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Oma Architecture

OrganizationPopular
first-fluke
oma-architecture

Evaluate system boundaries and architectural tradeoffs. Use for architecture decisions, design reviews, and ADRs.

Overview

Publisherfirst-fluke
Repositoryoh-my-agent
Skill nameoma-architecture
Stars
1.3K
Forks
149
Bundled files
7
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.

  • 7 bundled files

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

  • Open source

    Published by first-fluke on GitHub. Read the source before you install it.

Installation

Install the Oma 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/first-fluke/oh-my-agent.git /tmp/oh-my-agent
mkdir -p .claude/skills
cp -r /tmp/oh-my-agent/skills/oma-architecture .claude/skills/oma-architecture
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Oma 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 Oma 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 Oma 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.

Architecture Agent - Software Architecture Specialist

Scheduling

Goal

Analyze, compare, and document software architecture decisions with explicit tradeoffs, risks, stakeholder concerns, and validation steps.

Intent signature

  • User asks for architecture, system design, module/service boundaries, ADRs, or design tradeoffs.
  • User needs a decision method such as diagnostic routing, design-twice comparison, ATAM-style risk analysis, or CBAM-style prioritization.
  • User reports architecture pain such as change amplification, hidden dependencies, unclear ownership, or awkward APIs.
  • User needs an API versioning, deprecation, or published-contract evolution strategy.

When to use

  • Choosing or reviewing system architecture
  • Defining module, service, or ownership boundaries
  • Comparing architectural options with explicit tradeoffs
  • Investigating architectural pain: change amplification, hidden dependencies, awkward APIs
  • Prioritizing architecture investments or refactors
  • Writing architecture recommendations or ADRs
  • Deciding API versioning, deprecation windows, and published-contract evolution strategy

When NOT to use

  • Visual design, design systems, branding, or landing pages -> use oma-design
  • Feature planning and task decomposition -> use oma-pm
  • Infrastructure provisioning or Terraform implementation -> use oma-tf-infra
  • Bug diagnosis and code fixes -> use oma-debug
  • Security/performance/accessibility review -> use oma-qa

Expected inputs

  • Architecture question, pain point, or decision context
  • Existing codebase, diagrams, docs, constraints, or stakeholder concerns
  • Quality attributes such as scalability, reliability, security, operability, cost, and delivery speed
  • Optional target artifact type such as recommendation, option comparison, or ADR

Expected outputs

  • Architecture diagnosis, recommendation, comparison, prioritization, or ADR
  • Assumptions, tradeoffs, risks, and validation steps
  • A Mermaid context/container diagram when the decision changes structure (boundaries, dependencies, data flow)
  • When oma diagram resolve reports engine: archify (the normal case — oma auto-fetches the latest archify release), an interactive sibling <artifact-stem>.archify.json + .archify.html derived from that Mermaid (see _shared/conditional/diagram-engine.md)
  • Saved architecture artifacts under .agents/results/architecture/ when producing durable outputs
yaml
outputs:
  - name: architecture-artifact
    description: ADR, comparison, or recommendation written to durable storage when the run is meant to persist
    artifact: ".agents/results/architecture/*.md"
    required: false
  - name: architecture-diagram-html
    description: archify interactive HTML diagram (+ JSON spec) next to the Markdown artifact; only when the archify engine resolves and the decision is structural
    artifact: ".agents/results/architecture/*.archify.html"
    required: false

Dependencies

  • resources/execution-protocol.md for workflow
  • resources/methodology-selection.md for method choice
  • resources/stakeholder-synthesis.md when cross-cutting stakeholder consultation is justified
  • resources/output-templates.md for final artifact shapes
  • resources/api-evolution.md for published-contract versioning/deprecation decisions (MAP evolution patterns)
  • resources/migration-patterns.md for transition plans when the chosen architecture requires restructuring a live system
  • _shared/conditional/diagram-engine.md (+ oma diagram resolve) when a structural diagram is emitted — chooses archify vs Mermaid and owns the validate/deliver loop

Control-flow features

  • Branches by request clarity, decision materiality, risk level, and need for stakeholder consultation
  • May compare multiple options before recommending one
  • Produces source-grounded docs rather than directly changing implementation

Structural Flow

Entry

  1. Identify the architecture problem, decision, or pain signal.
  2. Gather existing constraints, source evidence, and stakeholder context.
  3. Read prior decisions in .agents/results/architecture/ — new decisions supersede old ones explicitly, never contradict them silently.
  4. Select the lightest sufficient method.

Scenes

  1. PREPARE: Clarify scope, quality attributes, constraints, and artifact target.
  2. ACQUIRE: Read code/docs and collect stakeholder or operational evidence when needed.
  3. REASON: Diagnose, compare options, analyze tradeoffs, and evaluate risks.
  4. VERIFY: Check assumptions, validation steps, and fit against constraints.
  5. FINALIZE: Produce recommendation, ADR, or architecture artifact.

Transitions

  • If the request is vague, use Diagnostic Mode before recommending.
  • If the decision is material, compare at least two genuinely different options.
  • If risk/quality attributes dominate, use ATAM-style analysis.
  • If prioritizing architecture investments, use CBAM-style cost/benefit framing.
  • If the decision is final, format it as an ADR.

Failure and recovery

  • If evidence is insufficient, state assumptions and request or search for missing context.
  • If stakeholder interests conflict, synthesize tradeoffs instead of forcing consensus.
  • If the task belongs to another domain, route to the relevant skill.

Exit

  • Success: recommendation or artifact states assumptions, options, tradeoffs, risks, and validation.
  • Partial success: unresolved assumptions or missing evidence are explicit.

Logical Operations

Actions

ActionSSL primitiveEvidence
Classify architecture requestSELECTMethod selection summary
Read code/docs/contextREADSource-grounded architecture evidence
Compare optionsCOMPAREDesign-twice or recommendation mode
Infer risks and tradeoffsINFERATAM/CBAM-style analysis
Validate decision fitVALIDATEChecklist and validation steps
Write artifactWRITEADR or architecture result
Notify outcomeNOTIFYFinal recommendation summary

Tools and instruments

  • Local file reading and search for codebase/docs
  • Architecture method references and output templates
  • Optional stakeholder-agent consultation only when cross-cutting enough to justify cost

Canonical workflow path

Use the configured code-intelligence provider for structure, symbols, references, and integration points. If unavailable, use native search and scoped reads:

bash
ls .agents/results/architecture/   # prior decisions — read before deciding
rg --files
rg "ADR|architecture|boundary|service|module|dependency|owner|interface" .

Then choose Diagnostic, Recommendation, Design-Twice, ATAM-style, CBAM-style, or ADR mode before writing the artifact.

Resource scope

ScopeResource target
CODEBASEArchitecture-relevant source files and docs
LOCAL_FS.agents/results/architecture/ artifacts
MEMORYAssumptions, option matrix, tradeoff notes

Preconditions

  • The architecture concern or decision boundary is identifiable.
  • Relevant context can be read or assumptions can be stated.

Effects and side effects

  • Creates architecture recommendations or ADR-style records.
  • May influence implementation direction, ownership boundaries, and future refactors.
  • Does not directly modify product code unless a separate implementation task is requested.

Guardrails

  1. Diagnose the architecture problem before selecting a method.
  2. Use the lightest sufficient methodology for the current decision.
  3. Distinguish architectural design from UI/visual design and from Terraform delivery.
  4. Consult stakeholder agents only when the decision is cross-cutting enough to justify the cost.
  5. Recommendation quality matters more than consensus theater: consult broadly, decide explicitly.
  6. Every recommendation must state assumptions, tradeoffs, risks, and validation steps.
  7. Be cost-aware by default: implementation cost, operational cost, team complexity, and future change cost.
  8. When a decision is material, compare at least two genuinely different options before recommending one.
  9. Save architecture artifacts to .agents/results/architecture/.
  10. Read prior artifacts in .agents/results/architecture/ before deciding; when replacing an old decision, mark it superseded rather than contradicting it.
  11. When a durable artifact is finalized, emit the architecture.adr-complete L1 decision event and verify the checkpoint (commands in resources/execution-protocol.md Step 7).

Method Selection Summary

  • Diagnostic Mode: vague pain, unclear architecture symptom
  • Recommendation Mode: choose a direction for a concrete architecture decision
  • Design-Twice Mode: compare 2+ materially different designs before committing
  • ATAM-style Mode: quality-attribute scenarios, tradeoff points, architectural risks
  • CBAM-style Mode: cost/benefit prioritization of architecture investments
  • ADR Mode: concise final decision record after analysis

References

  • Local code tools: ../_shared/core/code-intelligence.md (code search/navigation)

  • Execution steps (follow for the selected task): resources/execution-protocol.md

  • Checklist (run before handoff): resources/checklist.md

  • Method selection: resources/methodology-selection.md

  • Stakeholder protocol: resources/stakeholder-synthesis.md

  • Output templates: resources/output-templates.md

  • API evolution patterns (versioning, deprecation, lifecycle guarantees): resources/api-evolution.md

  • Migration/transition patterns (strangler fig, branch by abstraction, expand-contract): resources/migration-patterns.md

  • Context loading: ../_shared/core/context-loading.md

  • Task decomposition: ../_shared/core/difficulty-guide.md (unresolved scope or dependencies)

  • Clarification protocol: ../_shared/core/clarification-protocol.md

  • Quality principles: ../_shared/core/quality-principles.md

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 Oma Architecture AI skill do?

Evaluate system boundaries and architectural tradeoffs. Use for architecture decisions, design reviews, and ADRs.

Why use Oma Architecture on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/first-fluke/oh-my-agent/tree/main/skills/oma-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 Oma 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 Oma Architecture?

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

Is the Oma Architecture AI skill free?

Yes. It is published on GitHub by first-fluke 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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