Decision Capture logo

Decision Capture

Organization
WellApp-ai
decision-capture

Capture patine (decision wisdom) at Gates when KO or challenge occurs

Overview

PublisherWellApp-ai
RepositoryWell
Skill namedecision-capture
Stars
342
Forks
48
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 WellApp-ai on GitHub. Read the source before you install it.

Installation

Install the Decision Capture 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/WellApp-ai/Well.git /tmp/Well
mkdir -p .claude/skills
cp -r /tmp/Well/cursor-rules/skills/decision-capture .claude/skills/decision-capture
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Decision Capture 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 Capture 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 Capture 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 Capture Skill

Lightweight skill for capturing the "patine" - the accumulated wisdom of why decisions were made and alternatives rejected. Triggered at Gates when human provides KO or challenges a proposal.

When to Use

  • At any Gate when human provides KO
  • When human challenges or rejects a proposal
  • When debug skill finds a pattern worth remembering
  • When significant technical decision is made

Trigger Conditions

TriggerContextType
Gate 1 KOWireframe rejectedDecision
Gate 2 KOScope option rejectedDecision
Gate 3 KO/BLOCKPhasing challengedDecision
Gate 4 KOTechnical approach rejectedDecision
Gate 5 ChangesPR review feedbackDecision
Debug PatternRecurring issue foundKaizen
DIG 3+ timesSame wireframe refined repeatedlyKaizen
Same error fixed 2+ timesFix pattern emergedKaizen
User corrects assumptionAI was wrong about somethingHansei
Takt warning exceededPhase took longer than targetHansei
Jidoka Tier 2/3Escalation to human requiredHansei

Phase 1: Detect Decision Type

Categorize the decision:

TypeSignalExample
TechnicalCode, architecture, library"Don't use GraphQL subscriptions"
UXInteraction, visual, flow"Sidebar navigation, not top nav"
ProcessWorkflow, phasing, priority"Ship auth before tables"

Phase 2: Prompt for Rationale

Ask for brief rationale (keep it light):

markdown
I'll note this decision for future reference.

**In one sentence, why this decision?**

Examples:
- "We tried X in 2024, broke production"
- "Users missed this in testing"
- "Conflicts with our caching strategy"

(Press Enter to skip if you prefer not to explain)

If human declines: Record decision without rationale (still valuable).

Phase 3: Attribute

Capture metadata:

FieldSource
WhoCurrent user (from context)
WhenCurrent date
DomainFrom branch name or changed files
GateWhich Gate triggered capture
Related TaskNotion task ID if available

Phase 4: Store

Layer 1: Notion (Default - Always)

Use Notion MCP to create record:

API-create-page:
  parent: { database_id: "[DECISION_PATINE_DB_ID]" }
  properties:
    Title: { title: [{ text: { content: "[Decision summary]" }}]}
    Domain: { select: { name: "[domain]" }}
    Type: { select: { name: "[Technical/UX/Process]" }}
    Decision: { rich_text: [{ text: { content: "[What we decided]" }}]}
    Rationale: { rich_text: [{ text: { content: "[Why]" }}]}
    Rejected: { rich_text: [{ text: { content: "[What we didn't do and why]" }}]}
    Impact: { select: { name: "[Low/Medium/High]" }}
    Gate: { select: { name: "[Gate 1/2/3/4/5/Debug]" }}

Layer 2: ADR File (If High Impact)

If Impact = High or Type = Technical with cross-domain effect:

  1. Get next ADR number: ls docs/decisions/ | wc -l
  2. Create file: /docs/decisions/NNN-[slug].md
  3. Use ADR template

ADR Template:

markdown
# ADR-[NNN]: [Title]

**Date**: [YYYY-MM-DD]
**Status**: Accepted
**Domain**: [domain]
**Captured at**: Gate [N]

## Context

[1-2 sentences: What problem were we solving?]

## Decision

[What we chose to do]

## Rationale

[Why this approach - the positive case]

## Rejected Alternatives

### [Alternative Name]
**Why not**: [Reason]

## Consequences

- [Trade-off 1]
- [Trade-off 2]

## References

- Notion: [link to Decision Patine record]
- Task: [link to related task if applicable]

Layer 3: Inline Comment (If Micro/Code-Specific)

For small code-level decisions during implementation:

typescript
// ADR: [Brief decision]. [Why not alternative]. —@[initials] [YYYY-MM]

Example:

typescript
// ADR: No useMemo here - profiling showed <1ms gain, adds complexity. —@mc 2026-01

Phase 5: Confirm

Output confirmation:

markdown
**Noted:** [Decision summary]

Stored in Decision Patine database.
[If ADR created: Created ADR-[NNN] in /docs/decisions/]

Continuing with workflow...

Kaizen/Hansei Capture (NEW)

Automatic learning capture without user prompts. These triggers capture patterns and reflections silently.

Automatic Triggers

These captures happen automatically without prompting user:

Kaizen (pattern emerged):

  • Wireframe DIG'd 3+ times → Capture the pattern that emerged
  • Same error fixed 2+ times → Capture the fix pattern
  • Repeated code pattern → Capture abstraction opportunity

Hansei (reflection):

  • User corrects AI assumption → Capture what was wrong
  • Phase exceeded takt warning → Capture why it took longer
  • Jidoka escalation → Capture what blocked progress

Kaizen Format

markdown
Type: KAIZEN
Source: [Phase] [Loop/Commit]
Learning: "[What pattern emerged]"
Category: [UX_PATTERN | TECHNICAL | PROCESS]
Impact: LOW | MEDIUM | HIGH

Example:

markdown
Type: KAIZEN
Source: DIVERGE Loop 3
Learning: "Invite modals benefit from email preview side panel"
Category: UX_PATTERN
Impact: MEDIUM

Hansei Format

markdown
Type: HANSEI
Source: [Phase] [Loop/Commit]
Learning: "[What we learned from the mistake/delay]"
Category: [ASSUMPTION | COMPLEXITY | PROCESS]
Impact: LOW | MEDIUM | HIGH

Example:

markdown
Type: HANSEI
Source: CONVERGE
Learning: "Original scope too ambitious - exceeded 40min takt warning"
Category: COMPLEXITY
Impact: LOW

Silent Capture Rules

  • Do NOT prompt user for rationale on Kaizen/Hansei triggers
  • Capture automatically based on observed patterns
  • Include in session-journal sync at end of session
  • Only HIGH impact Kaizen/Hansei create immediate ADR files

Session Aggregation

Instead of immediately creating Notion entries for each:

  1. Collect Kaizen/Hansei entries in memory during session
  2. Aggregate in session-journal sync at end (Phase 5 of notion-sync)
  3. Only HIGH impact items create immediate ADR files

Querying Patine

Before proposing new patterns, query existing decisions:

API-query-database:
  database_id: "[DECISION_PATINE_DB_ID]"
  filter:
    property: "Domain"
    select:
      equals: "[current domain]"

Use results to:

  1. Avoid re-proposing rejected alternatives
  2. Understand existing constraints
  3. Reference past decisions in new proposals

Anti-Patterns

DON'T:

  • Require rationale for every micro-decision
  • Create ADR files for non-architectural choices
  • Capture decisions that are already in code comments
  • Ask "why" more than once if human declines

DO:

  • Capture at the moment of friction (KO, challenge)
  • Accept "we tried this before, it failed" as valid rationale
  • Keep entries scannable (1-2 sentences)
  • Link to evidence when available (PRs, issues, metrics)

Integration

This skill is invoked by:

  • ask.mdc - Gates 1, 2, 3 on KO
  • plan.mdc - Gate 4 on option rejection
  • push-pr.mdc - Gate 5 on changes requested
  • debug - When pattern worth remembering is found

Notion Database Schema

Database: Decision Patine

PropertyTypeRequired
TitleTitleYes
DomainSelectYes
TypeSelectYes
DecisionRich TextYes
RationaleRich TextNo
RejectedRich TextNo
Challenged ByPersonNo
DateDateYes
ImpactSelectYes
GateSelectNo
Related TaskRelationNo
ADR FileURLNo

Invocation

Invoked automatically at Gates on KO, or manually with "use decision-capture skill".

Frequently asked questions

What does the Decision Capture AI skill do?

Capture patine (decision wisdom) at Gates when KO or challenge occurs

Why use Decision Capture on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/WellApp-ai/Well/tree/main/cursor-rules/skills/decision-capture. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Decision Capture?

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 Capture?

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

Is the Decision Capture AI skill free?

Yes. It is published on GitHub by WellApp-ai 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇