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Tech Divergence

Organization
WellApp-ai
tech-divergence

Evaluate technical options with scoring matrix, trigger Gate 4 for significant decisions

Overview

PublisherWellApp-ai
RepositoryWell
Skill nametech-divergence
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 Tech Divergence 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/tech-divergence .claude/skills/tech-divergence
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Tech Divergence 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 Tech Divergence 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 Tech Divergence 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.

Tech Divergence Skill

Evaluate technical implementation options using an 8-dimension scoring matrix. Low scores auto-proceed; high scores (>= 4) trigger Gate 4 for human decision.

When to Use

  • During Plan Mode Phase 2 (Technical Diverge)
  • Before committing to a specific architecture or pattern
  • When multiple valid implementation approaches exist

Scoring Matrix (8 Dimensions)

Each dimension scores 0 (auto-proceed) or 1 (adds to checkpoint score):

Dimension0 (Low Risk)1 (Checkpoint)
PatternExists in codebaseNew pattern required
ScopeSingle domainCross-domain impact
Data ModelAdd field to existingNew entity/table
DependenciesUse existing libsNew dependency
API SurfaceInternal onlyPublic/breaking change
ReversibilityEasy to undoRequires migration
SecurityNon-sensitive dataAuth/permissions
PerformanceSimple CRUDCache/queue/optimization

Phase 1: Gather Context

1.1 Query Pattern Library (Notion)

Check if similar patterns exist:

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

1.2 Search Codebase

SemanticSearch: "How is [similar feature] implemented?"
Grep: "[pattern name]" in relevant directories

1.3 Query Context7 (External Libraries)

If new libraries are being considered:

Context7 MCP:
1. resolve-library-id: libraryName = "[library]"
2. get-library-docs: topic = "best practices", mode = "info"

Phase 2: Score Each Dimension

For each of the 8 dimensions, evaluate and score:

## Technical Divergence Score

| Dimension | Score | Rationale |
|-----------|-------|-----------|
| Pattern | 0/1 | [Exists/New] |
| Scope | 0/1 | [Single/Cross-domain] |
| Data Model | 0/1 | [Field/Entity] |
| Dependencies | 0/1 | [Existing/New] |
| API Surface | 0/1 | [Internal/Public] |
| Reversibility | 0/1 | [Easy/Migration] |
| Security | 0/1 | [Non-sensitive/Auth] |
| Performance | 0/1 | [CRUD/Optimization] |
| **TOTAL** | [0-8] | |

Phase 3: Determine Path

If Score < 4: Auto-Proceed

## Technical Approach: Auto-Proceed

**Score:** [N]/8 (below threshold)

**Selected Approach:** [Describe the approach]

**Rationale:** 
- Pattern exists: [reference]
- Low cross-domain impact
- Easy to reverse if needed

Proceeding to Commit Plan...

If Score >= 4: Gate 4 (Human Checkpoint)

## Gate 4: Technical Approach Selection

**Score:** [N]/8 (threshold reached)

**Why human input needed:**
- [List dimensions that scored 1]

### Option A: [Name] (Conservative)

**Approach:** [Description]
**Effort:** S
**Risk:** Low
**Trade-off:** [What you give up]

### Option B: [Name] (Balanced)

**Approach:** [Description]
**Effort:** M
**Risk:** Medium
**Trade-off:** [What you give up]

### Option C: [Name] (Bold)

**Approach:** [Description]
**Effort:** L
**Risk:** Higher
**Trade-off:** [What you give up]

---

**Which approach would you like to proceed with?** (A / B / C)

*If you reject an option, I'll invoke decision-capture to record why.*

Phase 4: Record Decision

After Gate 4 selection:

  1. If option rejected, invoke decision-capture skill
  2. Record selected approach in context for Commit Plan
  3. Update Pattern Library if new pattern established

Integration with Plan Mode

This skill is invoked during Plan Mode Phase 2:

Plan Mode Flow:
Phase 1: APPETITE → 
Phase 2: TECHNICAL DIVERGE (this skill) →
  If score < 4: Auto-proceed
  If score >= 4: Gate 4 → Human selects
Phase 3: COMMIT PLAN

MCP Tools Used

ToolPurpose
Notion MCPQuery Pattern Library database
Context7 MCPGet library best practices
SemanticSearchFind existing codebase patterns
GrepSearch for specific implementations

Example Scoring

Low Score Example (Auto-Proceed)

Feature: Add a new column to existing table

DimensionScoreRationale
Pattern0Column additions done before
Scope0Single domain (tables)
Data Model0Adding field, not entity
Dependencies0Using existing libs
API Surface0Internal only
Reversibility0Easy migration
Security0Non-sensitive data
Performance0Simple CRUD
TOTAL0Auto-proceed

High Score Example (Gate 4)

Feature: Add real-time collaboration

DimensionScoreRationale
Pattern1No WebSocket patterns yet
Scope1Affects auth, tables, workflows
Data Model1New presence entity
Dependencies1Need socket.io
API Surface1New WS endpoints
Reversibility1Would need cleanup migration
Security1User session handling
Performance1Needs connection pooling
TOTAL8Gate 4 required

Invocation

Invoked automatically by Plan Mode Phase 2, or manually with "use tech-divergence skill".

Frequently asked questions

What does the Tech Divergence AI skill do?

Evaluate technical options with scoring matrix, trigger Gate 4 for significant decisions

Why use Tech Divergence on TypingMind?

Because you install it once and use it with any model. Tech Divergence 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 Tech Divergence in TypingMind?

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

Which AI models can use Tech Divergence?

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 Tech Divergence?

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

Is the Tech Divergence 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.

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