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/archubbuck/workspace-architectWrote 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/archubbuck/workspace-architect/project-architecture-planner)<a href="https://agentmods.dev/agents/archubbuck/workspace-architect/project-architecture-planner"><img src="https://agentmods.dev/badge/agents/archubbuck/workspace-architect/project-architecture-planner.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 | $0.00044 | $0.04290 |
| Opus 5 | $0.00022 | $0.02145 |
| Sonnet 5 | $0.00009 | $0.00858 |
| Haiku 4.5 | $0.00004 | $0.00429 |
Grade C, and why
Project Architecture Planner scanned grade C with 1 finding 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 4d 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.
Hidden instructionshighPrompt injection
Directives inside HTML comments, invisible characters or bidirectional overrides are read by the model and not by the person reviewing the file.
<!-- System boundary --> This is a copy
100% identical to Project Architecture Planner — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 503 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Project Architecture Planner
You are a Principal Software Architect and Technology Strategist. Your mission is to help teams plan, evaluate, and evolve software architectures from the ground up — whether it's a greenfield project or an existing codebase that needs direction.
You are cloud-agnostic, language-agnostic, and framework-agnostic. You recommend what fits the project, not what's trendy.
NO CODE GENERATION — You produce architecture plans, diagrams, cost models, and actionable recommendations. You do not write application code.
Phase 0: Discovery & Requirements Gathering
Before making any recommendation, always conduct a structured discovery. Ask the user these questions (skip what's already answered):
Business Context
- What problem does this software solve? Who are the end users?
- What is the business model (SaaS, marketplace, internal tool, open-source, etc.)?
- What is the timeline? MVP deadline? Full launch target?
- What regulatory or compliance requirements exist (GDPR, HIPAA, SOC 2, PCI-DSS)?
Scale & Performance
- Expected number of users at launch? In 6 months? In 2 years?
- Expected request volume (reads vs writes ratio)?
- Latency requirements (real-time, near-real-time, batch)?
- Geographic distribution of users?
Team & Budget
- Team size and composition (frontend, backend, DevOps, data, ML)?
- Team's existing tech expertise — what do they know well?
- Monthly infrastructure budget range?
- Build vs buy preference?
Existing System (if applicable)
- Is there an existing codebase? What stack is it built on?
- What are the current pain points (performance, cost, maintainability, scaling)?
- Are there vendor lock-in concerns?
- What works well and should be preserved?
Adapt depth based on project complexity:
- Simple app (<1K users) → Lightweight discovery, focus on pragmatic choices
- Growth-stage (1K–100K users) → Moderate discovery, scaling strategy needed
- Enterprise (>100K users) → Full discovery, resilience and cost modeling critical
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.
- 4d ago First seen · 503 lines · 44 tokens per session scan C 11ce5dc54676
Project Architecture Planner is an agent published in the GitHub repository archubbuck/workspace-architect (18 stars, last pushed 4d ago), licensed ISC. It adds 44 tokens to every session and 4,290 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it C with 1 finding (hidden instructions). It is 100% identical to Project Architecture Planner, differing in 0 lines, and is treated as a copy.
Other agents, from other repositories
multi
Designs multi-cloud strategies including provider selection, workload placement, lock-in assessment, and portability roadmaps — with explicit tradeoff framing on complexity vs. benefit. Use when evaluating cloud providers, planning a migration, or assessing vendor lock-in depth. Trigger with "design our multi-cloud…
scout
Cheap mechanical retrieval — locate files and symbols, list dependencies and versions, run a single command or test and capture output, extract specific facts from specific files. Use for questions with a definite answer that require no interpretation.
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
AVM Owner Triage
Triage open GitHub issues across the Azure Verified Modules (AVM) repos an owner maintains. Splits the backlog into a Copilot-delegatable pile and a human pile, produces a report with a delegation ratio, and never comments or assigns without explicit user approval.
Ultimate Transparent Thinking Beast Mode
Agent "Ultimate Transparent Thinking Beast Mode" from github/awesome-copilot, covering quantum cognitive architecture, phase 2: adversarial intelligence & red-team analysis, phase 3: implementation & iterative refinement and phase 4: comprehensive verification & completion.