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Invest Adr

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erikaflowers
invest-adr

Generates a numbered Architecture Decision Record from a decision description, using VECTOR.md constraints, ARCHITECTURE.md stack context, and existing ADRs to avoid contradictions. Use when making an architectural or product decision that should be documented and stored.

Overview

Publishererikaflowers
Repositoryinvestiture
Skill nameinvest-adr
Stars
55
Forks
9
Bundled files
Instructions only
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 erikaflowers on GitHub. Read the source before you install it.

Installation

Install the Invest Adr 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/erikaflowers/investiture/tree/main/.claude/skills-optional/invest-adr
  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/erikaflowers/investiture.git /tmp/investiture
mkdir -p .claude/skills
cp -r /tmp/investiture/.claude/skills-optional/invest-adr .claude/skills/invest-adr
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Invest Adr 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 Invest Adr 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 Invest Adr 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.

Investiture Skill: Decision Record

You are capturing a decision before it becomes invisible. Architecture decisions made without records cause confusion months later when no one remembers why a choice was made — or worse, when the same decision gets relitigated. This skill takes a decision in flight and turns it into a permanent, numbered, cross-referenced ADR.

This skill runs on demand. It does not depend on invest-doctrine or invest-architecture having run first, but it reads doctrine files to inform the ADR. It writes to /vector/decisions/, which invest-backfill initializes. If doctrine files are missing, the skill flags what context is absent and continues.

Step 0: Get the Decision Description

If a decision description was passed as an argument, use it directly.

If no argument was provided, prompt the operator:

What decision are you capturing? Describe it as a question or a choice:
Examples:
  "Should we add Supabase or stay local-first with IndexedDB?"
  "Use React Query vs. manual fetch + useEffect for data loading"
  "Deploy as Cloudflare Worker vs. Netlify serverless function"

Step 1: Read Doctrine

Read the following files. They define the constraints and context within which this decision lives.

  1. VECTOR.md — Extract: constraints (hard limits), value proposition (what this product must do), design principles (philosophy that should guide decisions), project stage.
  2. ARCHITECTURE.md — Extract: current stack (what is already in use), declared preferences, existing patterns, "What Not to Do" prohibitions.
  3. /vector/decisions/ — List all existing ADR files. Read their titles and decisions. You need this to:
    • Number the new ADR correctly (next in sequence after the highest existing number)
    • Identify prior decisions this new one must not contradict
    • Find prior decisions this one supersedes or relates to

If VECTOR.md or ARCHITECTURE.md is missing, flag it and continue — you can still write a useful ADR, but note which context is missing.

If /vector/decisions/ does not exist, create it. The new ADR will be the first: ADR-001.

Step 2: Determine the ADR Number and Slug

Numbering: Find the highest existing ADR number in /vector/decisions/. The new ADR is that number + 1, zero-padded to three digits: 001, 002, 031, 100.

If no ADRs exist, start at ADR-001.

Slug: Derive from the decision description. Lowercase, hyphenated, 3-6 words:

  • "Should we add Supabase or stay local-first with IndexedDB?" → supabase-vs-indexeddb
  • "Use React Query vs. manual fetch" → react-query-vs-manual-fetch
  • "Deploy as Cloudflare Worker vs. Netlify" → cloudflare-worker-vs-netlify

Filename: ADR-[NNN]-[slug].md

Step 3: Analyze the Decision

Before writing the ADR, think through the decision using the doctrine context:

Options identification: What are the real alternatives? There are almost always at least two, sometimes three. Name them clearly. "Do nothing / keep the current approach" is always a valid option and often worth including.

Constraint filtering: Which options are ruled out by VECTOR.md constraints or ARCHITECTURE.md prohibitions? For example, if VECTOR.md says "no external dependencies," cloud-hosted databases are eliminated. State this explicitly — eliminating options based on declared constraints is faster than evaluating them.

Stack compatibility: Which options fit the existing stack without friction? Which require new dependencies, new patterns, or new expertise?

Value prop alignment: Does one option better serve the core value proposition in VECTOR.md? If yes, that's a meaningful signal, not just a technical preference.

Prior ADR cross-reference: Does any existing ADR constrain this decision? For example, a prior ADR establishing "local-first architecture" would weigh heavily against a cloud-hosted option. If a prior ADR is directly relevant, reference it by number.

Step 4: Draft the ADR

Write a complete ADR using this structure:

markdown
# ADR-[NNN]: [Decision Title — present tense, descriptive]

**Date:** [today's date]
**Status:** [proposed | accepted | deprecated | superseded by ADR-NNN]
**Deciders:** [OPERATOR: List who was involved in this decision, or remove this line]

## Context

[2-4 sentences. What is the situation that requires a decision? What problem are we solving? What constraints apply from VECTOR.md or ARCHITECTURE.md? What is the cost of not deciding?]

## Decision Drivers

[Bullet list of the factors that matter most for this decision. These come from VECTOR.md constraints, ARCHITECTURE.md preferences, and project stage. Be specific — not "performance" but "must render under 100ms on low-end devices."]

- [Driver 1 — sourced from doctrine if possible]
- [Driver 2]
- [Driver 3]

## Options Considered

### Option A: [Name]

[1-2 sentences describing this option.]

**Pros:**
- [Concrete advantage — specific to this project, not generic]
- [Another advantage]

**Cons:**
- [Concrete disadvantage or risk]
- [Another disadvantage]

### Option B: [Name]

[Same structure]

### Option C: [Name, if applicable]

[Same structure]

## Decision

**We will [chosen option].**

[2-3 sentences explaining why this option was chosen. Reference the decision drivers that it best satisfies. If a constraint from VECTOR.md or a prior ADR was the deciding factor, say so explicitly.]

## Consequences

**Positive:**
- [What gets better or easier]
- [What new capability this enables]

**Negative:**
- [What this forecloses — options that are now off the table]
- [Technical debt or tradeoffs this introduces]
- [What will need to be revisited if circumstances change]

**Neutral:**
- [Side effects that aren't good or bad, just worth knowing]

## Related Decisions

[List any ADRs that this decision relates to, supersedes, or is constrained by. Format: `ADR-NNN: [Title]`. Or "None." if this is the first ADR.]

On the [OPERATOR: ...] bracket in Deciders: Leave it exactly as written. Do not guess or fill in who was involved — that's for the operator to provide. If you do not know who made the decision, leave the bracket.

Status field:

  • proposed — Decision is being considered, not yet finalized. Default if no --status flag was passed.
  • accepted — Decision is final.
  • deprecated — The decision is no longer in effect but wasn't superseded by a specific newer ADR.
  • superseded by ADR-NNN — A newer decision replaced this one.

If --status was passed as an argument, use that value.

Step 5: Present for Confirmation

Output the drafted ADR to the terminal in full. Then ask:

ADR-[NNN]-[slug].md drafted above.
Write to /vector/decisions/ADR-[NNN]-[slug].md? (yes/no)

If --dry-run was passed: Output the ADR to the terminal and stop. Do not write the file, do not ask for confirmation.

Step 6: Write the File

On confirmation, write to /vector/decisions/ADR-[NNN]-[slug].md.

Create /vector/decisions/ if it does not exist.

Output the final ADR to the terminal and save it to /vector/decisions/ADR-[NNN]-[slug].md. Each ADR is a new sequential file — do not overwrite existing ADRs.

After writing, output:

ADR-[NNN] written to /vector/decisions/ADR-[NNN]-[slug].md

[If this supersedes a prior ADR:]
Note: You may want to update ADR-[prior NNN] to set its status to "superseded by ADR-[NNN]."

Arguments

  • No arguments: Interactive — prompts for decision description, uses --status proposed by default
  • [decision description]: Uses the argument as the decision description, skips the prompt
  • --status [proposed|accepted|deprecated|superseded]: Sets the ADR status field
  • --dry-run: Drafts and displays the ADR without writing the file

Output

/vector/decisions/ADR-[NNN]-[slug].md

When to Run

  • Before committing to a new dependency or external service
  • Before adopting a new architectural pattern that will propagate across the codebase
  • Before making a choice that is hard to reverse
  • When the same decision keeps coming up — record it so it stops being relitigated
  • After a significant pivot — record what changed and why

Principles

  • One decision, one ADR. Don't bundle multiple choices into a single record. If you're deciding both the state management library AND the data fetching strategy, write two ADRs.
  • Constraints are filters, not opinions. If VECTOR.md rules something out, say so clearly. You're not making a judgment call — you're applying declared doctrine.
  • "Do nothing" is always an option. Including the status quo as an evaluated option makes the decision more honest.
  • Specificity over generality. "Fast enough for our users" is not a decision driver. "Must render initial content under 200ms on a 3G connection" is.
  • Consequences matter as much as the decision. The point of an ADR is not just to record the choice — it's to record what it forecloses. Future contributors need to know what they're inheriting.

Frequently asked questions

What does the Invest Adr AI skill do?

Generates a numbered Architecture Decision Record from a decision description, using VECTOR.md constraints, ARCHITECTURE.md stack context, and existing ADRs to avoid contradictions. Use when making an architectural or product decision that should be documented and stored.

Why use Invest Adr on TypingMind?

Because you install it once and use it with any model. Invest Adr 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 Invest Adr in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/erikaflowers/investiture/tree/main/.claude/skills-optional/invest-adr. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Invest Adr?

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 Invest Adr?

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

Is the Invest Adr AI skill free?

It is published on GitHub by erikaflowers. Check the repository for licensing terms. 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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