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Kiro Validate Gap

CommunityPopular
gotalab
kiro-validate-gap

Analyze implementation gap between requirements and existing codebase. Use when planning integration with existing systems.

Overview

Publishergotalab
Repositorycc-sdd
Skill namekiro-validate-gap
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 Gap 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-gap .claude/skills/kiro-validate-gap
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Kiro Validate Gap 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 Gap 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 Gap 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-gap Skill

Role

You are a specialized skill for analyzing the implementation gap between requirements and existing codebase to inform implementation strategy.

Core Mission

  • Mission: Analyze the gap between requirements and existing codebase to inform implementation strategy
  • Success Criteria:
    • Comprehensive understanding of existing codebase patterns and components
    • Clear identification of missing capabilities and integration challenges
    • Multiple viable implementation approaches evaluated
    • Technical research needs identified for design phase

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
  • Core steering context: product.md, tech.md, structure.md
  • Additional steering files only when directly relevant to the feature's domain rules, integrations, runtime prerequisites, compliance/security constraints, or existing product boundaries
  • Relevant local agent skills or playbooks only when they clearly match the feature's host environment or use case and provide analysis-relevant context

Step 2: Read Analysis Guidelines

  • Read rules/gap-analysis.md from this skill's directory for comprehensive analysis framework

Step 3: Execute Gap Analysis

Parallel Research

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

  1. Codebase analysis: Existing implementations, architecture patterns, integration points, extension possibilities (using Grep/Glob/Read)
  2. External dependency research: Dependency compatibility, version constraints, known integration challenges (using WebSearch/WebFetch when needed)
  3. Context loading: Requirements, core steering, task-relevant extra steering, relevant local agent skills/playbooks, and gap-analysis rules

After all parallel research completes, synthesize findings for gap analysis.

  • Follow gap-analysis.md framework for thorough investigation
  • Evaluate multiple implementation approaches (extend/new/hybrid)
  • Use language specified in spec.json for output

Step 4: Generate Analysis Document

  • Create comprehensive gap analysis following the output guidelines in gap-analysis.md
  • Present multiple viable options with trade-offs
  • Flag areas requiring further research

Step 5: Write Gap Analysis to Disk

Write the gap analysis to disk so it survives session boundaries and can be referenced during design phase.

  • Use the Write tool to save the gap analysis to {{KIRO_DIR}}/specs/{feature}/research.md
  • If the file already exists, append the new analysis (separated by a horizontal rule ---) rather than overwriting previous research
  • Verify the file was written by reading it back

Important Constraints

  • Information over Decisions: Provide analysis and options, not final implementation choices
  • Multiple Options: Present viable alternatives when applicable
  • Thorough Investigation: Use tools to deeply understand existing codebase
  • Explicit Gaps: Clearly flag areas needing research or investigation
  • Context Discipline: Start with core steering and expand only with analysis-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 analysis
  • Grep extensively: Search codebase for patterns, conventions, and integration points
  • WebSearch/WebFetch: Research external dependencies and best practices when needed
  • Write last: Generate analysis only after complete investigation

Output Description

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

  1. Analysis Summary: Brief overview (3-5 bullets) of scope, challenges, and recommendations
  2. Document Status: Confirm analysis approach used
  3. Next Steps: Guide user on proceeding to design phase

Format Requirements:

  • Use Markdown headings for clarity
  • Keep summary concise (under 300 words)
  • Detailed analysis follows gap-analysis.md output guidelines

Safety & Fallback

Error Scenarios

  • Missing Requirements: If requirements.md doesn't exist, stop with message: "Run /kiro-spec-requirements {feature} first to generate requirements"
  • Requirements Not Approved: If requirements not approved, warn user but proceed (gap analysis can inform requirement revisions)
  • Empty Steering Directory: Warn user that project context is missing and may affect analysis quality
  • Complex Integration Unclear: Flag for comprehensive research in design phase rather than blocking
  • Language Undefined: Default to English (en) if spec.json doesn't specify language

Next Phase: Design Generation

If Gap Analysis Complete:

  • Review gap analysis insights
  • Run /kiro-spec-design {feature} to create technical design document
  • Or /kiro-spec-design {feature} -y to auto-approve requirements and proceed directly

Note: Gap analysis is optional but recommended for brownfield projects to inform design decisions.

Frequently asked questions

What does the Kiro Validate Gap AI skill do?

Analyze implementation gap between requirements and existing codebase. Use when planning integration with existing systems.

Why use Kiro Validate Gap on TypingMind?

Because you install it once and use it with any model. Kiro Validate Gap 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 Gap 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-gap. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Kiro Validate Gap?

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 Gap?

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

Is the Kiro Validate Gap 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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