You are an accessibility expert specializing in WCAG compliance, inclusive design, and assistive technology compatibility. Conduct audits, identify barriers, and provide remediation guidance.
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Use this skill when working with Conductor's context-driven development methodology, managing project context artifacts, or understanding the relationship between product.md, tech-stack.md, and workflow.md files.
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설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Guide for implementing and maintaining context as a managed artifact alongside code, enabling consistent AI interactions and team alignment through structured project documentation.
Context-Driven Development treats project context as a first-class artifact managed alongside code. Instead of relying on ad-hoc prompts or scattered documentation, establish a persistent, structured foundation that informs all AI interactions.
Key principles:
Follow the Context → Spec & Plan → Implement workflow:
Purpose: Captures product vision, goals, target users, and business context.
Contents:
Update when:
Purpose: Establishes brand voice, messaging standards, and communication patterns.
Contents:
Update when:
Purpose: Documents technology choices, dependencies, and architectural decisions.
Contents:
Update when:
Purpose: Establishes development practices, quality gates, and team workflows.
Contents:
Update when:
Purpose: Registry of all work units with status and metadata.
Contents:
Update when:
Ensure changes in one artifact reflect in related documents:
Before adding any new dependency:
After completing a feature track:
Before starting any track:
For new projects:
/conductor:setup to create all artifacts interactivelyCharacteristics:
For existing codebases:
/conductor:setup with existing codebase detectionCharacteristics:
conductor/
├── index.md # Navigation hub linking all artifacts
├── product.md # Product vision and goals
├── product-guidelines.md # Communication standards
├── tech-stack.md # Technology preferences
├── workflow.md # Development practices
├── tracks.md # Work unit registry
├── setup_state.json # Resumable setup state
├── code_styleguides/ # Language-specific conventions
│ ├── python.md
│ ├── typescript.md
│ └── ...
└── tracks/
└── <track-id>/
├── spec.md
├── plan.md
├── metadata.json
└── index.md
/conductor:setupBefore starting implementation on any track, validate context:
Avoid these context management mistakes:
Problem: Context documents become outdated and misleading. Solution: Update context as part of each track's completion process.
Problem: Information scattered across multiple locations. Solution: Use the defined artifact structure; resist creating new document types.
Problem: Relying on knowledge not captured in artifacts. Solution: If you reference something repeatedly, add it to the appropriate artifact.
Problem: One person maintains context without team input. Solution: Review context artifacts in pull requests; make updates collaborative.
Problem: Context becomes so detailed it's impossible to maintain. Solution: Keep artifacts focused on decisions that affect AI behavior and team alignment.
Configure your IDE to display context files prominently:
Consider pre-commit hooks that:
Include context validation in pipelines:
Conductor supports multi-session development through context persistence:
If interrupted mid-task:
[~] with note about stopping point아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.
로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.
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You are an accessibility expert specializing in WCAG compliance, inclusive design, and assistive technology compatibility. Conduct audits, identify barriers, and provide remediation guidance.
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Systematic improvement of existing agents through performance analysis, prompt engineering, and continuous iteration.
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Optimize multi-agent systems with coordinated profiling, workload distribution, and cost-aware orchestration. Use when improving agent performance, throughput, or reliability.
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Build production-ready LLM applications, advanced RAG systems, and intelligent agents. Implements vector search, multimodal AI, agent orchestration, and enterprise AI integrations. Use PROACTIVELY for LLM features, chatbots, AI agents, or AI-powered applications.
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Build production Apache Airflow DAGs with best practices for operators, sensors, testing, and deployment. Use when creating data pipelines, orchestrating workflows, or scheduling batch jobs.
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Migrate from AngularJS to Angular using hybrid mode, incremental component rewriting, and dependency injection updates. Use when upgrading AngularJS applications, planning framework migrations, or modernizing legacy Angular code.
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