Awesome GitHub Copilot is a community collection of custom agents, instructions, skills, hooks, workflows, plugins, and configuration for GitHub Copilot. It helps Copilot users customize coding and development tasks. Catalogue entries are individual Copilot add-ons from this collection.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/github/awesome-copilotWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/github/awesome-copilot/hlbpa)<a href="https://agentmods.dev/agents/github/awesome-copilot/hlbpa"><img src="https://agentmods.dev/badge/agents/github/awesome-copilot/hlbpa.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00045 | $0.02740 |
| Opus 5 | $0.00023 | $0.01370 |
| Sonnet 5 | $0.00009 | $0.00548 |
| Haiku 4.5 | $0.00005 | $0.00274 |
Grade A, and why
High-Level Big Picture Architect (HLBPA) scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
3 near-identical copies found in the catalogue:
- High-Level Big Picture Architect (HLBPA) — 100% identical, 0 lines differ
- High-Level Big Picture Architect (HLBPA) — 100% identical, 0 lines differ
- hlbpa — 97% identical, 40 lines differ
How it starts
The opening of the file, as written. The whole thing — 234 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
- Simplicity: Strive for simplicity in design and documentation. Avoid unnecessary complexity and focus on the essential elements.
- Clarity: Ensure that all documentation is clear and easy to understand. Use plain language and avoid jargon whenever possible.
- Consistency: Maintain consistency in terminology, formatting, and structure throughout all documentation. This helps to create a cohesive understanding of the system.
- 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.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 3d ago First seen · 234 lines · 45 tokens per session scan A bba7a818480f
High-Level Big Picture Architect (HLBPA) is an agent published in the GitHub repository github/awesome-copilot (38,691 stars, last pushed today), licensed MIT. It adds 45 tokens to every session and 2,740 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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ndv-refactor
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ndv-review
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ndv-research
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application-security-analyst
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