agentleFS
tovrleaftovrleaf/openkata/skills/create-adr/SKILL.md

create-adr

Skill1 starsChanged 13 days agoMITView on GitHub

Detects architectural decisions in conversation and guides creation of Architecture Decision Records. Activate when the user is making, discussing, or debating: technology choices (languages, frameworks, databases, cloud services), structural patterns (monolith vs microservices, event-driven, CQRS, API design), cross-cutting conventions (error handling, logging, authentication, API versioning), hard-to-reverse decisions (data models, public API contracts, infrastructure topology), or deviations from existing standards. Also activate when the user explicitly asks to create an ADR.

16 other projects use a version of this.

Adopted by

Most recently taken up by

Used with

Projects with it also use create-plan.

Other files people use alongside this one.

Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
  • JetBrainsSkill

    create-plan

    Research the codebase and create an implementation plan with architecture notes, design document, and track decomposition. Use when starting a new feature or large change.

    Also in 3 projects that use this

The file

We split this skill into the rules it gives an agent

8 rules. 0 are common, 3 are distinctive, and 5 are very specific to this project.

  • 0

    Common

    Most of the 13 edited versions we read keep these.

  • 3

    Distinctive

    This author's own choices. Worth a look.

  • 5

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    Update the old ADR's status to SUPERSEDED and link to the new one.

    line 29
  2. Very specific:

    Read existing ADRs in docs/adr/ for related or superseded decisions

    line 43
  3. Very specific:

    Check the tech stack (package.json, go.mod, requirements.txt, etc.)

    line 44
  4. Distinctive:

    Don't ask the user what you can look up yourself.

    line 48
  5. Very specific:

    Always set status to PROPOSED — never ACCEPTED or DECIDED.

    line 59
  6. Very specific:

    Check existing files in docs/adr/ to determine the next number

    line 74
  7. Distinctive:

    Before finalizing an ADR, verify:

    line 86
  8. Distinctive:

    When a decision directly maps to code changes, add an Implementation Plan section describing affected paths and patterns to follow.

    line 104

“Common” is measured: kept by at least 6 in 10 of the 13 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Create ADR

ADR Lifecycle

PROPOSED → ACCEPTED → (SUPERSEDED or DEPRECATED)
  • New ADRs always start as PROPOSED — including ADRs that supersede an existing one. Never set a new ADR to ACCEPTED.
  • For material changes to a decision, write a new ADR that supersedes the old one. The new ADR's status is PROPOSED. Update the old ADR's status to SUPERSEDED and link to the new one.
  • Minor corrections (typos, missing details) can be edited in place.

Workflow

  1. Detect — An architectural decision is in play.
  2. Propose — Ask the user: "This looks like an architectural decision. Would you like to record it as an ADR?" If this is the first ADR in the session, also ask: "Would you like me to walk through the details one question at a time, or should I draft it from what I already know?"
  3. Explore the codebase — Before asking the user anything, gather what you can on your own:

    • Read existing ADRs in docs/adr/ for related or superseded decisions
    • Check the tech stack (package.json, go.mod, requirements.txt, etc.)
    • Find code patterns related to the decision area
    • Identify affected files and existing conventions

    Don't ask the user what you can look up yourself.

  4. Gather context — Collect what you couldn't find in the codebase. For each question, provide your recommended answer with justification based on what you found in step 3. Let the user confirm or correct. Collect: the core decision, key decision drivers, rationale, alternatives with pros/cons/rejection reasons, consequences (positive, negative, neutral), non-goals, and relevant links.
  5. Generate — Create the ADR using the template at assets/adr-template.md. Fill every section with real content — do not leave placeholder text. The ADR must include:
    • YAML frontmatter with status: PROPOSED, date, and authors. Always set status to PROPOSED — never ACCEPTED or DECIDED. This is non-negotiable even when the user has already decided.
    • Context explaining the problem and forces at play
    • Decision drivers as a prioritized list
    • The decision in active voice
    • At least two alternatives with pros, cons, and rejection reasons
    • Consequences split into positive, negative, and neutral
    • Non-goals section if anything is explicitly out of scope (omit if none)
    • Reversibility section if it's useful to note how to undo the decision or what would trigger reconsideration (omit if none)
    • References section listing any links, related ADRs, or resources discovered during research (omit if none)
  6. Place the file — Save to docs/adr/NNNN-<slug>.md where:
    • NNNN is the next sequential number (zero-padded to 4 digits)
    • <slug> is a lowercase, hyphenated summary of the decision
    • Check existing files in docs/adr/ to determine the next number
    • Example: docs/adr/0003-use-postgresql-for-persistence.md
  7. Confirm — Show the user the generated ADR and ask if any adjustments are needed before finalizing.
  8. Find blind spots — When the ADR is finalized, check if grill-with-docs exists. If it does, ask: "Want me to grill this ADR against your domain language?" If yes, activate it. If grill-with-docs doesn't exist, try grill-me instead. If neither exists, skip silently.

Quality self-check (E.C.A.D.R.)

Before finalizing an ADR, verify:

  • Explicit problem statement — Context makes the problem unambiguous
  • Comprehensive options analysis — at least 2 alternatives with honest pros/cons
  • Actionable decision — specific enough to act on, in active voice
  • Documented consequences — positive, negative, and neutral impacts
  • References included — any links from research are listed

For sections that cannot be filled from available data, insert investigation prompts: [INVESTIGATE: description of what needs follow-up]

Match depth to complexity

Omit optional sections (Non-goals, Reversibility, References) when they add no information. A two-paragraph ADR for a straightforward choice is better than a bloated one.

When a decision directly maps to code changes, add an Implementation Plan section describing affected paths and patterns to follow.

Example Scenario

User: "Should we use Postgres or DynamoDB for the order service?"

  1. Skill detects a technology choice decision
  2. Asks: "This looks like an architectural decision. Want an ADR?"
  3. Reads existing ADRs, checks go.mod/package.json for current DB
  4. Drafts ADR with both options, pros/cons, and a recommendation
  5. Saves to docs/adr/0007-use-postgresql-for-order-service.md

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.