements", "clarify design", "clarify the plan", "질문 계속해", "모호한 게 없게", "명확해질 때까지", "계속 물어봐", "Q&A로 정리", "질문답변 기록", "요구사항 명확화", "설계 명확화". Relentless ambiguity-resolution interview that records Q&A under .hoyeon/clarify/<topic>/ and hands off to specify/blueprint/docs when clear.

中文简介正在准备中。当前显示原文说明。

team-attention/hoyeon1732026年5月21日 更新

查看 team-attention 的全部技能

报告此技能的问题