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Architectural Proposals

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bradygaster
architectural-proposals

How to write comprehensive architectural proposals that drive alignment before code is written

Overview

Publisherbradygaster
Repositorysquad
Skill namearchitectural-proposals
Stars
3.3K
Forks
506
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by bradygaster on GitHub. Read the source before you install it.

Installation

Install the Architectural Proposals AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/bradygaster/squad/tree/dev/packages/squad-cli/templates/skills/architectural-proposals
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/bradygaster/squad.git /tmp/squad
mkdir -p .claude/skills
cp -r /tmp/squad/packages/squad-cli/templates/skills/architectural-proposals .claude/skills/architectural-proposals
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Architectural Proposals in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Architectural Proposals on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Architectural Proposals is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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:

  1. Problem Statement — Why current state is broken (specific, measurable evidence)
  2. Proposed Architecture — Solution with technical specifics (not hand-waving)
  3. What Changes — Impact on existing work (waves, milestones, modules)
  4. What Stays the Same — Preserve existing functionality (no regression)
  5. Key Decisions Needed — Explicit choices with recommendations
  6. Risks and Mitigations — Likelihood + impact + mitigation strategy
  7. 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:

  1. Acknowledge the shift: "This becomes Wave 0 (Foundation)"
  2. Cascade impacts: Adjust downstream waves (Wave 1, Wave 2, Wave 3)
  3. Preserve non-blocking work: Identify what can proceed in parallel
  4. 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

Decision Framing

Format: "Recommendation: X (recommended) or alternatives?"

Components:

  • Recommendation (pick one, justify)
  • Alternatives (what else was considered)
  • Decision rationale (why recommended option wins)
  • Needs sign-off from (which agents/roles must approve)

Example:

### 1. Terminal UI Library: `ink` (recommended) or alternatives?

**Recommendation:** `ink`
**Alternatives:** `blessed`, raw readline
**Decision rationale:** Component model enables testable UI. Battle-tested ecosystem.

**Needs sign-off from:** Product Lead (product direction), Runtime Engineer (runtime performance)

Risk Documentation

Format per risk:

  • Risk: Specific failure mode
  • Likelihood: Low / Medium / High (not percentages)
  • Impact: Low / Medium / High
  • Mitigation: Concrete actions (measurable)

Example:

### Risk 2: SDK Streaming Reliability

**Risk:** SDK streaming events might drop messages or arrive out of order.
**Likelihood:** Low (SDK is production-grade).
**Impact:** High — broken streaming makes shell unusable.

**Mitigation:**
- Add integration test: Send 1000-message stream, verify all deltas arrive in order
- Implement fallback: If streaming fails, fall back to polling session state
- Log all SDK events to `.squad/orchestration-log/sdk-events.jsonl` for debugging

Examples

File references from interactive shell proposal:

  • Full proposal: docs/proposals/squad-interactive-shell.md
  • User directive: .squad/decisions/inbox/copilot-directive-2026-02-21T202535Z.md
  • Team decisions: .squad/decisions.md
  • Current architecture: docs/architecture/module-map.md, docs/prd-23-release-readiness.md

Key patterns demonstrated:

  1. Read user directive first (understand the "why")
  2. Survey current architecture (module map, existing waves)
  3. Research SDK APIs (exploration task to validate feasibility)
  4. Document problem with specific evidence (unreliable handoffs, zero visibility, UX mismatch)
  5. Propose solution with technical specifics (ink components, SDK session management, spawn.ts module)
  6. Restructure waves when foundation shifts (Wave 0 becomes blocker)
  7. Preserve backward compatibility (squad.agent.md still works, VS Code mode unchanged)
  8. Frame decisions explicitly (5 key decisions with recommendations)
  9. Document risks with mitigations (5 risks, each with concrete actions)
  10. Define scope (what's in v1 vs. deferred)

Anti-Patterns

Avoid:

  • ❌ Proposals without problem statements (solution-first thinking)
  • ❌ Vague architecture ("we'll use a shell") — be specific (ink components, session registry, spawn.ts)
  • ❌ Ignoring existing work — always document impact on waves/milestones
  • ❌ No risk analysis — every architecture has risks, document them
  • ❌ Unbounded scope — draw the v1 line explicitly
  • ❌ Missing decision ownership — always say "needs sign-off from X"
  • ❌ No backward compatibility plan — users don't care about your replatform
  • ❌ Hand-waving timelines ("a few weeks") — be specific (2-3 weeks, 1 engineer full-time)

Red flags in proposal reviews:

  • "Users will love this" (citation needed)
  • "We'll figure out X later" (scope creep incoming)
  • "This is revolutionary" (tone ceiling violation)
  • No section on "What Stays the Same" (regression risk)
  • No risks documented (wishful thinking)

Frequently asked questions

What does the Architectural Proposals AI skill do?

How to write comprehensive architectural proposals that drive alignment before code is written

Why use Architectural Proposals on TypingMind?

Because you install it once and use it with any model. Architectural Proposals is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Architectural Proposals in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/bradygaster/squad/tree/dev/packages/squad-cli/templates/skills/architectural-proposals. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Architectural Proposals?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Architectural Proposals?

As many as you like. As long as a model supports skills, you can use Architectural Proposals with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Architectural Proposals AI skill free?

Yes. It is published on GitHub by bradygaster under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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