Deepen Architecture logo

Deepen Architecture

Community
danielvm-git
deepen-architecture

Find deepening opportunities in a codebase, informed by the domain language in specs/tech-architecture/tech-stack.md and the decisions in specs/adr/. Use when the user wants to improve architecture, find refactoring opportunities, consolidate tightly-coupled modules, or make a codebase more testable and AI-navigable.

Overview

Publisherdanielvm-git
Repositorybigpowers
Skill namedeepen-architecture
Stars
206
Forks
18
Bundled files
3
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.

  • 3 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by danielvm-git on GitHub. Read the source before you install it.

Installation

Install the Deepen 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.
  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/danielvm-git/bigpowers.git /tmp/bigpowers
mkdir -p .claude/skills
cp -r /tmp/bigpowers/skills/deepen-architecture .claude/skills/deepen-architecture
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Deepen 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 Deepen 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 Deepen 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.

Deepen Architecture

Surface architectural friction and propose deepening opportunities — refactors that turn shallow modules into deep ones. The aim is testability and AI-navigability.

Distinct from define-language and model-domain: Use this skill to find module-level refactoring opportunities in the codebase. Use define-language to produce a canonical glossary of terms. Use model-domain to stress-test a plan through a domain-model interview.

HARD GATE — Deep modules must solve a forcing function, not just be "nice abstractions." If you cannot articulate why the abstraction exists, it is premature.

Glossary

Use these terms exactly in every suggestion. Consistent language is the point — don't drift into "component," "service," "API," or "boundary." Full definitions in LANGUAGE.md.

  • Module — anything with an interface and an implementation (function, class, package, slice).
  • Interface — everything a caller must know to use the module: types, invariants, error modes, ordering, config. Not just the type signature.
  • Implementation — the code inside.
  • Depth — leverage at the interface: a lot of behaviour behind a small interface. Deep = high leverage. Shallow = interface nearly as complex as the implementation.
  • Seam — where an interface lives; a place behaviour can be altered without editing in place. (Use this, not "boundary.")
  • Adapter — a concrete thing satisfying an interface at a seam.
  • Leverage — what callers get from depth.
  • Locality — what maintainers get from depth: change, bugs, knowledge concentrated in one place.

Key principles (see LANGUAGE.md for the full list):

  • Deletion test: imagine deleting the module. If complexity vanishes, it was a pass-through. If complexity reappears across N callers, it was earning its keep.
  • The interface is the test surface.
  • One adapter = hypothetical seam. Two adapters = real seam.

This skill is informed by the project's domain model — specs/tech-architecture/tech-stack.md and any specs/adr/. The domain language gives names to good seams; ADRs record decisions the skill should not re-litigate. See CONTEXT-FORMAT.md and ADR-FORMAT.md.

Process

1. Explore

Read existing documentation first:

  • specs/tech-architecture/tech-stack.md (or specs/tech-architecture/tech-stack.md + each specs/tech-architecture/tech-stack.md in a multi-context repo)
  • Relevant ADRs in specs/adr/

If any of these files don't exist, proceed silently — don't flag their absence or suggest creating them upfront.

Look-here-first (churn heuristic): Before organic exploration, rank candidate modules by recent commit frequency. High-churn files are architectural friction magnets — start there.

bash
bash scripts/bp-churn-rank.sh --since 90.days --limit 20

Then use the Agent tool with subagent_type=Explore to walk the codebase. Don't follow rigid heuristics — explore organically and note where you experience friction:

  • Where does understanding one concept require bouncing between many small modules?
  • Where are modules shallow — interface nearly as complex as the implementation?
  • Where have pure functions been extracted just for testability, but the real bugs hide in how they're called?
  • Where do tightly-coupled modules leak across their seams?
  • Which parts of the codebase are untested, or hard to test through their current interface?

Apply the deletion test to anything you suspect is shallow.

2. Module Depth score

For each candidate module, assign a Module Depth score (1–5, Ousterhout):

ScoreMeaning
1Shallow — interface complexity ≈ implementation
3Balanced
5Deep — small interface, substantial hidden behavior

Include the score in each candidate row. Prioritize score ≤ 2 for deepening.

3. Present candidates

Present a numbered list of deepening opportunities. For each candidate:

  • Files — which files/modules are involved
  • Problem — why the current architecture is causing friction
  • Solution — plain English description of what would change
  • Benefits — explained in terms of locality and leverage, and how tests would improve

Use specs/tech-architecture/tech-stack.md vocabulary for the domain, and LANGUAGE.md vocabulary for the architecture.

ADR conflicts: if a candidate contradicts an existing ADR, only surface it when the friction is real enough to warrant revisiting the ADR. Mark it clearly. Don't list every theoretical refactor an ADR forbids.

Do NOT propose interfaces yet. Ask the user: "Which of these would you like to explore?"

4. Grilling loop

Once the user picks a candidate, drop into a grilling conversation. Walk the design tree with them — constraints, dependencies, the shape of the deepened module, what sits behind the seam, what tests survive.

Side effects happen inline as decisions crystallize:

  • Naming a deepened module after a concept not in specs/tech-architecture/tech-stack.md? Add the term to specs/tech-architecture/tech-stack.md — same discipline as model-domain (see CONTEXT-FORMAT.md). Create the file lazily if it doesn't exist.
  • Sharpening a fuzzy term during the conversation? Update specs/tech-architecture/tech-stack.md right there.
  • User rejects the candidate with a load-bearing reason? Offer an ADR, framed as: "Want me to record this as an ADR so future architecture reviews don't re-suggest it?" Only offer when the reason would actually be needed by a future explorer. See ADR-FORMAT.md.
  • Want to explore alternative interfaces for the deepened module? See INTERFACE-DESIGN.md.

5. Import-boundary hygiene (e45s14)

When a deepening move splits or merges modules, update specs/import-boundaries.json (Playwright DEPS.list pattern) — declare which scripts/lib/*.sh files may source which peers. CI enforces via:

bash
bash scripts/check-import-boundaries.sh

Run the check before proposing cross-module source edges. Convention docs alone do not authorize new imports; the allowlist must list them.

Verify

→ verify: test -f specs/import-boundaries.json && bash scripts/check-import-boundaries.sh

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Deepen Architecture AI skill do?

Find deepening opportunities in a codebase, informed by the domain language in specs/tech-architecture/tech-stack.md and the decisions in specs/adr/. Use when the user wants to improve architecture, find refactoring opportunities, consolidate tightly-coupled modules, or make a codebase more testable and AI-navigable.

Why use Deepen Architecture on TypingMind?

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

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

Which AI models can use Deepen 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 Deepen Architecture?

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

Is the Deepen Architecture AI skill free?

Yes. It is published on GitHub by danielvm-git 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇