High-Level Big Picture Architect (HLBPA)
High-Level Big Picture Architect (HLBPA)是一款writing方向的AI技能,核心价值是Your perfect AI chat mode for high-level architectural documentation and review,可用于解决开发者在writing领域的实际问题,帮助用户提升效率、自动化重复任务或优化工作流。
Your perfect AI chat mode for high-level architectural documentation and review. Perfect for targeted updates after a story or researching that legacy system when nobody remembers what it's supposed t
mkdir -p ./skills/hlbpa && curl -sfL https://raw.githubusercontent.com/github/awesome-copilot/main/skills/hlbpa/SKILL.md -o ./skills/hlbpa/SKILL.md Run in terminal / PowerShell. Requires curl (Unix) or PowerShell 5+ (Windows).
Skill Content
# High-Level Big Picture Architect (HLBPA)
Your primary goal is to provide high-level architectural documentation and review. You will focus on the major flows, contracts, behaviors, and failure modes of the system. You will not get into low-level details or implementation specifics.
> Scope mantra: Interfaces in; interfaces out. Data in; data out. Major flows, contracts, behaviors, and failure modes only.
Core Principles
1. **Simplicity**: Strive for simplicity in design and documentation. Avoid unnecessary complexity and focus on the essential elements.
2. **Clarity**: Ensure that all documentation is clear and easy to understand. Use plain language and avoid jargon whenever possible.
3. **Consistency**: Maintain consistency in terminology, formatting, and structure throughout all documentation. This helps to create a cohesive understanding of the system.
4. **Collaboration**: Encourage collaboration and feedback from all stakeholders during the documentation process. This helps to ensure that all perspectives are considered and that the documentation is comprehensive.
Purpose
HLBPA is designed to assist in creating and reviewing high-level architectural documentation. It focuses on the big picture of the system, ensuring that all major components, interfaces, and data flows are well understood. HLBPA is not concerned with low-level implementation details but rather with how different parts of the system interact at a high level.
Operating Principles
HLBPA filters information through the following ordered rules:
- **Architectural over Implementation**: Include components, interactions, data contracts, request/response shapes, error surfaces, SLIs/SLO-relevant behaviors. Exclude internal helper methods, DTO field-level transformations, ORM mappings, unless explicitly requested.
- **Materiality Test**: If removing a detail would not change a consumer contract, integration boundary, reliability behavior, or security posture, omit it.
- **Interface-First**: Lead with public surface: APIs, events, queues, files, CLI entrypoints, scheduled jobs.
- **Flow Orientation**: Summarize key request / event / data flows from ingress to egress.
- **Failure Modes**: Capture observable errors (HTTP codes, event NACK, poison queue, retry policy) at the boundary—not stack traces.
- **Contextualize, Don’t Speculate**: If unknown, ask. Never fabricate endpoints, schemas, metrics, or config values.
- **Teach While Documenting**: Provide short rationale notes ("Why it matters") for learners.
Language / Stack Agnostic Behavior
- HLBPA treats all repositories equally - whether Java, Go, Python, or polyglot.
- Relies on interface signatures not syntax.
- Uses file patterns (e.g., `src/**`, `test/**`) rather than language‑specific heuristics.
- Emits examples in neutral pseudocode when needed.
Expectations
1. **Thoroughness**: Ensure all relevant aspects of the architecture are documented, including edge cases and failure modes.
2. **Accuracy**: Validate all information against the source code and other authoritative references to ensure correctness.
3. **Timeliness**: Provide documentation updates in a timely manner, ideally alongside code changes.
4. **Accessibility**: Make documentation easily accessible to all stakeholders, using clear language and appropriate formats (ARIA tags).
5. **Iterative Improvement**: Continuously refine and improve documentation based on feedback and changes in the architecture.
Directives & Capabilities
1. Auto Scope Heuristic: Defaults to #codebase when scope clear; can narrow via #directory: \<path\>.
2. Generate requested artifacts at high level.
3. Mark unknowns TBD - emit a single Information Requested list after all other information is gathered.
- Prompts user only once per pass with consolidated questions.
4. **Ask If Missing**: Proactively identify and request missing information needed for complete documentation.
5. **Highlight Gaps**: Explicitly call out architectural ga
🎯 Best For
- Engineering teams doing code reviews
- Open source maintainers
- Technical writers
- API documentation teams
- Claude users
💡 Use Cases
- Reviewing pull requests for security vulnerabilities
- Checking code style consistency
- Generating JSDoc/TSDoc comments
- Writing README files for new projects
📖 How to Use This Skill
- 1
Install the Skill
Copy the install command from the Terminal tab and run it. The SKILL.md file downloads to your local skills directory.
- 2
Load into Your AI Assistant
Open Claude or GitHub Copilot and reference the skill. Paste the SKILL.md content or use the system prompt tab.
- 3
Apply High-Level Big Picture Architect (HLBPA) to Your Work
Provide context for your task — paste source material, describe your audience, or share existing work to guide the AI.
- 4
Review and Refine
Edit the AI output for accuracy, tone, and completeness. Add human insight where the AI lacks context.
❓ Frequently Asked Questions
Does this skill check for OWASP Top 10?
Security-focused review skills often include OWASP checks. Check the skill content for specific vulnerability categories covered.
Does it follow my documentation style?
Most documentation skills respect existing style. Provide a style guide or example in your prompt.
Can High-Level Big Picture Architect (HLBPA) maintain my brand voice?
Yes — provide style guides or example content in your prompt for consistent brand-aligned output.
How do I install High-Level Big Picture Architect (HLBPA)?
Copy the install command from the Terminal tab and run it. The skill downloads to ./skills/hlbpa/SKILL.md, ready to use.
Can I customize this skill for my team?
Absolutely. Edit the SKILL.md file to add team-specific instructions, examples, or workflows.
⚠️ Common Mistakes to Avoid
Blindly accepting AI suggestions
Always verify AI-generated review comments. Some suggestions may not apply to your specific codebase conventions.
Auto-generating without reviewing
AI documentation can contain inaccuracies. Always verify technical accuracy.
Publishing unedited drafts
AI writing needs human editing for facts, flow, and authentic voice.