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Parallel Feature Development

CommunityPopular
wshobson
parallel-feature-development

Coordinate parallel feature development with file ownership strategies, conflict avoidance rules, and integration patterns for multi-agent implementation. Use this skill when decomposing a large feature into independent work streams, when two or more agents need to implement different layers of the same system simultaneously, when establishing file ownership to prevent merge conflicts in a shared codebase, when designing interface contracts so parallel implementers can build against each other's APIs before they are ready, or when deciding whether to use vertical slices versus horizontal layers for a full-stack feature.

Overview

Publisherwshobson
Repositoryagents
Skill nameparallel-feature-development
Stars
39.8K
Forks
4.2K
Bundled files
2
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.

  • 2 bundled files

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

  • Open source

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

Installation

Install the Parallel Feature Development 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/wshobson/agents.git /tmp/agents
mkdir -p .claude/skills
cp -r /tmp/agents/plugins/agent-teams/skills/parallel-feature-development .claude/skills/parallel-feature-development
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Parallel Feature Development 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 Parallel Feature Development 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 Parallel Feature Development 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.

Parallel Feature Development

Strategies for decomposing features into parallel work streams, establishing file ownership boundaries, avoiding conflicts, and integrating results from multiple implementer agents.

When to Use This Skill

  • Decomposing a feature for parallel implementation
  • Establishing file ownership boundaries between agents
  • Designing interface contracts between parallel work streams
  • Choosing integration strategies (vertical slice vs horizontal layer)
  • Managing branch and merge workflows for parallel development

File Ownership Strategies

By Directory

Assign each implementer ownership of specific directories:

implementer-1: src/components/auth/
implementer-2: src/api/auth/
implementer-3: tests/auth/

Best for: Well-organized codebases with clear directory boundaries.

By Module

Assign ownership of logical modules (which may span directories):

implementer-1: Authentication module (login, register, logout)
implementer-2: Authorization module (roles, permissions, guards)

Best for: Feature-oriented architectures, domain-driven design.

By Layer

Assign ownership of architectural layers:

implementer-1: UI layer (components, styles, layouts)
implementer-2: Business logic layer (services, validators)
implementer-3: Data layer (models, repositories, migrations)

Best for: Traditional MVC/layered architectures.

Conflict Avoidance Rules

The Cardinal Rule

One owner per file. No file should be assigned to multiple implementers.

When Files Must Be Shared

If a file genuinely needs changes from multiple implementers:

  1. Designate a single owner — One implementer owns the file
  2. Other implementers request changes — Message the owner with specific change requests
  3. Owner applies changes sequentially — Prevents merge conflicts
  4. Alternative: Extract interfaces — Create a separate interface file that the non-owner can import without modifying

Interface Contracts

When implementers need to coordinate at boundaries:

typescript
// src/types/auth-contract.ts (owned by team-lead, read-only for implementers)
export interface AuthResponse {
  token: string;
  user: UserProfile;
  expiresAt: number;
}

export interface AuthService {
  login(email: string, password: string): Promise<AuthResponse>;
  register(data: RegisterData): Promise<AuthResponse>;
}

Both implementers import from the contract file but neither modifies it.

Integration Patterns

Vertical Slice

Each implementer builds a complete feature slice (UI + API + tests):

implementer-1: Login feature (login form + login API + login tests)
implementer-2: Register feature (register form + register API + register tests)

Pros: Each slice is independently testable, minimal integration needed. Cons: May duplicate shared utilities, harder with tightly coupled features.

Horizontal Layer

Each implementer builds one layer across all features:

implementer-1: All UI components (login form, register form, profile page)
implementer-2: All API endpoints (login, register, profile)
implementer-3: All tests (unit, integration, e2e)

Pros: Consistent patterns within each layer, natural specialization. Cons: More integration points, layer 3 depends on layers 1 and 2.

Hybrid

Mix vertical and horizontal based on coupling:

implementer-1: Login feature (vertical slice — UI + API + tests)
implementer-2: Shared auth infrastructure (horizontal — middleware, JWT utils, types)

Best for: Most real-world features with some shared infrastructure.

Branch Management

Single Branch Strategy

All implementers work on the same feature branch:

  • Simple setup, no merge overhead
  • Requires strict file ownership to avoid conflicts
  • Best for: small teams (2-3), well-defined boundaries

Multi-Branch Strategy

Each implementer works on a sub-branch:

feature/auth
  ├── feature/auth-login      (implementer-1)
  ├── feature/auth-register    (implementer-2)
  └── feature/auth-tests       (implementer-3)
  • More isolation, explicit merge points
  • Higher overhead, merge conflicts still possible in shared files
  • Best for: larger teams (4+), complex features

Troubleshooting

Implementers are blocking each other waiting for shared code. Extract the shared piece into its own interface contract file owned by the team-lead and have implementers import from it. Neither implementer modifies the contract — they only implement against it.

Merge conflicts appear even with clear ownership rules. A file was assigned to two agents, or a config/index file (e.g., index.ts, __init__.py) that auto-imports everything was modified by both. Designate one owner for all barrel/index files, or have the lead merge them at the end.

An implementer finishes early but the integration step is blocked. Use a staging interface: the finished implementer writes a stub or mock of the downstream dependency so the other implementer can continue working. Replace with the real implementation at integration time.

The feature decomposition turned out wrong mid-stream. Stop new work, have the lead redistribute files, and communicate the change via broadcast. Sunk cost on partially written code is acceptable — continuing with the wrong split is worse.

Tests written by one implementer fail against code written by another. Interface contracts drifted: the implementer who owns the API changed a signature without notifying the test implementer. Enforce the rule that contract files require a broadcast before modification.

Related Skills

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 Parallel Feature Development AI skill do?

Coordinate parallel feature development with file ownership strategies, conflict avoidance rules, and integration patterns for multi-agent implementation. Use this skill when decomposing a large feature into independent work streams, when two or more agents need to implement different layers of the same system simultaneously, when establishing file ownership to prevent merge conflicts in a shared codebase, when designing interface contracts so parallel implementers can build against each other's APIs before they are ready, or when deciding whether to use vertical slices versus horizontal la...

Why use Parallel Feature Development on TypingMind?

Because you install it once and use it with any model. Parallel Feature Development 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 Parallel Feature Development in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/wshobson/agents/tree/main/plugins/agent-teams/skills/parallel-feature-development. 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 Parallel Feature Development?

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 Parallel Feature Development?

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

Is the Parallel Feature Development AI skill free?

Yes. It is published on GitHub by wshobson 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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