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Architecture Decision Records

Community
Fmarzochi
architecture-decision-records

Capture architectural decisions made during AI coding sessions as structured ADRs. Auto-detects decision moments, records context, alternatives considered, and rationale. Maintains an ADR log so future developers understand why the codebase is shaped the way it is.

Overview

PublisherFmarzochi
RepositoryEGC
Skill namearchitecture-decision-records
Stars
68
Forks
49
Bundled files
Instructions only
LicenseApache-2.0
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 Fmarzochi on GitHub. Read the source before you install it.

Installation

Install the Architecture Decision Records 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/Fmarzochi/EGC/tree/main/skills/architecture/architecture-decision-records
  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/Fmarzochi/EGC.git /tmp/EGC
mkdir -p .claude/skills
cp -r /tmp/EGC/skills/architecture/architecture-decision-records .claude/skills/architecture-decision-records
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Architecture Decision Records 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 Architecture Decision Records 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 Architecture Decision Records 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 Decision Records

Capture architectural decisions as they happen during coding sessions. Instead of decisions living only in Slack threads, PR comments, or someone's memory, this skill produces structured ADR documents that live alongside the code.

When to Activate

  • User explicitly says "let's record this decision" or "ADR this"
  • User chooses between significant alternatives (framework, library, pattern, database, API design)
  • User says "we decided to..." or "the reason we're doing X instead of Y is..."
  • User asks "why did we choose X?" (read existing ADRs)
  • During planning phases when architectural trade-offs are discussed

ADR Format

Use the lightweight ADR format proposed by Michael Nygard, adapted for AI-assisted development:

markdown
# ADR-NNNN: [Decision Title]

**Date**: YYYY-MM-DD
**Status**: proposed | accepted | deprecated | superseded by ADR-NNNN
**Deciders**: [who was involved]

## Context

What is the issue that we're seeing that is motivating this decision or change?

[2-5 sentences describing the situation, constraints, and forces at play]

## Decision

What is the change that we're proposing and/or doing?

[1-3 sentences stating the decision clearly]

## Alternatives Considered

### Alternative 1: [Name]
- **Pros**: [benefits]
- **Cons**: [drawbacks]
- **Why not**: [specific reason this was rejected]

### Alternative 2: [Name]
- **Pros**: [benefits]
- **Cons**: [drawbacks]
- **Why not**: [specific reason this was rejected]

## Consequences

What becomes easier or more difficult to do because of this change?

### Positive
- [benefit 1]
- [benefit 2]

### Negative
- [trade-off 1]
- [trade-off 2]

### Risks
- [risk and mitigation]

Workflow

Capturing a New ADR

When a decision moment is detected:

  1. Initialize (first time only): if docs/adr/ does not exist, ask the user for confirmation before creating the directory, a README.md seeded with the index table header (see ADR Index Format below), and a blank template.md for manual use. Do not create files without explicit consent.
  2. Identify the decision: extract the core architectural choice being made
  3. Gather context: what problem prompted this? What constraints exist?
  4. Document alternatives: what other options were considered? Why were they rejected?
  5. State consequences: what are the trade-offs? What becomes easier/harder?
  6. Assign a number: scan existing ADRs in docs/adr/ and increment
  7. Confirm and write: present the draft ADR to the user for review. Only write to docs/adr/NNNN-decision-title.md after explicit approval. If the user declines, discard the draft without writing any files.
  8. Update the index: append to docs/adr/README.md

Reading Existing ADRs

When a user asks "why did we choose X?":

  1. Check if docs/adr/ exists: if not, respond: "No ADRs found in this project. Would you like to start recording architectural decisions?"
  2. If it exists, scan docs/adr/README.md index for relevant entries
  3. Read matching ADR files and present the Context and Decision sections
  4. If no match is found, respond: "No ADR found for that decision. Would you like to record one now?"

ADR Directory Structure

docs/
└── adr/
    ├── README.md              ← index of all ADRs
    ├── 0001-use-nextjs.md
    ├── 0002-postgres-over-mongo.md
    ├── 0003-rest-over-graphql.md
    └── template.md            ← blank template for manual use

ADR Index Format

markdown
# Architecture Decision Records

| ADR | Title | Status | Date |
|-----|-------|--------|------|
| [0001](0001-use-nextjs.md) | Use Next.js as frontend framework | accepted | 2026-01-15 |
| [0002](0002-postgres-over-mongo.md) | PostgreSQL over MongoDB for primary datastore | accepted | 2026-01-20 |
| [0003](0003-rest-over-graphql.md) | REST API over GraphQL | accepted | 2026-02-01 |

Decision Detection Signals

Watch for these patterns in conversation that indicate an architectural decision:

Explicit signals

  • "Let's go with X"
  • "We should use X instead of Y"
  • "The trade-off is worth it because..."
  • "Record this as an ADR"

Implicit signals (suggest recording an ADR: do not auto-create without user confirmation)

  • Comparing two frameworks or libraries and reaching a conclusion
  • Making a database schema design choice with stated rationale
  • Choosing between architectural patterns (monolith vs microservices, REST vs GraphQL)
  • Deciding on authentication/authorization strategy
  • Selecting deployment infrastructure after evaluating alternatives

What Makes a Good ADR

Do

  • Be specific: "Use Prisma ORM" not "use an ORM"
  • Record the why: the rationale matters more than the what
  • Include rejected alternatives: future developers need to know what was considered
  • State consequences honestly: every decision has trade-offs
  • Keep it short: an ADR should be readable in 2 minutes
  • Use present tense: "We use X" not "We will use X"

Don't

  • Record trivial decisions: variable naming or formatting choices don't need ADRs
  • Write essays: if the context section exceeds 10 lines, it's too long
  • Omit alternatives: "we just picked it" is not a valid rationale
  • Backfill without marking it: if recording a past decision, note the original date
  • Let ADRs go stale: superseded decisions should reference their replacement

ADR Lifecycle

proposed → accepted → [deprecated | superseded by ADR-NNNN]
  • proposed: decision is under discussion, not yet committed
  • accepted: decision is in effect and being followed
  • deprecated: decision is no longer relevant (e.g., feature removed)
  • superseded: a newer ADR replaces this one (always link the replacement)

Categories of Decisions Worth Recording

CategoryExamples
Technology choicesFramework, language, database, cloud provider
Architecture patternsMonolith vs microservices, event-driven, CQRS
API designREST vs GraphQL, versioning strategy, auth mechanism
Data modelingSchema design, normalization decisions, caching strategy
InfrastructureDeployment model, CI/CD pipeline, monitoring stack
SecurityAuth strategy, encryption approach, secret management
TestingTest framework, coverage targets, E2E vs integration balance
ProcessBranching strategy, review process, release cadence

Integration with Other Skills

  • Planner agent: when the planner proposes architecture changes, suggest creating an ADR
  • Code reviewer agent: flag PRs that introduce architectural changes without a corresponding ADR

Frequently asked questions

What does the Architecture Decision Records AI skill do?

Capture architectural decisions made during AI coding sessions as structured ADRs. Auto-detects decision moments, records context, alternatives considered, and rationale. Maintains an ADR log so future developers understand why the codebase is shaped the way it is.

Why use Architecture Decision Records on TypingMind?

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

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

Which AI models can use Architecture Decision Records?

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 Architecture Decision Records?

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

Is the Architecture Decision Records AI skill free?

Yes. It is published on GitHub by Fmarzochi under the Apache-2.0 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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