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Kiro Spec Batch

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gotalab
kiro-spec-batch

Create complete specs (requirements, design, tasks) for all features in roadmap.md using parallel sub-agent dispatch by dependency wave.

Overview

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

Use it in TypingMind

Enable Kiro Spec Batch 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 Batch 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 Batch 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.

Spec Batch

<background_information>

  • Success Criteria:
    • All features have complete spec files (spec.json, requirements.md, design.md, tasks.md)
    • Dependency ordering respected (upstream specs complete before downstream)
    • Independent features processed in parallel via sub-agent dispatch
    • Cross-spec consistency verified (data models, interfaces, naming)
    • Mixed roadmap context understood without breaking ## Specs (dependency order) parsing
    • Controller context stays lightweight (sub-agents do the heavy work) </background_information>

Step 1: Read Roadmap and Validate

  1. Read {{KIRO_DIR}}/steering/roadmap.md
  2. Parse the ## Specs (dependency order) section to extract:
    • Feature names
    • One-line descriptions
    • Dependencies for each feature
    • Completion status ([x] = done, [ ] = pending)
  3. If present, also read for context:
    • ## Existing Spec Updates
    • ## Direct Implementation Candidates Do not include these in dependency-wave execution; they are awareness-only inputs for sequencing and consistency review.
  4. For each pending feature in ## Specs (dependency order), verify {{KIRO_DIR}}/specs/<feature>/brief.md exists
  5. If any brief.md is missing, stop and report: "Missing brief.md for: [list]. Run /kiro-discovery to generate briefs first."

Step 2: Build Dependency Waves

Group pending features into waves based on dependencies:

  • Wave 1: Features with no dependencies (or all dependencies already completed [x])
  • Wave 2: Features whose dependencies are all in Wave 1 or already completed
  • Wave N: Features whose dependencies are all in earlier waves or already completed

Display the execution plan:

Spec Batch Plan:
  Wave 1 (parallel): app-foundation
  Wave 2 (parallel): block-editor, page-management
  Wave 3 (parallel): sidebar-navigation, database-views
  Wave 4 (parallel): cli-integration
  Total: 6 specs across 4 waves

If roadmap contains ## Existing Spec Updates or ## Direct Implementation Candidates, mention them separately as non-batch items so the user can see the whole decomposition.

Step 3: Execute Waves

For each wave, dispatch all features in the wave as parallel sub-agents.

For each feature in the wave, spawn a sub-agent with this task:

Create a complete specification for feature "{feature-name}".

1. Read the brief at {{KIRO_DIR}}/specs/{feature-name}/brief.md for feature context
2. Read the roadmap at {{KIRO_DIR}}/steering/roadmap.md for project context
3. Execute the full spec pipeline. For each phase, read the corresponding skill's SKILL.md for complete instructions (templates, rules, review gates):
   a. Initialize: Read .agent/skills/kiro-spec-init/SKILL.md, then create spec.json and requirements.md
   b. Generate requirements: Read .agent/skills/kiro-spec-requirements/SKILL.md, then follow its steps
   c. Generate design: Read .agent/skills/kiro-spec-design/SKILL.md, then follow its steps
   d. Generate tasks: Read .agent/skills/kiro-spec-tasks/SKILL.md, then follow its steps
4. Set all approvals to true in spec.json (auto-approve mode, equivalent of -y flag)
5. Report completion with file list and task count

Antigravity does not support programmatic sub-agent dispatch. Execute features in the wave sequentially in the main context.

After all sub-agents in the wave complete:

  1. Verify each feature has: spec.json, requirements.md, design.md, tasks.md
  2. If any feature failed, report the error and continue with features that succeeded
  3. Display wave completion: "Wave N complete: [features]. Files verified."
  4. Proceed to next wave

Step 4: Cross-Spec Review

After all waves complete, perform a cross-spec consistency review. This is the highest-value quality gate -- it catches issues that per-spec review gates cannot.

Read ALL generated specs and check for consistency across the entire project:

  • {{KIRO_DIR}}/specs/*/design.md (primary: contains interfaces, data models, architecture)
  • {{KIRO_DIR}}/specs/*/requirements.md (for scope and acceptance criteria)
  • {{KIRO_DIR}}/specs/*/tasks.md (for boundary annotations only -- read Boundary: lines, skip task descriptions)
  • {{KIRO_DIR}}/steering/roadmap.md

Reading priority: Focus on design.md files (they contain interfaces, data models, architecture). For requirements.md, focus on section headings and acceptance criteria. For tasks.md, focus on Boundary: annotations.

Check:

  1. Data model consistency: Same entities defined consistently across specs (field names, types, relationships)
  2. Interface alignment: Where spec A outputs what spec B consumes, do contracts match exactly?
  3. No duplicate functionality: Any capability specified in more than one spec?
  4. Dependency completeness: Every design.md references correct upstream specs? Implicit dependencies not in roadmap?
  5. Naming conventions: Component names, file paths, API routes, table names consistent across specs?
  6. Shared infrastructure: Shared concerns (auth, error handling, logging) handled in one spec and correctly referenced?
  7. Task boundary alignment: Task Boundary: annotations partition codebase cleanly? No files claimed by multiple specs?
  8. Roadmap boundary continuity: If roadmap includes Existing Spec Updates or Direct Implementation Candidates, do the generated new specs avoid absorbing that work by accident?
  9. Architecture boundary integrity: Do the specs preserve clean responsibility seams, avoid shared ownership, keep dependency direction coherent, and include enough revalidation triggers to catch downstream impact?
  10. Change-friendly decomposition: Has any spec absorbed multiple independent seams that should probably be split instead of kept together?

Output: CONSISTENT areas + ISSUES with (which specs, what's inconsistent, suggested fix).

After the review completes:

  • Critical/important issues found: Fix each affected spec to apply the suggested fixes. If the issue is really a decomposition problem (for example boundary overlap or one spec carrying multiple independent seams), stop and return to roadmap/discovery instead of papering over it locally. Re-run cross-spec review after fixes (max 3 remediation rounds).
  • Minor issues only: Report them for user awareness, proceed to Step 5.
  • No issues: Proceed to Step 5.

Step 5: Finalize

  1. Scan {{KIRO_DIR}}/specs/*/tasks.md to verify all specs exist
  2. For each completed spec, read spec.json to confirm phase and approvals
  3. Update roadmap.md: mark completed specs as [x]
  4. If roadmap.md includes Existing Spec Updates or Direct Implementation Candidates, leave them untouched and mention them as remaining follow-up items unless already explicitly completed elsewhere

Display final summary:

Spec Batch Complete:
  ✓ app-foundation: X requirements, Y design components, Z tasks
  ✓ block-editor: ...
  ✓ page-management: ...
  ...
  Total: N specs created, M tasks generated
  Cross-spec review: PASSED / N issues found (M fixed)
  Existing spec updates pending: <count or none>
  Direct implementation candidates pending: <count or none>

Next: Review generated specs, then start implementation with /kiro-impl <feature>

Critical Constraints

  • Controller stays lightweight: Only read roadmap.md and brief.md existence checks in main context. All spec generation happens in sub-agents.
  • Wave ordering is strict: Never start a wave until all features in previous waves are complete.
  • Parallel within waves: All features in the same wave should be dispatched in parallel if multi-agent is available.
  • No partial waves: If a feature in a wave fails, still complete the other features in that wave before reporting.
  • Skip completed specs: Features with [x] in roadmap.md or existing tasks.md are skipped.
  • ## Specs (dependency order) remains authoritative for batch execution: Other roadmap sections are context, not wave inputs.

Safety & Fallback

Sub-agent failure:

  • Log the error, skip the failed feature
  • Continue with remaining features in the wave
  • Report failed features in the summary
  • Suggest: "Run /kiro-spec-quick <feature> --auto manually for failed features."

Circular dependencies:

  • If dependency graph has cycles, report the cycle and stop
  • Suggest: "Fix dependency ordering in roadmap.md"

Roadmap not found:

  • Stop and report: "No roadmap.md found. Run /kiro-discovery first."

All specs already complete:

  • Report: "All specs in roadmap.md are already complete. Nothing to do."

Frequently asked questions

What does the Kiro Spec Batch AI skill do?

Create complete specs (requirements, design, tasks) for all features in roadmap.md using parallel sub-agent dispatch by dependency wave.

Why use Kiro Spec Batch on TypingMind?

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

Which AI models can use Kiro Spec Batch?

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 Batch?

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

Is the Kiro Spec Batch 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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