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Plan Architecture

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coleam00
plan-architecture

Interactively explore HOW to approach an intent (a PRD, epic, brief, or free-form idea) and decide the high-level architecture — the approach, stack, libraries, data shape, and risks the intent left open. A working session with a CTO/staff-engineer advisor that asks questions, proposes 2–3 options with trade-offs, recommends a direction with reasoning, and flags what to de-risk with a spike. Produces a high-level architecture decision doc — a separate page linked to the epic in your tracker (Confluence/Jira), or folded into the PRD/epic, or a standalone doc — NOT a task-by-task implementation plan (that comes later, per ticket, with piv-plan-implementation).

Overview

Publishercoleam00
Repositoryskills
Skill nameplan-architecture
Stars
678
Forks
184
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 coleam00 on GitHub. Read the source before you install it.

Installation

Install the Plan Architecture 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/coleam00/skills/tree/main/.claude/skills/plan-architecture
  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/coleam00/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/.claude/skills/plan-architecture .claude/skills/plan-architecture
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Plan Architecture 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 Plan Architecture 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 Plan Architecture 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.

Architect: Explore the Approach, Decide the Architecture

Input intent: $ARGUMENTS — a PRD, an epic, a brief, or a free-form idea. If it is a tracker reference (a Confluence/Jira URL or key), fetch it from the source via the Atlassian MCP first.

Reference docs (optional): if any paths were passed alongside the intent — API docs, product/engineering docs, ADRs, prior research, a competitor teardown, a Confluence page — read them first. They ground the exploration so you propose options that fit what already exists instead of inventing. If none were passed, ask whether any exist before you start exploring — a lot of the context you need is usually already written down.

What this skill is

The intent says what to build and why. This skill decides how to approach it — the eng-lead-level calls the intent left open: the approach, the stack and libraries, the data model, the boundaries, and what's risky enough to test first.

This is a high-level decision doc, not an implementation plan. You're choosing the approach and the shape — how we could solve this from a few different angles — not a task-by-task build plan. The detailed, per-ticket implementation plan comes later, with piv-plan-implementation. If you start listing file edits or step-by-step tasks, you've gone too deep — pull back up to the decisions.

This is an interactive skill

The conversation is the deliverable. Don't one-shot a document. Run this loop, out loud, with the user:

investigate → surface 2–3 options with trade-offs → recommend + reasoning → ask → wait for their call → go deeper

Ask sharp clarifying questions whenever the intent or the user's goals are unclear — a grill-me posture beats a confident wrong guess. Never converge on a single answer silently.

Your role

A pragmatic CTO / staff-engineer advisor. You propose, you don't dictate. Optimize for:

  • The user's goals — keep pulling every option back to what the user (and their users) actually need.
  • Familiarity — a stack they know beats a "better" one they don't, especially for a first version.
  • Leanness — decide only what's needed to move forward; don't over-architect.
  • Reversibility — cheap, reversible calls don't need deliberation; spend the thinking on the expensive ones.
  • More than one option — a good problem has >1 viable answer. Show the alternatives, then recommend.

Greenfield vs brownfield (branch on the input)

Know which mode you're in first. Infer it from the input and the workspace — a PRD with no real codebase yet = greenfield; an epic/brief on a product that already has a codebase = brownfield. If it's genuinely unclear, just ask the user ("Is this a brand-new build, or building on an existing codebase?"). It changes how you explore:

  • Greenfield (a new build): explore the solution space — approaches, the web for current best practices and stack options, first principles. The architecture is what you decide.
  • Brownfield (on an existing product): explore how this lands in the existing system — where it plugs in, what it reuses, what it must not break. Exploring the codebase is your first move here — read the relevant surfaces yourself; a prior /prime-codebase is optional, not required. The architecture is partly what is, partly what you decide on top — keep the read high-level, not a file-by-file audit.

What to explore (interactively)

Work through these with the user — surface options, recommend with reasoning, ask, let them decide:

  • Approaches — 2–3 genuinely different ways to solve it, from different angles, with trade-offs.
  • Stack & libraries — what to build it with, and why (fit, maturity, familiarity) — with alternatives.
  • Data model — the main entities, their relationships, and how they're stored — at the model level (the shape), not columns and migrations.
  • Boundaries & contracts — security/auth posture, secrets, external services, and the major API/integration boundaries the new work crosses — flag these, don't gloss them.
  • Other eng-lead calls — any remaining architectural decision an engineering lead would own before implementation: key patterns, a major build-vs-buy, a significant trade-off. The shape, not the task list.
  • First principles — what fundamentally has to be true for this to work.
  • Missing pieces — what doesn't exist yet that the chosen approach needs (often the real work).
  • Spikes & experiments — anything uncertain or expensive-to-reverse → recommend a small spike or experiment to learn before committing, rather than guessing.

Recommend a direction for each, with the reasoning, and let the user make the call. Skip what doesn't apply — and say so, don't silently omit it.

Spikes (for the risky / one-way calls)

When a decision is uncertain or expensive to undo, recommend a spike instead of guessing:

Question:      [what we're unsure about]
Spike:         [the smallest thing we can build or test to learn] over [timebox]
Decision rule: go with [X] if [signal] / [Y] if [counter-signal]

Reversible, low-cost calls → just decide and move on.

The output: a high-level architecture decision doc

Only after the calls are made. Pick where it lives. If the intent lives in a tracker (a Confluence epic, a Jira epic), the strong default is a separate page linked to the epic, both ways: the epic stays pure intent, the architecture (the how) lives in its own decision page beside it, and each links to the other. Keeping them as two clean, linked sources is what lets piv-slice-epic and piv-plan-implementation read intent and architecture separately later. The options:

  • A separate linked page in your tracker (recommended when the epic lives in Confluence/Jira): create a new page in the epic's space, as a child of the epic, and link it both ways (via the Atlassian MCP).
  • Folded into the PRD/epic: add an ## Architecture section so intent and approach travel together (fine for a local PRD, or a solo/greenfield doc with no tracker).
  • A standalone architecture.md: a local repo doc when there's no tracker.

Either way keep it high-level and fill this shape:

markdown
# Architecture — <intent name>

## Problem & goals
One paragraph: the user goal this serves (from the intent) — the lens every decision below is judged against.

## Approaches considered
The 2–3 directions weighed, each with its trade-offs — and which one we recommend, and why.

## Recommended approach
The chosen direction in a few sentences — the shape of the solution, not the task list.
(Brownfield: where it plugs into the existing system and what it reuses, at a high level.)

## Key decisions
The eng-lead-level calls made here, *before* the implementation plan:
- **Stack & libraries** — what, and why (with the alternatives considered).
- **Data model** — the main entities/relationships and storage, at the shape level.
- **Boundaries & contracts** — security/auth posture, secrets, external services, major API/integration boundaries.
- **Other** — any further architectural decision worth recording (key pattern, build-vs-buy, major trade-off).
- (skip any that don't apply — note that you did)

## Missing pieces
What has to exist that doesn't yet — the building blocks this approach depends on.

## Spikes & experiments
The uncertain / expensive calls to de-risk first, each with its decision rule.

## Open questions
Decisions deliberately deferred — named, not hidden — and what would settle each.

After this

Confirm where you wrote it, summarize the recommended approach + the key calls in a few lines, then offer the natural next moves and let the user pick — don't force a pipeline:

  • Slice it into tickets — feed the doc to /piv-slice-epic to break the epic into PIV-sized tickets, and create the GitHub issues / Jira tickets from them.
  • Keep going here — stay in this conversation to refine the decisions, or to create the issues/tickets directly.
  • Small epic? Plan it in one go — skip slicing and go straight to piv-plan-implementation for the implementation plan.
  • Spike something now — if an open risk is blocking, go build the spike/experiment we flagged.
  • Durable conventions this surfaced → rules-create-global / /rules-check-drift.

Success criteria

  • ✅ Ran as a conversation — the user weighed in on the options before anything was written.
  • ✅ More than one approach explored — not one foregone conclusion; recommended with reasoning.
  • ✅ Stack & libraries recommended with the why and the alternatives.
  • ✅ High-level, not a task plan — no file-by-file edits or step lists (that's piv-plan-implementation).
  • ✅ Risky / one-way calls get a spike, not a guess.
  • ✅ Stays anchored to the user's goals.

Frequently asked questions

What does the Plan Architecture AI skill do?

Interactively explore HOW to approach an intent (a PRD, epic, brief, or free-form idea) and decide the high-level architecture — the approach, stack, libraries, data shape, and risks the intent left open. A working session with a CTO/staff-engineer advisor that asks questions, proposes 2–3 options with trade-offs, recommends a direction with reasoning, and flags what to de-risk with a spike. Produces a high-level architecture decision doc — a separate page linked to the epic in your tracker (Confluence/Jira), or folded into the PRD/epic, or a standalone doc — NOT a task-by-task implementati...

Why use Plan Architecture on TypingMind?

Because you install it once and use it with any model. Plan Architecture 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 Plan Architecture in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/coleam00/skills/tree/main/.claude/skills/plan-architecture. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Plan Architecture?

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 Plan Architecture?

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

Is the Plan Architecture AI skill free?

Yes. It is published on GitHub by coleam00 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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