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Architectural Decision Record

Organization
testdouble
architectural-decision-record

Create, extract, or convert an ADR (architectural decision record) using the ADR template. Use when creating new ADRs, extracting an ADR from existing documentation, converting a document into an ADR, recording an architecture or design decision, or updating the status of an existing ADR. Does not create or update enforceable coding standards or conventions — use coding-standard for that. Does not write feature or system documentation — use project-documentation instead.

Overview

Publishertestdouble
Repositoryhan
Skill namearchitectural-decision-record
Stars
320
Forks
26
Bundled files
2
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.

  • 2 bundled files

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

  • Open source

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

Installation

Install the Architectural Decision Record 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/testdouble/han/tree/main/han-documentation/skills/architectural-decision-record
  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/testdouble/han.git /tmp/han
mkdir -p .claude/skills
cp -r /tmp/han/han-documentation/skills/architectural-decision-record .claude/skills/architectural-decision-record
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Architectural Decision Record 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 Decision Record 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 Decision Record 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.

Create ADR

Operating Principles

  • YAGNI applies to ADRs themselves. Apply the evidence-based YAGNI rule from ../../references/yagni-rule.md. An ADR is worth recording only when there is a concrete forcing function today — a real decision the team is actively making, an existing code path or architectural choice that will be locked in by this record, an applicable regulation, a customer commitment, or a documented incident that drove the choice. ADRs about decisions that don't have to be made yet, "for future flexibility", "best practice says we should pick X", or symmetry with other ADRs ("we have one for auth, so we should have one for billing") are YAGNI candidates and the ADR should not be written. When proposed, recommend deferral with the trigger that would justify writing the ADR (a real decision arising, a real incident, a real regulation taking effect). The user always wins; the rule's job is to make the cost of writing speculative architectural records visible — every ADR is a future-reader's load and a pattern future agents will treat as committed.
  • The companion evidence rule applies to the ADR's supporting evidence. Apply the evidence rule from ../../references/evidence-rule.md to the citations that justify the ADR's decision and rejected alternatives. Name the trust class of each citation (codebase, web, provided); mark single-source web claims that drive the chosen option; and when no evidence at any tier supports a claimed trade-off, label it rather than presenting it as a weak preference.
  • The readability rule shapes the ADR's prose. Source the standard by invoking han-communication:readability-guidance and apply it as you write the ADR. Hold its default audience frame: a capable reader who did not make this decision and lacks your context. The frame governs how each section reads, never whether a required technical fact appears.

Project Context

  • CLAUDE.md: !find . -maxdepth 1 -name "CLAUDE.md" -type f
  • project-discovery.md: !find . -maxdepth 3 -name "project-discovery.md" -type f
  • personal config directory: !bash "${CLAUDE_PLUGIN_ROOT}/scripts/han-config-dir.sh" 2>/dev/null || echo "$HOME/.claude"
  • project .han/config.md: !cat .han/config.md 2>/dev/null || echo ""

As your first action, use the Read tool on .han/config.md inside the personal config directory path above. A read that returns no file is no personal configuration: continue silently. When that file or the project .han/config.md probe supplies content, apply it per config-rule.md, which governs precedence between the two files, relative-path resolution, and what to do with a file that reads but cannot be used.

Step 1: Determine Mode

Determine which mode to operate in based on the user's request:

ModeWhenInitial StatusThen
Creating newBuilding an ADR from scratch for a new or recent decisionproposed→ Step 2
Converting existingUser provides an existing document to convert into an ADRaccepted→ Step 2
Updating existingModifying an existing ADR (status change, superseding, adding notes)—Read the existing ADR, → Step 3

Step 2: Discover Project Structure

  1. Retrieve project config: Resolve project config: read CLAUDE.md's ## Project Discovery section for docs and ADR directories; fall back to project-discovery.md; fall back to Glob defaults (docs/, docs/adr/). Continue without any keys that remain unfound.

  2. Determine the ADR directory: Use the ADR directory if found; otherwise use {docs-dir}/adr/ if a docs directory was found; otherwise use docs/adr/. Run mkdir -p on the resolved directory to ensure it exists.

  3. Enumerate existing ADRs: Use Glob to find existing .md files in the ADR directory.

  4. Check existing ADR format: If existing ADRs were found, read one to understand the project's format. If it differs from template.md, ask the user whether to match the existing format or use this skill's template.

  5. Discover the filename hierarchy taxonomy: ADRs are organized by a one- or two-level hierarchy encoded in the filename so related decisions sort together in a directory listing. Discover the taxonomy that applies to this project — never hardcode it.

    • From existing filenames: If existing ADRs were enumerated, parse their filenames to extract the leading hierarchy segments already in use (e.g., auth-session-storage.md → top-level auth; auth-tokens-rotation.md → top-level auth, second-level tokens). Build a list of top-level prefixes and known second-level prefixes per top-level.
    • From project context: Read CLAUDE.md and project-discovery.md (paths from project context above) to identify the project's languages, frameworks, runtimes, subsystems, and bounded contexts. Each is a candidate top-level hierarchy (e.g., auth, billing, api, worker, postgres, terraform).
    • Carry forward to Step 4: the discovered top-level prefixes (existing + candidate) and any second-level prefixes already in use under each.

Step 3: Gather Context

  1. From the arguments and conversation, determine:
    • Topic/title — What is the ADR about?
    • Decision — What was decided and why?
    • Alternatives — What other options were considered?
    • Forcing function — What concrete trigger requires this decision now? Per ../../references/yagni-rule.md, an ADR requires evidence that the decision must be made today: an active engineering choice, a code path locking in, a regulation taking effect, a customer commitment, a documented incident driving the choice. If no forcing function exists, recommend deferring the ADR rather than writing it; surface the recommendation to the user with the trigger that would justify revisiting.
  2. If any of these are unclear or missing, use AskUserQuestion to clarify before writing. If the forcing function is the unclear one, surface that explicitly — "I don't see a current trigger forcing this decision; recommend deferring the ADR until {trigger}. Override?"

Explore the Codebase

Skip agent exploration if the user has already provided full context (converting or updating). When creating a new ADR with sparse context, launch 1-2 han-core:codebase-explorer agents to discover evidence. Use 1 agent for narrow decisions, 2 when the decision crosses multiple system areas. Explorer 1 focuses on code affected by the decision topic (current patterns, entry points, core logic). Explorer 2 focuses on existing ADRs, coding standards, and project docs (starting from the docs directory found in Step 2).

Compile Evidence

After agents complete (or if skipped), merge findings with user-provided context. Agent discovery items map to Context (current state of the codebase), Decision (why the chosen option fits), and Notes (key files table, cross-references). Merge duplicates and resolve conflicts between agents.

Dispatch Architectural Review

Skip this sub-step in update mode when only status is changing. Otherwise, launch review agents in parallel against the compiled evidence, the proposed decision, and the considered alternatives. Pass each agent the topic, the proposed decision, the alternatives, and the evidence compiled above.

  1. Launch architect agent — use han-core:software-architect when the decision is scoped to a single codebase or bounded context (module boundaries, class and interface design, abstraction points, refactoring paths). Use han-core:system-architect when the decision crosses service or bounded-context seams (integration patterns, data ownership across services, failure-domain topology, context-map relationships). If uncertain, prefer han-core:system-architect. Prompt: "Review the proposed decision against the compiled evidence. For the chosen option, identify structural or topological risks that the ADR's Consequences section should name. For each rejected alternative, identify the strongest case for it that the ADR's Decision section needs to rebut or concede. Return findings keyed to the ADR's Decision, Decision Drivers, and Consequences sections."

  2. Launch han-core:risk-analyst agent — prompt: "Assess the risk of adopting the chosen option versus staying with the current approach or adopting each rejected alternative. Score each on likelihood, severity, blast radius, and reversibility. Return findings keyed to the ADR's Consequences section, and flag any dimension where a rejected alternative scores better than the chosen option — the ADR needs to explain why."

  3. Launch han-core:junior-developer agent in artifact-review mode — prompt: "Read the proposed Context, Decision, Decision Drivers, and Considered Options as a generalist encountering this decision for the first time. Surface: unexplained jargon, assumptions baked into the Decision Drivers without evidence, alternatives dismissed without a reason a generalist would accept, and places where the ADR relies on context a future reader will not have. Return a short list of clarifying questions and must-answer gaps."

Merge the three agents' findings into the Decision, Decision Drivers, and Consequences sections before writing. Where an agent raises a must-answer gap that requires user judgment, surface it with a recommended resolution rather than resolving silently.

Step 4: Write the ADR

  1. Convert source document (if converting): Read the source document and map sections to ADR sections using the mapping at conversion-mapping.md.

  2. Copy the template from template.md

  3. File name and location: {top-level}[-{second-level}]-{kebab-case-title}.md — a one- or two-level hierarchy prefix followed by the decision's specific title. The hierarchy must come from the taxonomy discovered in Step 2.6, never invented or hardcoded.

    • Top-level (required): the highest-level grouping the decision belongs to (e.g., auth, billing, api, postgres). Reuse an existing top-level prefix from Step 2.6 when one fits; only introduce a new top-level when no existing prefix applies, and prefer one that matches a subsystem, bounded context, or technology already named in CLAUDE.md or project-discovery.md.
    • Second-level (optional): add only when the top-level has — or will plausibly grow — multiple ADRs that benefit from a sub-grouping (e.g., auth-tokens-…, auth-sessions-…). Reuse an existing second-level prefix from Step 2.6 when one fits. Skip the second level when the ADR is the only one (or one of a few) under its top-level.
    • Kebab-case-title (required): the specific decision, kebab-cased, distinct from the hierarchy prefix.
    • If the discovered taxonomy offers more than one reasonable placement, ask the user to choose before writing.
    • Place the file in the directory from Step 2.
  4. Fill in metadata: Status per Step 1 mode (proposed for new, accepted for converted; use deprecated or superseded when updating). Date Created / Last Updated: current date and time.

  5. Fill each required section following the template's HTML comments for guidance. Readability: invoke han-communication:readability-guidance to surface the shared readability standard into your context, then write the prose in each section against it — lead with the main point, give each paragraph one idea carried by its first sentence, number sequential steps and bullet non-sequential items, and disclose detail in layers. Keep the template's prescribed section structure (Context, Decision Drivers, Considered Options, Decision, Consequences, Notes); the rule governs the prose within them, and its descriptive-heading rule applies to any sub-headings you add, not the prescribed section names.

  6. Notes section must include:

    • Key files table — important files related to this decision:
      FilePurpose
      path/to/fileDescription
    • Cross-references — links to related ADRs (e.g., See also [Soft Deletes](./data-soft-deletes.md))
    • Related docs — links to related docs outside the ADR directory
  7. If updating an existing ADR: Update Status, Last Updated, and add notes about what changed. If superseding, cross-reference the new ADR and set the old ADR's status to superseded.

  8. Handle source document (conversions only): If the source document is fully subsumed, delete it and update references in CLAUDE.md, AGENTS.md, and other markdown files. If it retains useful content, add a link to the new ADR and remove migrated sections.

Step 5: Integration

  1. Add a See reference in the relevant section of any existing CLAUDE.md or AGENTS.md, following the pattern of existing ADR references. Place it near the feature or component the ADR describes.
  2. Search for related documentation (other ADRs, coding standards, feature docs) and add cross-references in the new ADR's Notes section.
  3. Add back-references from related docs where they add value.
  4. If converting (Step 4), confirm all old references to the source document are updated.

Step 6: Verification

Read back the ADR file and confirm:

  1. All metadata fields are filled (no {placeholder} values remain) and template structure from template.md was followed
  2. All required sections (Context, Decision Drivers, Considered Options, Decision, Consequences, Notes) have substantive content, and Notes includes a key files table with paths verified by Glob
  3. Cross-references in the ADR point to documents that exist
  4. Agent configuration file references (CLAUDE.md/AGENTS.md) correctly point to the new ADR
  5. If converting: source document was handled (deleted or updated with link)
  6. Fix any issues found

Step 7: Readability Self-Check

Run the standardized readability self-check (the shared standard is in your context from han-communication:readability-guidance) over the ADR's prose regions only — never inside code fences, diagram bodies, or citation identifiers. This skill runs no rewrite pass, so this self-check is the fidelity guard on the output; the fidelity criterion is not optional. Confirm each criterion and fix any failure before presenting:

Run the readability rule's standardized self-check, which is already in your context from the readability-guidance invocation above. Correct every failure before presenting. Its fidelity criterion is not optional: the standard governs how the content is said, and drops a required fact only when the reader asked for less and losing it would not change what they do next.

The descriptive-heading criterion applies to sub-headings you added, not to the section names the ADR template prescribes.

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 Architectural Decision Record AI skill do?

Create, extract, or convert an ADR (architectural decision record) using the ADR template. Use when creating new ADRs, extracting an ADR from existing documentation, converting a document into an ADR, recording an architecture or design decision, or updating the status of an existing ADR. Does not create or update enforceable coding standards or conventions — use coding-standard for that. Does not write feature or system documentation — use project-documentation instead.

Why use Architectural Decision Record on TypingMind?

Because you install it once and use it with any model. Architectural Decision Record 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 Decision Record in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/testdouble/han/tree/main/han-documentation/skills/architectural-decision-record. 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 Architectural Decision Record?

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 Decision Record?

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

Is the Architectural Decision Record AI skill free?

Yes. It is published on GitHub by testdouble 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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