Dependency Mapping

Organization

WellApp-ai

dependency-mapping

Map slice dependencies using DSM matrix and prioritize by risk

Overview

Publisher	WellApp-ai
Repository	`Well`
Skill name	`dependency-mapping`
Stars	342
Forks	48
Bundled files	Instructions only
License	MIT
Links	Skill source Repository Homepage

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 Dependency Mapping AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
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.

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/dependency-mapping .claude/skills/dependency-mapping

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Dependency Mapping 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 Dependency Mapping 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 Dependency Mapping 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.

Dependency Mapping Skill

Map dependencies between implementation slices using Design Structure Matrix (DSM), calculate risk scores, and recommend implementation sequence.

When to Use

During Ask mode Phase 2 (CONVERGE)
When planning multi-slice features
Before phasing to understand risk order

Instructions

Phase 1: Build DSM Matrix

Create a square matrix with slices on both axes. Mark dependencies with *:


         | #1.1 | #1.2 | #2.1 | #2.2 | #2.3 | #3.1 |
---------+------+------+------+------+------+------+
#1.1     |  -   |      |      |      |      |      |
#1.2     |  *   |  -   |      |      |      |      |
#2.1     |      |  *   |  -   |      |      |      |
#2.2     |      |      |  *   |  -   |      |  *   |
#2.3     |      |  *   |  *   |      |  -   |      |
#3.1     |      |      |      |      |      |  -   |

Legend: * = row depends on column
Reading: Row #2.2 has * in columns #2.1 and #3.1 = #2.2 depends on #2.1 AND #3.1

Phase 2: Calculate Dependency Score

For each slice, count:

Metric	Formula	Meaning
Fan-in	How many slices depend ON this?	High = blocker, ship early
Fan-out	How many slices does this DEPEND on?	High = risky, ship later
Dependency Score	Fan-out count	Lower = safer

Phase 3: Calculate Leverage Score

Score each slice on reuse of existing patterns:

Level	Score	Description
Full Reuse	0	Uses existing component from design system/Storybook as-is
Extend	1	Extends existing component with new props/variants
Compose	2	Composes multiple existing components
New Pattern	3	Creates new component following design system tokens
New System	5	Requires new patterns not in design system

Check these sources before scoring:

/docs/design-system/components.md - Existing components
Glob **/*.stories.tsx - Storybook patterns
SemanticSearch for similar implementations in codebase

Phase 4: Calculate Risk Score


Risk Score = (Dependencies x 2) + Leverage + PriorityTier

Where PriorityTier:
- P1 (Frontend-only) = 0
- P2 (Frontend + Backend non-breaking) = 1
- P3 (Backend contract changes) = 2
- P4 (Data model changes) = 3

Phase 5: Identify Blockers

Flag slices that block others (high fan-in):


#3.1 WorkspaceInvite Entity
  Fan-in: 3 (blocks #2.2, #2.3, #2.4)
  RECOMMENDATION: Consider stub/mock for Phase 1, or ship early despite risk

Phase 6: Rank by Risk

Sort slices by Risk Score (lowest first = ships first):

Rank	Slice	Deps	Leverage	Tier	Risk Score
1	#1.1 Workspace Switcher	0	Extend (1)	P1 (0)	1
2	#1.2 Members Page UI	1	Compose (2)	P1 (0)	4
3	#2.1 List Members API	1	N/A	P2 (1)	4

Output Format

markdown
## Dependency Analysis

### DSM Matrix

[Matrix as shown above]

### Risk Scoring

| Slice | Deps | Leverage | Tier | Risk | Rank |
|-------|------|----------|------|------|------|
| [Slice] | [N] | [Level (score)] | P[N] | [Score] | [#] |

### Blockers Identified

| Slice | Blocks | Fan-in | Recommendation |
|-------|--------|--------|----------------|
| [Slice] | [List] | [N] | [Stub/Ship early/etc] |

### Recommended Sequence

1. [Lowest risk slice] - [Why safe]
2. [Next slice] - [Dependencies satisfied by #1]
...

### Existing System Leverage

| Component | Source | Slices Using | LOC Saved |
|-----------|--------|--------------|-----------|
| [Component] | [design-system/Storybook] | [List] | ~[N] |

Invocation

Invoke manually with "use dependency-mapping skill" or follow Ask mode Phase 2 (CONVERGE) which references this skill.

Related Skills

phasing - Uses risk scores to group into phases
design-context - Identifies existing patterns to leverage
gtm-alignment - May override risk-based order for GTM priority

Frequently asked questions

What does the Dependency Mapping AI skill do?

Map slice dependencies using DSM matrix and prioritize by risk

Why use Dependency Mapping on TypingMind?

Because you install it once and use it with any model. Dependency Mapping 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 Dependency Mapping in TypingMind?

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

Which AI models can use Dependency Mapping?

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 Dependency Mapping?

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

Is the Dependency Mapping 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Dependency Mapping Skill

When to Use

Instructions

Phase 1: Build DSM Matrix

Phase 2: Calculate Dependency Score

Phase 3: Calculate Leverage Score

Phase 4: Calculate Risk Score

Phase 5: Identify Blockers

Phase 6: Rank by Risk

Output Format

Invocation

Related Skills

Frequently asked questions

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