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Kiro Spec Tasks

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gotalab
kiro-spec-tasks

Generate implementation tasks from requirements and design. Use when creating actionable task lists.

Overview

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

Use it in TypingMind

Enable Kiro Spec Tasks 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 Tasks 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 Tasks 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-spec-tasks Skill

Core Mission

  • Success Criteria:
    • All requirements mapped to specific tasks
    • Tasks properly sized (1-3 hours each)
    • Clear task progression with proper hierarchy
    • Natural language descriptions focused on capabilities
    • A lightweight task-plan sanity review confirms the task graph is executable before tasks.md is written

Execution Steps

Step 1: Gather Context

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

  • {{KIRO_DIR}}/specs/{feature}/spec.json, requirements.md, design.md

  • {{KIRO_DIR}}/specs/{feature}/tasks.md (if exists, for merge mode)

  • Core steering context: product.md, tech.md, structure.md

  • Additional steering files only when directly relevant to requirements coverage, design boundaries, runtime prerequisites, or team conventions that affect task executability

  • Determine execution mode:

    • sequential = (sequential flag is true)

Validate approvals:

  • If auto-approve flag (-y) is true: Auto-approve requirements and design in spec.json. Tasks approval is also handled automatically in Step 4.
  • Otherwise: Verify both approved (stop if not, see Safety & Fallback)

Step 2: Generate Implementation Tasks

  • Read rules/tasks-generation.md from this skill's directory for principles
  • Read rules/tasks-parallel-analysis.md from this skill's directory for parallel judgement criteria
  • Read {{KIRO_DIR}}/settings/templates/specs/tasks.md for format (supports (P) markers)
Parallel Research

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

  1. Context loading: Spec documents (requirements.md, design.md), steering files
  2. Rules loading: tasks-generation.md, tasks-parallel-analysis.md, tasks template

After all parallel research completes, synthesize findings before generating tasks.

Generate task list following all rules:

  • Use language specified in spec.json
  • Map all requirements to tasks and list numeric requirement IDs only (comma-separated) without descriptive suffixes, parentheses, translations, or free-form labels
  • Ensure all design components included
  • Verify task progression is logical and incremental
  • Ensure each executable sub-task includes at least one detail bullet that states what "done" looks like in observable terms
  • Keep normal implementation tasks within a single responsibility boundary; if work crosses boundaries, make it an explicit integration task
  • Apply (P) markers to tasks that satisfy parallel criteria when !sequential
  • Explicitly note dependencies preventing (P) when tasks appear parallel but are not safe
  • If sequential mode is true, omit (P) entirely
  • If existing tasks.md found, merge with new content

Step 3: Review Task Plan

  • Keep the draft task plan in working memory; do NOT write tasks.md yet
  • Run the Task Plan Review Gate from rules/tasks-generation.md
  • Review coverage:
    • Every requirement ID appears in at least one task
    • Every design component, contract, integration point, runtime prerequisite, and validation concern is represented
  • Review executability:
    • Each sub-task is an executable 1-3 hour work unit
    • Each sub-task has a verifiable deliverable
    • Each executable sub-task includes an observable completion bullet
    • No implicit prerequisites remain hidden
    • _Depends:_, _Boundary:_, and (P) markers still match the dependency graph and architecture boundaries
  • If issues are task-plan-local, repair the draft and re-run the review gate before writing
  • Keep the review bounded to at most 2 repair passes
  • If review exposes a real requirements/design gap or contradiction, stop and send the user back to requirements/design instead of inventing filler tasks

Step 3.5: Run Task-Graph Sanity Review

Before writing tasks.md, run one lightweight independent sanity review of the task graph.

  • If fresh subagent dispatch is available, spawn one fresh review subagent for this step. Otherwise perform the same review in the current context.
  • Provide only file paths, the draft task plan, and merge context if an existing tasks.md is being updated. The reviewer should read requirements.md, design.md, and the task-generation rules directly instead of relying on a parent-synthesized coverage summary.
  • Check only:
    • hidden prerequisites or missing setup tasks
    • dependency or ordering mistakes
    • boundary overlap or ambiguous ownership between tasks
    • tasks that are too large, too vague, cross boundaries without being explicit integration tasks, or are missing a verifiable deliverable
    • contradictions introduced between requirements, design, and the task graph
  • Return one verdict:
    • PASS
    • NEEDS_FIXES
    • RETURN_TO_DESIGN
  • If NEEDS_FIXES, repair the draft once and re-run the sanity review one time.
  • If RETURN_TO_DESIGN, stop without writing tasks.md and point back to the exact gap in requirements/design.
  • Keep this bounded. Do not turn it into a second full planning cycle.

Step 4: Finalize

Write tasks.md:

  • Create/update {{KIRO_DIR}}/specs/{feature}/tasks.md
  • Update spec.json metadata:
    • Set phase: "tasks-generated"
    • Set approvals.tasks.generated: true, approved: false
    • Set approvals.requirements.approved: true
    • Set approvals.design.approved: true
    • Update updated_at timestamp

Approval:

  • If auto-approve flag (-y) is true:
    • Set approvals.tasks.approved: true in spec.json
    • Display task summary (task count, major groups, parallel markers)
    • Respond: "Tasks generated and auto-approved. Start implementation with /kiro-impl {feature}"
  • Otherwise (interactive):
    • Display a summary of the generated tasks (task count, major groups, parallel markers)
    • Ask the user: "Tasks generated. Approve and proceed to implementation?"
    • If the user approves:
      • Set approvals.tasks.approved: true in spec.json
      • Respond: "Tasks approved. Start implementation with /kiro-impl {feature}"
    • If the user wants changes:
      • Keep approvals.tasks.approved: false
      • Respond with guidance on what to adjust and re-run

Critical Constraints

  • Task Integration: Every task must connect to the system (no orphaned work)
  • Boundary annotations: Required for (P) tasks, recommended for all (_Boundary: ComponentName_)
  • Explicit dependencies: Cross-boundary non-obvious dependencies declared with _Depends: X.X_
  • Executable deliverable granularity: Each task must produce a verifiable deliverable (file, endpoint, UI component, config). Infrastructure tasks (project scaffolding, manifest, host integration, build config) must be explicit — never assume they exist
  • Observable done state: Each executable sub-task must include at least one detail bullet that makes the completed state visible without adding new bookkeeping fields
  • No implicit prerequisites: If a task requires a runtime, SDK, framework setup, or config file, that setup must be a separate preceding task

Output Description

Provide brief summary in the language specified in spec.json:

  1. Status: Confirm tasks generated at {{KIRO_DIR}}/specs/{feature}/tasks.md
  2. Task Summary:
    • Total: X major tasks, Y sub-tasks
    • All Z requirements covered
    • Average task size: 1-3 hours per sub-task
  3. Quality Validation:
    • All requirements mapped to tasks
    • Design coverage and runtime prerequisites reviewed
    • Task dependencies verified
    • Task plan review gate passed
    • Independent task-graph sanity review passed
    • Testing tasks included
  4. Next Action: Review tasks and proceed when ready

Format: Concise (under 200 words)

Safety & Fallback

Error Scenarios

Requirements or Design Not Approved:

  • Stop Execution: Cannot proceed without approved requirements and design
  • User Message: "Requirements and design must be approved before task generation"
  • Suggested Action: "Run /kiro-spec-tasks {feature} -y to auto-approve all (requirements, design, and tasks) and proceed"

Missing Requirements or Design:

  • Stop Execution: Both documents must exist
  • User Message: "Missing requirements.md or design.md at {{KIRO_DIR}}/specs/{feature}/"
  • Suggested Action: "Complete requirements and design phases first"

Incomplete Requirements Coverage:

  • Warning: "Not all requirements mapped to tasks. Review coverage."
  • User Action Required: Confirm intentional gaps or regenerate tasks

Spec Gap Found During Task Review:

  • Stop Execution: Do not write a patched-over tasks.md
  • User Message: "Requirements/design do not provide enough clear coverage to generate an executable task plan"
  • Suggested Action: "Refine requirements.md or design.md, then re-run /kiro-spec-tasks {feature}"

Template/Rules Missing:

  • User Message: "Template or rules files missing in {{KIRO_DIR}}/settings/"
  • Fallback: Use inline basic structure with warning
  • Suggested Action: "Check repository setup or restore template files"
  • Missing Numeric Requirement IDs:
    • Stop Execution: All requirements in requirements.md MUST have numeric IDs. If any requirement lacks a numeric ID, stop and request that requirements.md be fixed before generating tasks.

Next Phase: Implementation

Tasks are approved in Step 4 via user confirmation. Once approved:

  • Autonomous implementation: /kiro-impl {feature}
  • Specific tasks only: /kiro-impl {feature} 1.1,1.2

Frequently asked questions

What does the Kiro Spec Tasks AI skill do?

Generate implementation tasks from requirements and design. Use when creating actionable task lists.

Why use Kiro Spec Tasks on TypingMind?

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

Which AI models can use Kiro Spec Tasks?

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 Tasks?

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

Is the Kiro Spec Tasks 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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