Ln 24 Architecture Decision Recorder logo

Ln 24 Architecture Decision Recorder

Community
levnikolaevich
ln-24-architecture-decision-recorder

Records one architecture decision with alternatives, consequences and status; does not design the whole system.

Overview

Publisherlevnikolaevich
Repositoryclaude-code-skills
Skill nameln-24-architecture-decision-recorder
Stars
565
Forks
84
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 levnikolaevich on GitHub. Read the source before you install it.

Installation

Install the Ln 24 Architecture Decision Recorder 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/levnikolaevich/claude-code-skills.git /tmp/claude-code-skills
mkdir -p .claude/skills
cp -r /tmp/claude-code-skills/plugins/architecture-suite/skills/ln-24-architecture-decision-recorder .claude/skills/ln-24-architecture-decision-recorder
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ln 24 Architecture Decision Recorder 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 Ln 24 Architecture Decision Recorder 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 Ln 24 Architecture Decision Recorder 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 Recorder

Goal: Preserve the context, forces, alternatives, decision, and consequences of one architecturally significant choice in a compact durable record. Change only approved decision documentation; do not design the whole system, approve a decision silently, delete history, audit code, or implement the choice.

Execution contract: The checklist defines completion. Track each item internally as PENDING, PROVEN with evidence, CLEARED with evidence its condition is absent, or UNPROVEN with a gap; reading, delegation, or tool failure is not proof. Reconcile after each section. Before returning, resolve all PENDING, count only PROVEN and CLEARED, and apply verdict and approval rules to every gap. Preserve intent, scope, and existing authorization. Continue authorized work; ask only for consequential unresolved choices or required external approval. Scale depth to material risk without skipping checks. Preserve dependency and safety order; otherwise choose an appropriate verification method. Accept equivalent user or repository evidence; no other skill, named artifact, or complete lifecycle is required. Preserve source requirement and decision IDs. Bind reused evidence to relevant source versions, dirty changes, configuration, and environment; invalidate only affected claims. On continuation, reconcile task, authorization, current state, and unresolved evidence. For long work, return a compact continuation record or update an already authorized artifact; read-only skills do not persist it. Distinguish artifact readiness, verified behavior, and external-action authority. Prepare authorized work before required approval. If blocked by an instruction, cite its exact source and unresolved boundary; do not invent approval gates from caution.

Tool Routing

NeedPreferred capabilityFallback
Existing decision conventionRepository search and direct document readsUse the default path and compact format
Decision driversRequirements, architecture artifacts, implementation evidence, and stakeholder statementsMark unsupported drivers UNKNOWN
Alternatives and external claimsRepository evidence plus current official sourcesMark time-sensitive claims UNVERIFIED
Sequence and supersessionExisting filenames, indexes, and decision linksReturn BLOCKED rather than reuse a number
Document mutationMinimal patch to one approved decision record and necessary supersession linksReturn BLOCKED if authority or path is unclear

One record captures one decision. If the request contains independent decisions with different drivers or lifecycles, split them only with explicit approval.

Artifact Rules

  • Reuse the repository's established ADR convention when one exists.
  • Otherwise use docs/architecture/decisions/NNNN-<slug>.md with the next unused monotonic number.
  • Never renumber, delete, or rewrite the decision and rationale of historical records. Permit scoped status and supersession-link updates under the rules below; distinguish evolving proposed drafts from accepted history.
  • Default a new record to Proposed.
  • Use Accepted only after explicit confirmation from an authorized decision-maker.
  • A proposed replacement links to the current decision without changing its effective status. Mark the old record Superseded and link both directions only after the replacement is explicitly accepted; preserve historical content.
  • Keep the record short enough to review as a single decision.
  • Preserve the rationale a future maintainer needs to reconsider it safely.
  • Label retrospective records explicitly; do not imply that documentation created after implementation was prior approval.
  • Separate evidence needed before acceptance from monitoring required after adoption.
  • Prefer stable repository references over conversation, branch-local, or ephemeral links.

Checklist

1. Establish the Decision Contract

  • Resolve the exact decision, scope, owner or deciders, affected system, and why the choice is architecturally significant.
  • Read repository instructions, Git state, and existing decision conventions.
  • Search for duplicate, conflicting, deprecated, or superseding decisions before allocating a new record.
  • Confirm the request is one decision rather than a broad design or implementation plan.
  • Resolve the approved status; default to Proposed when acceptance is not explicit.

2. Gather Context and Forces

  • State the current context and problem in value-neutral language.
  • Extract business drivers, quality attributes, constraints, assumptions, and decision horizon from available evidence.
  • Identify affected boundaries, contracts, data, security, operations, cost, ownership, and migration implications.
  • Separate present facts from forecasts and preferences.
  • Record contradictions or missing evidence that could change the choice.

3. Evaluate Alternatives

  • Include the status quo and the simplest credible option unless they are demonstrably infeasible.
  • For an open choice, compare materially different credible alternatives; for retrospective records, include only evidenced historical options and mark missing rationale unknown.
  • Compare options against the same drivers: correctness, quality targets, complexity, reversibility, cost, operations, team fit, and evolution.
  • State why each rejected alternative loses in this context without claiming universal inferiority.
  • Record sensitivity or review triggers that would make a rejected option preferable later.

4. Record the Decision

  • Write title, status, date, deciders or owner, context, drivers, considered options, decision, consequences, validation, and review triggers.
  • State the decision in active, testable language and name what remains deliberately undecided.
  • Record positive, negative, and neutral consequences plus accepted risks.
  • Link affected requirements, designs, diagrams, interfaces, migration documents, or issues by stable repository reference.
  • Apply the Artifact Rules for proposed or accepted supersession; verify links and preserve prior rationale.

5. Validate and Report

  • Confirm the record contains one decision and can be understood without conversation history.
  • Confirm consequential claims have evidence or labelled assumptions/unknowns with validation actions; never invent historical rationale or rejected alternatives for a retrospective record.
  • Confirm Accepted was not assigned without explicit authority.
  • Identify the requirements, interfaces, and downstream decisions affected by this decision or its supersession; preserve the accepted/proposed distinction and historical rationale.
  • Use RECORDED when the record and status are valid; use INCOMPLETE when material context or required acceptance authority remains unresolved; a complete explicitly Proposed record does not require acceptance to be RECORDED; use BLOCKED for ambiguous scope, unsafe numbering, conflicting ownership, or no writable destination.

Self-Check

  • Reconcile before returning. Check item-level evidence, requirement coverage, contradictions, scope, verdict, and applicable cleanup. Correct the report or authorized artifacts. Reuse valid evidence; do not automatically rescan the repository or rerun successful commands. Repeat checks only for relevant changes, failures, or unresolved evidence. Disclose remaining gaps.

Output Contract

Report in the user's language, in this order; retain all five fields and state each fact once. Small results may use one line per field; omit empty tables and do not copy linked artifacts:

  1. Result: Skill-specific verdict and supported outcome.
  2. Scope: Reviewed/changed scope, exclusions, baseline, and material assumptions.
  3. Evidence: Skill-specific fields below; distinguish facts, inferences, and unverified claims. Link artifacts; use tables when useful.
  4. Verification: Checks/results, unavailable evidence, and applicable cleanup/external state.
  5. Completion: Checklist: X/Y complete; Incomplete: None or each UNPROVEN item's reason, outcome impact, and exact next action; residual risks and required decisions.

Skill-specific evidence: Artifact path, decision identity/status/owner, selected option, alternatives, consequences, evidence, and acceptance/validation needs. Report links or status changes to historical records, preserving their rationale; identify unresolved choices and review triggers without implying acceptance of a proposal.

Frequently asked questions

What does the Ln 24 Architecture Decision Recorder AI skill do?

Records one architecture decision with alternatives, consequences and status; does not design the whole system.

Why use Ln 24 Architecture Decision Recorder on TypingMind?

Because you install it once and use it with any model. Ln 24 Architecture Decision Recorder 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 Ln 24 Architecture Decision Recorder in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/levnikolaevich/claude-code-skills/tree/master/plugins/architecture-suite/skills/ln-24-architecture-decision-recorder. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Ln 24 Architecture Decision Recorder?

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 Ln 24 Architecture Decision Recorder?

As many as you like. As long as a model supports skills, you can use Ln 24 Architecture Decision Recorder with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Ln 24 Architecture Decision Recorder AI skill free?

Yes. It is published on GitHub by levnikolaevich 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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