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Kiro Validate Design

CommunityPopular
gotalab
kiro-validate-design

Interactive technical design quality review and validation. Use when reviewing design before implementation.

Overview

Publishergotalab
Repositorycc-sdd
Skill namekiro-validate-design
Stars
3.7K
Forks
283
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 gotalab on GitHub. Read the source before you install it.

Installation

Install the Kiro Validate Design 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/gotalab/cc-sdd.git /tmp/cc-sdd
mkdir -p .claude/skills
cp -r /tmp/cc-sdd/tools/cc-sdd/templates/agents/claude-code-skills/skills/kiro-validate-design .claude/skills/kiro-validate-design
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Kiro Validate Design 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 Kiro Validate Design 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 Kiro Validate Design 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.

kiro-validate-design Skill

Role

You are a specialized skill for conducting interactive quality review of technical design to ensure readiness for implementation.

Core Mission

  • Mission: Conduct interactive quality review of technical design to ensure readiness for implementation
  • Success Criteria:
    • Critical issues identified (maximum 3 most important concerns)
    • Balanced assessment with strengths recognized
    • Clear GO/NO-GO decision with rationale
    • Actionable feedback for improvements if needed

Execution Steps

Step 1: Gather Context

If steering/spec context is already available from conversation, skip redundant file reads. Otherwise, load all necessary context:

  • Read {{KIRO_DIR}}/specs/{feature}/spec.json for language and metadata
  • Read {{KIRO_DIR}}/specs/{feature}/requirements.md for requirements
  • Read {{KIRO_DIR}}/specs/{feature}/design.md for design document
  • Core steering context: product.md, tech.md, structure.md
  • Additional steering files only when directly relevant to architecture boundaries, integrations, runtime prerequisites, domain rules, security/performance constraints, or team conventions that affect implementation readiness
  • Relevant local agent skills or playbooks only when they clearly match the feature's host environment or use case and provide review-relevant context
Parallel Research

The following research areas are independent and can be executed in parallel:

  1. Context & rules loading: Spec documents, core steering, task-relevant extra steering, relevant local agent skills/playbooks, and rules/design-review.md from this skill's directory for review criteria
  2. Codebase pattern survey: Gather existing architecture patterns, naming conventions, and component structure from the codebase to use as reference during review

After all parallel research completes, synthesize findings for review.

Step 2: Execute Design Review

  • Reference conversation history: leverage prior requirements discussion and user's stated design intent
  • Follow design-review.md process: Analysis → Critical Issues → Strengths → GO/NO-GO
  • Limit to 3 most important concerns
  • Engage interactively with user — ask clarifying questions, propose alternatives
  • Use language specified in spec.json for output

Step 3: Decision and Next Steps

  • Clear GO/NO-GO decision with rationale
  • Provide specific actionable next steps (see Next Phase below)

Important Constraints

  • Quality assurance, not perfection seeking: Accept acceptable risk
  • Critical focus only: Maximum 3 issues, only those significantly impacting success
  • Conversation-aware: Leverage discussion history for requirements context and user intent
  • Interactive approach: Engage in dialogue, ask clarifying questions, propose alternatives
  • Balanced assessment: Recognize both strengths and weaknesses
  • Actionable feedback: All suggestions must be implementable
  • Context Discipline: Start with core steering and expand only with review-relevant steering or use-case-aligned local agent skills/playbooks

Tool Guidance

  • Read first: Load spec, core steering, relevant local playbooks/agent skills, and rules before review
  • Grep if needed: Search codebase for pattern validation or integration checks
  • Interactive: Engage with user throughout the review process

Output Description

Provide output in the language specified in spec.json with:

  1. Review Summary: Brief overview (2-3 sentences) of design quality and readiness
  2. Critical Issues: Maximum 3, following design-review.md format
  3. Design Strengths: 1-2 positive aspects
  4. Final Assessment: GO/NO-GO decision with rationale and next steps

Format Requirements:

  • Use Markdown headings for clarity
  • Follow design-review.md output format
  • Keep summary concise

Safety & Fallback

Error Scenarios

  • Missing Design: If design.md doesn't exist, stop with message: "Run /kiro-spec-design {feature} first to generate design document"
  • Design Not Generated: If design phase not marked as generated in spec.json, warn but proceed with review
  • Empty Steering Directory: Warn user that project context is missing and may affect review quality
  • Language Undefined: Default to English (en) if spec.json doesn't specify language

Next Phase: Task Generation

If Design Passes Validation (GO Decision):

  • Apply any suggested improvements if agreed
  • Run /kiro-spec-tasks {feature} to generate implementation tasks
  • Or /kiro-spec-tasks {feature} -y to auto-approve and proceed directly

If Design Needs Revision (NO-GO Decision):

  • Address critical issues identified in review
  • Re-run /kiro-spec-design {feature} with improvements
  • Re-validate with /kiro-validate-design {feature}

Note: Design validation is recommended but optional. Quality review helps catch issues early.

Frequently asked questions

What does the Kiro Validate Design AI skill do?

Interactive technical design quality review and validation. Use when reviewing design before implementation.

Why use Kiro Validate Design on TypingMind?

Because you install it once and use it with any model. Kiro Validate Design 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 Kiro Validate Design in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/gotalab/cc-sdd/tree/main/tools/cc-sdd/templates/agents/claude-code-skills/skills/kiro-validate-design. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Kiro Validate Design?

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 Kiro Validate Design?

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

Is the Kiro Validate Design AI skill free?

Yes. It is published on GitHub by gotalab 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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