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Kiro Spec Quick

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gotalab
kiro-spec-quick

Quick spec generation with interactive or automatic mode

Overview

Publishergotalab
Repositorycc-sdd
Skill namekiro-spec-quick
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 Spec Quick 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/antigravity-skills/skills/kiro-spec-quick .claude/skills/kiro-spec-quick
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Kiro Spec Quick 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 Spec Quick 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 Spec Quick 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.

Quick Spec Generator

If --auto flag is present in $ARGUMENTS, you are in AUTOMATIC MODE.

In Automatic Mode:

  • Execute ALL 4 phases in a continuous loop without stopping
  • Display progress after each phase (e.g., "Phase 1/4 complete: spec initialized")
  • IGNORE any "Next Step" messages from Phase 2-4 (they are for standalone usage)
  • After Phase 4, run the final sanity review before exiting
  • Stop ONLY after the sanity review completes or if error occurs

Core Task

Execute 4 spec phases sequentially. In automatic mode, execute all phases without stopping. In interactive mode, prompt user for approval between phases.

Before claiming quick generation is complete, run one lightweight sanity review over the generated requirements, design, and tasks. If the host supports fresh sub-agents, use one. Otherwise run the sanity review inline.

Execution Steps

Step 1: Parse Arguments and Initialize

Parse $ARGUMENTS:

  • If contains --auto: Automatic Mode (execute all 4 phases)
  • Otherwise: Interactive Mode (prompt at each phase)
  • Extract description (remove --auto flag if present)

Example:

"User profile with avatar upload --auto" → mode=automatic, description="User profile with avatar upload"
"User profile feature" → mode=interactive, description="User profile feature"

Display mode banner and proceed to Step 2.

Step 2: Execute Phase Loop

Execute these 4 phases in order:


Phase 1: Initialize Spec (Direct Implementation)

Core Logic:

  1. Check for Brief:

    • If {{KIRO_DIR}}/specs/{feature-name}/brief.md exists (created by /kiro-discovery), read it for discovery context (problem, approach, scope, constraints)
    • Use brief content as the project description instead of $ARGUMENTS
  2. Generate Feature Name:

    • Convert description to kebab-case
    • Example: "User profile with avatar upload" → "user-profile-avatar-upload"
    • Keep name concise (2-4 words ideally)
  3. Check Uniqueness:

    • Use Glob to check {{KIRO_DIR}}/specs/*/
    • If directory exists with only brief.md (no spec.json), use that directory (discovery created it)
    • Otherwise if feature name exists, append -2, -3, etc.
  4. Create Directory:

    • Use Bash: mkdir -p {{KIRO_DIR}}/specs/{feature-name} (skip if already exists from discovery)
  5. Initialize Files from Templates:

    a. Read templates:

    - {{KIRO_DIR}}/settings/templates/specs/init.json
    - {{KIRO_DIR}}/settings/templates/specs/requirements-init.md

    b. Replace placeholders:

    {{FEATURE_NAME}} → feature-name
    {{TIMESTAMP}} → current ISO 8601 timestamp (use `date -u +"%Y-%m-%dT%H:%M:%SZ"`)
    {{PROJECT_DESCRIPTION}} → description
    {{LANG_CODE}} → language code (detect from user's input language, default to `en`)

    c. Write files using Write tool:

    - {{KIRO_DIR}}/specs/{feature-name}/spec.json
    - {{KIRO_DIR}}/specs/{feature-name}/requirements.md
  6. Output Progress: "Phase 1/4 complete: Spec initialized at {{KIRO_DIR}}/specs/{feature-name}/"

Automatic Mode: IMMEDIATELY continue to Phase 2.

Interactive Mode: Prompt "Continue to requirements generation? (yes/no)"

  • If "no": Stop, show current state
  • If "yes": Continue to Phase 2

Phase 2: Generate Requirements

Invoke /kiro-spec-requirements {feature-name}.

Wait for completion. IGNORE any "Next Step" message (it is for standalone usage).

Output Progress: "Phase 2/4 complete: Requirements generated"

Automatic Mode: IMMEDIATELY continue to Phase 3.

Interactive Mode: Prompt "Continue to design generation? (yes/no)"

  • If "no": Stop, show current state
  • If "yes": Continue to Phase 3

Phase 3: Generate Design

Invoke /kiro-spec-design {feature-name} -y. The -y flag auto-approves requirements.

Wait for completion. IGNORE any "Next Step" message.

Output Progress: "Phase 3/4 complete: Design generated"

Automatic Mode: IMMEDIATELY continue to Phase 4.

Interactive Mode: Prompt "Continue to tasks generation? (yes/no)"

  • If "no": Stop, show current state
  • If "yes": Continue to Phase 4

Phase 4: Generate Tasks

Invoke /kiro-spec-tasks {feature-name} -y. The -y flag auto-approves requirements, design, and tasks.

Wait for completion.

Output Progress: "Phase 4/4 complete: Tasks generated"

Final Sanity Review

After Phase 4, run a lightweight sanity review before claiming completion.

  • Review requirements.md, design.md, and tasks.md directly from disk. If brief.md exists, use it only as supporting context.
  • Prefer a fresh review sub-agent when the host supports it. Pass only file paths and the review objective; the reviewer should read the generated files itself.
  • Review focus:
    • Do requirements, design, and tasks tell a coherent story?
    • Are there obvious contradictions, missing prerequisites, or missing task coverage for required design work?
    • Are _Depends:_, _Boundary:_, and (P) markers plausible for implementation?
  • If the review finds only task-plan-local issues, repair or update the generated tasks.md once, then re-run the sanity review.
  • If the review finds a real requirements/design gap or contradiction, stop and report follow-up instead of claiming the quick spec is implementation-ready.

All 4 phases plus sanity review complete.

Output final completion summary (see Output Description section) and exit.


Important Constraints

Error Handling

  • Any phase failure stops the workflow
  • Display error and current state
  • Suggest manual recovery command

Output Description

Mode Banners

Interactive Mode:

Quick Spec Generation (Interactive Mode)

You will be prompted at each phase.
Note: Skips gap analysis and design validation.

Automatic Mode:

Quick Spec Generation (Automatic Mode)

All phases execute automatically without prompts.
Note: Skips optional validations (gap analysis, design review) and user approval prompts. Internal review gates still run.
Final sanity review still runs.

Intermediate Output

After each phase, show brief progress:

Spec initialized at {{KIRO_DIR}}/specs/{feature}/
Requirements generated → Continuing to design...
Design generated → Continuing to tasks...

Final Completion Summary

Provide output in the language specified in spec.json:

Quick Spec Generation Complete!

## Generated Files:
- {{KIRO_DIR}}/specs/{feature}/spec.json
- {{KIRO_DIR}}/specs/{feature}/requirements.md ({X} requirements)
- {{KIRO_DIR}}/specs/{feature}/design.md ({Y} components, {Z} endpoints)
- {{KIRO_DIR}}/specs/{feature}/tasks.md ({N} tasks)

Quick generation skipped:
- `/kiro-validate-gap` - Gap analysis (integration check)
- `/kiro-validate-design` - Design review (architecture validation)

Sanity review: PASSED | FOLLOW-UP REQUIRED

## Next Steps:
1. Review generated specs (especially design.md)
2. Optional validation:
   - `/kiro-validate-gap {feature}` - Check integration with existing codebase
   - `/kiro-validate-design {feature}` - Verify architecture quality
3. Start implementation: `/kiro-impl {feature}`

Safety & Fallback

Error Scenarios

Template Missing:

  • Check {{KIRO_DIR}}/settings/templates/specs/ exists
  • Report specific missing file
  • Exit with error

Directory Creation Failed:

  • Check permissions
  • Report error with path
  • Exit with error

Phase Execution Failed (Phase 2-4):

  • Stop workflow
  • Show current state and completed phases
  • Suggest: "Continue manually from /kiro-spec-{next-phase} {feature}"

Sanity Review Failed:

  • Stop workflow
  • Report the exact contradiction, missing prerequisite, or task-plan issue
  • Suggest targeted follow-up with /kiro-spec-design {feature}, /kiro-spec-tasks {feature}, or manual edits depending on the finding

User Cancellation (Interactive Mode):

  • Stop gracefully
  • Show completed phases
  • Suggest manual continuation

Frequently asked questions

What does the Kiro Spec Quick AI skill do?

Quick spec generation with interactive or automatic mode

Why use Kiro Spec Quick on TypingMind?

Because you install it once and use it with any model. Kiro Spec Quick 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 Spec Quick 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/antigravity-skills/skills/kiro-spec-quick. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Kiro Spec Quick?

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 Spec Quick?

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

Is the Kiro Spec Quick 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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