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Patterns

Organization
MadAppGang
patterns

Common agent patterns and templates for Claude Code. Use when implementing agents to follow proven patterns for Tasks integration, quality checks, and external model invocation via claudish CLI.

Overview

PublisherMadAppGang
Repositoryclaude-code
Skill namepatterns
Stars
281
Forks
26
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 MadAppGang on GitHub. Read the source before you install it.

Installation

Install the Patterns 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/MadAppGang/claude-code.git /tmp/claude-code
mkdir -p .claude/skills
cp -r /tmp/claude-code/plugins/agentdev/skills/patterns .claude/skills/patterns
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Patterns 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 Patterns 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 Patterns 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.

plugin: agentdev updated: 2026-02-11

Agent Patterns

External Model Invocation Pattern

External AI models are invoked via Bash+claudish CLI by the orchestrator (e.g., /team). Agents do NOT need special blocks to support external models — the orchestrator calls claudish directly:

bash
# Orchestrator calls claudish directly via Bash tool
claudish --model {MODEL_ID} --stdin --quiet < prompt.md > result.md

This is 100% reliable because it's a deterministic CLI invocation, not a prompt-based delegation.


Tasks Integration Pattern

Every agent must track workflow progress.

xml
<critical_constraints>
  <tasks_requirement>
    You MUST use Tasks to track your workflow.

    **Before starting**, create task list:
    1. Phase 1 description
    2. Phase 2 description
    3. Phase 3 description

    **Update continuously**:
    - Mark "in_progress" when starting
    - Mark "completed" immediately after finishing
    - Keep only ONE task "in_progress" at a time
  </tasks_requirement>
</critical_constraints>

<workflow>
  <phase number="1" name="Phase Name">
    <step>Initialize Tasks with all phases</step>
    <step>Mark PHASE 1 as in_progress</step>
    <step>... perform work ...</step>
    <step>Mark PHASE 1 as completed</step>
    <step>Mark PHASE 2 as in_progress</step>
  </phase>
</workflow>

Quality Checks Pattern (Implementers)

xml
<implementation_standards>
  <quality_checks mandatory="true">
    Before presenting code, perform these checks in order:

    <check name="formatting" order="1">
      <tool>Biome.js</tool>
      <command>bun run format</command>
      <requirement>Must pass</requirement>
      <on_failure>Fix and retry</on_failure>
    </check>

    <check name="linting" order="2">
      <tool>Biome.js</tool>
      <command>bun run lint</command>
      <requirement>All errors resolved</requirement>
      <on_failure>Fix errors, retry</on_failure>
    </check>

    <check name="type_checking" order="3">
      <tool>TypeScript</tool>
      <command>bun run typecheck</command>
      <requirement>Zero type errors</requirement>
      <on_failure>Resolve errors, retry</on_failure>
    </check>

    <check name="testing" order="4">
      <tool>Vitest</tool>
      <command>bun test</command>
      <requirement>All tests pass</requirement>
      <on_failure>Fix failing tests</on_failure>
    </check>
  </quality_checks>
</implementation_standards>

Review Feedback Pattern (Reviewers)

xml
<review_criteria>
  <feedback_format>
    ## Review: {name}

    **Status**: PASS | CONDITIONAL | FAIL
    **Reviewer**: {model}

    **Issue Summary**:
    - CRITICAL: {count}
    - HIGH: {count}
    - MEDIUM: {count}
    - LOW: {count}

    ### CRITICAL Issues
    #### Issue 1: {Title}
    - **Category**: YAML | XML | Security | Completeness
    - **Description**: What's wrong
    - **Impact**: Why it matters
    - **Fix**: How to fix it
    - **Location**: Section/line reference

    ### HIGH Priority Issues
    [Same format]

    ### Approval Decision
    **Status**: PASS | CONDITIONAL | FAIL
    **Rationale**: Why this status
  </feedback_format>
</review_criteria>

<approval_criteria>
  <status name="PASS">
    - 0 CRITICAL issues
    - 0-2 HIGH issues
    - All core sections present
  </status>
  <status name="CONDITIONAL">
    - 0 CRITICAL issues
    - 3-5 HIGH issues
    - Core functionality works
  </status>
  <status name="FAIL">
    - 1+ CRITICAL issues
    - OR 6+ HIGH issues
    - Blocks functionality
  </status>
</approval_criteria>

Orchestrator Phase Pattern (Commands)

xml
<phases>
  <phase number="1" name="Descriptive Name">
    <objective>Clear statement of what this phase achieves</objective>

    <steps>
      <step>Mark PHASE 1 as in_progress in task list</step>
      <step>Detailed action step</step>
      <step>Detailed action step</step>
      <step>Mark PHASE 1 as completed</step>
    </steps>

    <quality_gate>
      Exit criteria - what must be true to proceed
    </quality_gate>
  </phase>
</phases>

<delegation_rules>
  <rule scope="design">ALL design → architect agent</rule>
  <rule scope="implementation">ALL implementation → developer agent</rule>
  <rule scope="review">ALL reviews → reviewer agent</rule>
</delegation_rules>

Agent Templates

Planner Template

yaml
---
name: {domain}-architect
description: |
  Plans {domain} features with comprehensive design.
  Examples: (1) "Design X" (2) "Plan Y" (3) "Architect Z"
model: sonnet
color: purple
tools: TaskCreate, TaskUpdate, TaskList, TaskGet, Read, Write, Glob, Grep, Bash
---

Implementer Template

yaml
---
name: {domain}-developer
description: |
  Implements {domain} features with quality checks.
  Examples: (1) "Create X" (2) "Build Y" (3) "Implement Z"
model: sonnet
color: green
tools: TaskCreate, TaskUpdate, TaskList, TaskGet, Read, Write, Edit, Bash, Glob, Grep
---

Reviewer Template

yaml
---
name: {domain}-reviewer
description: |
  Reviews {domain} code for quality and standards.
  Examples: (1) "Review X" (2) "Validate Y" (3) "Check Z"
model: sonnet
color: cyan
tools: TaskCreate, TaskUpdate, TaskList, TaskGet, Read, Glob, Grep, Bash
---

Orchestrator Template

yaml
---
description: |
  Orchestrates {workflow} with multi-agent coordination.
  Workflow: PHASE 1 → PHASE 2 → PHASE 3
allowed-tools: Task, AskUserQuestion, Bash, Read, TaskCreate, TaskUpdate, TaskList, TaskGet, Glob, Grep
---

Frequently asked questions

What does the Patterns AI skill do?

Common agent patterns and templates for Claude Code. Use when implementing agents to follow proven patterns for Tasks integration, quality checks, and external model invocation via claudish CLI.

Why use Patterns on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/MadAppGang/claude-code/tree/main/plugins/agentdev/skills/patterns. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Patterns?

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 Patterns?

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

Is the Patterns AI skill free?

Yes. It is published on GitHub by MadAppGang 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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