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Decision Records

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ericrisco
decision-records

Use when a hard-to-reverse choice (database, framework, vendor, auth model) must be frozen as an immutable numbered ADR — the context that forced it, the options weighed, the decision, consequences both ways — or superseded without erasing history. NOT a meeting recap (that is meeting-notes), NOT standing project principles (that is constitution).

Overview

Publisherericrisco
Repositoryrsc-harness
Skill namedecision-records
Stars
239
Forks
24
Bundled files
3
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.

  • 3 bundled files

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

  • Open source

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

Installation

Install the 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/ericrisco/rsc-harness/tree/main/skills/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/ericrisco/rsc-harness.git /tmp/rsc-harness
mkdir -p .claude/skills
cp -r /tmp/rsc-harness/skills/decision-records .claude/skills/decision-records
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable 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 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 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.

Decision Records — Freeze the Why Before It Evaporates

A consequential choice got made. Capture it as one short, immutable, numbered ADR: the context that forced it, the real options weighed, the decision, and what it costs you both ways. Six months from now this file is the only thing standing between you and re-litigating the whole thing.

You own the durable single-decision artifact — an Architecture/Any Decision Record (ADR). Not a meeting recap, not a runbook, not a spec. One bounded choice, recorded so the reasoning survives the people who made it.

The one test: is this even ADR-worthy?

Write an ADR iff the choice is costly to reverse OR future-you will ask "why did we do it this way". Everything else is noise — an ADR for a trivial reversible tweak is just paperwork, and ADRs lose all their value the moment they become forced ceremony (this is the consistent message from Fowler and the AWS Architecture Blog).

Worth an ADRSkip it
Postgres vs DynamoDB for the primary storeWhich lint rule to enable
Monolith vs microservices for v1Renaming a local variable
Build vs buy auth (Clerk vs roll-your-own)Bumping a patch dependency
Dropping REST for tRPC across the APIChoosing a CSS color token
Picking Hetzner over AWS for hostingA reversible feature flag default

If you can rip it out in an afternoon and nobody will ever ask why, don't write an ADR. Decide, move on.

Decide home + naming once

Pick a storage convention before the first record, and never spawn a competing second log. Default to the project's existing convention if one exists.

Naming schoolLooks likeUse when
Numeric-prefix (adr-tools / MADR)0007-choose-postgres.mdYou need stable IDs to cross-reference for supersession — the default
Imperative verb-nounchoose-database.mdTiny log, no supersession expected, humans browse by topic

Prefer numeric-prefix: NNNN-title-with-dashes.md. Stable IDs are what make "Superseded by ADR-0012" mean something.

Where to store (first match wins):

  1. The project's existing convention — never invent a parallel log.
  2. A code repo with no convention: docs/adr/ (also seen as doc/adr/).
  3. A harness workspace: 02-DOCS/wiki/decisions/, indexed in 02-DOCS/wiki/index.md (the Knowledge map; root CLAUDE.md keeps only a short pointer).
text
Bad:  decision-final-v2-REAL.md          # no ID, no order, will rot
Bad:  notes/adr/db.md                     # buried, not in the log
Good: docs/adr/0007-choose-postgres.md    # stable ID, sorts, cross-referencable

Seed the index the moment you create the first ADR — a README row or 0000-index.md listing id, title, status, date. An ADR that isn't in the index is an orphan nobody will find.

The record spine

Map every ADR to the MADR 4.0.0 template (released 2024-09-17 — the current version). MADR ships full and minimal variants; use minimal until a section earns its place.

Required spine, in order: Title → Status → Date → Context and Problem Statement → Decision Drivers → Considered Options → Decision Outcome → Consequences. The MADR-optional sections (Confirmation, Pros and Cons of the Options, More Information) you add only when they pull weight.

Minimal skeleton — this is enough for most decisions. When the ADR lives in a harness workspace (02-DOCS/wiki/decisions/), it is part of an OKF v0.1 bundle, so it opens with YAML frontmatter carrying a non-empty type: decision (plus the OKF-recommended title/tags/ timestamp). The body keeps the human-readable - Status:/- Date: lines verbatim — they are the decision's domain record and scripts/verify.sh reads them, so the frontmatter is purely additive. Cross-references use standard markdown links, never wikilinks. (In a plain code repo under docs/adr/, the frontmatter is optional — OKF conformance only governs the wiki bundle.)

markdown
---
type: decision
title: "Choose Postgres over DynamoDB for the primary store"
tags: [adr, database, billing]
timestamp: 2026-06-02T00:00:00Z
---

# 7. Choose Postgres over DynamoDB for the primary store

- Status: accepted
- Date: 2026-06-02
- Deciders: @alice, @bob

## Context and Problem Statement

We need a primary datastore for the billing service. Relational
invoicing data, ~2k writes/min, team of 3 with deep SQL experience,
EU data-residency required. Which store do we adopt?

## Decision Drivers

- Strong relational integrity for invoices (foreign keys, transactions)
- Team SQL fluency; near-zero NoSQL operational experience
- EU residency + self-host option (constitution §data-residency)

## Considered Options

- Postgres (managed, EU region)
- DynamoDB
- MongoDB Atlas

## Decision Outcome

Chosen: **Postgres**, because it matches the relational shape of the
data and the team's existing fluency, with the lowest operational risk.

## Consequences

- Good: ACID transactions, mature tooling, team productive day one.
- Bad: we own connection-pool and vertical-scaling concerns earlier.
- Follow-up: provision read replica before launch (ADR-0009 tracks scaling).

Keep it to 1–2 pages, readable in 5–10 minutes. If a record sprawls, you're probably bundling two decisions — split them. The full annotated MADR full+minimal templates and a worked example live in references/templates.md.

Write context that forces the decision

The context section must make the decision feel inevitable. State the problem and the constraints — not the solution.

  • State the problem, not the answer. "We need to pick a store" not "We should use Postgres." The verdict belongs in Decision Outcome.
  • Quantify. "~2k writes/min", "team of 3", "EU residency", "p99 < 100ms". Numbers are what let future readers judge if your constraints still hold.
  • Cite the driver. If a constitution principle or a spec/plan fork forced this, link it. An ADR is often downstream of constitution and cites it as a driver.
markdown
Bad context:  "We decided to use Postgres because it's reliable."
              (conclusion smuggled in, no constraints, unfalsifiable)

Good context: "Billing needs relational integrity for invoices,
              ~2k writes/min, a 3-person team with SQL but no NoSQL
              ops experience, and EU data residency (constitution
              §data-residency). Which primary store do we adopt?"

Options must be real and comparable

The rejected options are the asset — they're the proof you thought, and they stop the next person re-proposing them.

  • List ≥2 genuine options. A single foregone "option" is theatre. If there was really only one path, you don't need an ADR.
  • Score each against the same drivers. Apples to apples, or the comparison is meaningless.
  • Record why each loser lost. That sentence is worth more than the winner's praise.
markdown
| Option    | Relational integrity | Team fluency | EU self-host | Op. risk |
|-----------|----------------------|--------------|--------------|----------|
| Postgres  | native               | high         | yes          | low      |
| DynamoDB  | app-enforced         | none         | no (AWS)     | high     |
| Mongo     | partial              | medium       | yes          | medium   |

Consequences, both directions

A one-sided ADR is marketing, and marketing erodes the trust that makes the log worth reading. Record:

  • What improves — the upside you bought.
  • What degrades — the price you paid, honestly.
  • New work / risk it creates — what you now own that you didn't before.
  • Follow-ups — concrete tasks, ideally linked to a tracker or a future ADR.

If you can't name a single downside, you haven't finished thinking. Every real choice has a cost.

Status lifecycle + immutability

An accepted or rejected ADR is immutable. You never edit the decision — you write a new ADR that supersedes it and flip the old one's status. This is the whole point: history stays intact.

StatusMeaningNext
proposeddrafted, under review→ accepted or rejected
acceptedadopted, in force→ deprecated or superseded
rejectedconsidered and declinedterminal (kept for the record)
deprecatedno longer relevant, not replacedterminal
superseded by ADR-NNNNreplaced by a newer decisionterminal, back-linked

The supersession ritual (never skip a step):

  1. Write the new ADR (e.g. 0012-adopt-trpc.md), status accepted, with a "Supersedes ADR-0004" note in its context.
  2. Edit only the status line of the old ADR (0004) to superseded by ADR-0012. Leave its decision and rationale untouched.
  3. Back-link both ways so a reader landing on either finds the other.

Never delete an ADR and never rewrite its decision — a wrong-in-hindsight ADR is still true history. A worked supersession pair is in references/templates.md.

Maintain the log

A decision log is only useful if it stays navigable.

  • Index every ADR at creation. One row: | 0007 | Choose Postgres | accepted | 2026-06-02 |. No orphans.
  • Keep supersession links live in both directions.
  • Review cadence. Periodically sweep accepted ADRs — anything reality has overtaken gets a superseding record, not a silent edit.
  • Index the log from the entry point — 02-DOCS/wiki/index.md — so onboarding finds it. The broader wiki and onboarding doc belong to knowledge-ops and codebase-onboarding; this skill owns only the ADRs the wiki links to. The meeting that spawned a decision routes to meeting-notes; the repeatable how-to it implies routes to sop-builder.

Anti-patterns

Anti-patternWhy it's wrongDo instead
Editing an accepted ADR's decision in placeDestroys history; future readers can't see the pathSupersede with a new ADR, flip old status
Single foregone "option"No real comparison; theatre, not a recordList ≥2 genuine options or skip the ADR
No consequences (or only upside)One-sided ADR is marketing; erodes trustRecord gains, costs, new work, follow-ups
Novella-length ADRNobody reads 8 pages; signal drowns1–2 pages; split bundled decisions
ADR for a trivial reversible choiceForced paperwork; kills the practiceApply the one test first
Re-deciding what the constitution settledDuplicates the standing ruleset, causes driftCite the constitution as a driver instead
Orphan ADR not in the indexInvisible; nobody finds itAdd the index row at creation
Context that states the answerDecision smuggled in, unfalsifiableProblem + constraints only; verdict in Outcome

Verify

Lint a produced ADR (or a whole decisions dir) with scripts/verify.sh <path>: it checks for a recognized status, a date, the required sections, ≥2 options, and a valid filename — read-only, no network. It expects the skeletons above.

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 Decision Records AI skill do?

Use when a hard-to-reverse choice (database, framework, vendor, auth model) must be frozen as an immutable numbered ADR — the context that forced it, the options weighed, the decision, consequences both ways — or superseded without erasing history. NOT a meeting recap (that is meeting-notes), NOT standing project principles (that is constitution).

Why use Decision Records on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ericrisco/rsc-harness/tree/main/skills/decision-records. 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 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 Decision Records?

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

Is the Decision Records AI skill free?

Yes. It is published on GitHub by ericrisco 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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