bradygaster/squad is a tool that creates a human-directed team of AI development agents inside a project repository through GitHub Copilot. Developers use it to delegate work among persistent specialists such as frontend, backend, testing, and lead agents while retaining responsibility for decisions and review. The catalogue entries are the skills, agents, and instructions that define and coordinate those team members.
Borrowing it
Nothing to install: this file belongs to bradygaster/squad. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/bradygaster/squad/dev/.copilot/skills/architectural-proposals/SKILL.mdgit clone --depth 1 https://github.com/bradygaster/squadWrote 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/skills/bradygaster/squad/architectural-proposals)<a href="https://agentmods.dev/skills/bradygaster/squad/architectural-proposals"><img src="https://agentmods.dev/badge/skills/bradygaster/squad/architectural-proposals.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 | $0.00018 | $0.01429 |
| Opus 5 | $0.00009 | $0.00714 |
| Sonnet 5 | $0.00004 | $0.00286 |
| Haiku 4.5 | $0.00002 | $0.00143 |
Grade A, and why
architectural-proposals 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 7d 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.
This is a copy
100% identical to architectural-proposals — 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 — 152 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Context
Proposals create alignment before code is written. Cheaper to change a doc than refactor code. Use this pattern when:
- Architecture shifts invalidate existing assumptions
- Product direction changes require new foundation
- Multiple waves/milestones will be affected by a decision
- External dependencies (Copilot CLI, SDK APIs) change
Patterns
Proposal Structure (docs/proposals/)
Required sections:
- Problem Statement — Why current state is broken (specific, measurable evidence)
- Proposed Architecture — Solution with technical specifics (not hand-waving)
- What Changes — Impact on existing work (waves, milestones, modules)
- What Stays the Same — Preserve existing functionality (no regression)
- Key Decisions Needed — Explicit choices with recommendations
- Risks and Mitigations — Likelihood + impact + mitigation strategy
- Scope — What's in v1, what's deferred (timeline clarity)
Optional sections:
- Implementation Plan (high-level milestones)
- Success Criteria (measurable outcomes)
- Open Questions (unresolved items)
- Appendix (prior art, alternatives considered)
Tone Ceiling Enforcement
Always:
- Cite specific evidence (user reports, performance data, failure modes)
- Justify recommendations with technical rationale
- Acknowledge trade-offs (no perfect solutions)
- Be specific about APIs, libraries, file paths
Never:
- Hype ("revolutionary", "game-changing")
- Hand-waving ("we'll figure it out later")
- Unsubstantiated claims ("users will love this")
- Vague timelines ("soon", "eventually")
Wave Restructuring Pattern
When a proposal invalidates existing wave structure:
- Acknowledge the shift: "This becomes Wave 0 (Foundation)"
- Cascade impacts: Adjust downstream waves (Wave 1, Wave 2, Wave 3)
- Preserve non-blocking work: Identify what can proceed in parallel
- Update dependencies: Document new blocking relationships
Example (Interactive Shell):
- Wave 0 (NEW): Interactive Shell — blocks all other waves
- Wave 1 (ADJUSTED): npm Distribution — shell bundled in cli.js
- Wave 2 (DEFERRED): SquadUI — waits for shell foundation
- Wave 3 (ADJUSTED): Public Docs — now documents shell as primary interface
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.
- 7d ago First seen · 152 lines · 18 tokens per session scan A 45719db4cb9b
architectural-proposals is a skill published in the GitHub repository bradygaster/squad (3,160 stars, last pushed 3d ago), licensed MIT. It adds 18 tokens to every session and 1,429 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to architectural-proposals, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
coding-agents-farm
To orchestrate parallel coding-agent farms (Claude, Codex, Copilot, Gemini, etc.) on isolated git worktrees.
ralphctl-code-review-and-quality
Multi-phase code-quality skill — primary frame for the evaluator role in Execute, the architecture axis in Plan, and correctness/readability in Refine. Multi-axis code review with severity vocabulary. Use when you are the evaluator assessing a generator's output, and when reviewing any change before signalling…
ralphctl-test-driven-development
Execute-phase skill — write the failing test before the code that makes it pass; for bug fixes, this is the reproduction test itself. Use for any logic change, bug fix, or behavioural modification; for the full root-cause triage pipeline around an unexpected failure, see ralphctl-debugging-and-error-recovery.
ralphctl-idea-refinement
Ideation skill — refine a raw, unshaped idea into a sharp, buildable concept through divergent expansion (variation lenses like inversion, simplification, audience shift) followed by convergent stress-testing (user value, feasibility, differentiation), ending in a one-pager with explicit assumptions and a "Not Doing"…
squads-cli
Squads CLI — operate your AI workforce through the loop; intent in, reviewed work out. TRIGGER when running squads, dispatching agents, triaging the inbox, reading/writing memory, recording feedback, or checking status/usage.
squads-learn
Capture learnings after completing work. Use when finishing a task, fixing a bug, discovering a pattern, or learning something worth remembering for future sessions. Helps build institutional memory.