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Kiro Validate Impl

CommunityPopular
gotalab
kiro-validate-impl

Validate feature-level integration after all tasks are implemented. Checks cross-task consistency, full test suite, and overall spec coverage.

Overview

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

Use it in TypingMind

Enable Kiro Validate Impl 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 Impl 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 Impl 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.

Implementation Integration Validation

<background_information> Individual tasks are usually reviewed during implementation. Your job is to catch problems that only become visible when looking across all tasks together.

Boundary terminology continuity:

  • discovery identifies Boundary Candidates

  • design fixes Boundary Commitments

  • tasks constrain execution with _Boundary:_

  • feature validation checks for cross-task Boundary Violations

  • Success Criteria:

    • All tasks marked [x] in tasks.md
    • Full test suite passes (not just per-task tests)
    • Cross-task integration works (data flows between components, interfaces match)
    • Requirements coverage is complete across all tasks (no gaps between tasks)
    • Design structure is reflected end-to-end (not just per-component)
    • No orphaned code, conflicting implementations, integration seams, or boundary spillover

What This Skill Does NOT Do: This skill is not a full replacement for task-local review during /kiro-impl. This skill does NOT re-check every individual task acceptance criterion, every per-file reality check, or every single-task spec detail unless a concrete integration finding forces it.

This skill's main question is: when the completed tasks are viewed together, do they still respect the designed boundary seams and dependency direction? </background_information>

1. Detect Validation Target

If no arguments provided ($1 empty):

  • Parse conversation history for /kiro-impl <feature> [tasks] commands
  • Extract feature names and task numbers from each execution
  • Aggregate all implemented tasks by feature
  • Report detected implementations (e.g., "user-auth: 1.1, 1.2, 1.3")
  • If no history found, scan {{KIRO_DIR}}/specs/ for features with completed tasks [x]

If feature provided ($1 present, $2 empty):

  • Use specified feature
  • Detect all completed tasks [x] in {{KIRO_DIR}}/specs/$1/tasks.md

If both feature and tasks provided ($1 and $2 present):

  • Validate specified feature and tasks only (e.g., user-auth 1.1,1.2)
Sub-agent Dispatch (parallel)

The following validation dimensions are independent and can be dispatched as sub-agents. The agent should decide the optimal decomposition based on feature scope — split, merge, or skip sub-agents as appropriate. Each sub-agent returns a structured findings summary to keep the main context clean for GO/NO-GO synthesis.

Typical validation dimensions (adjust as appropriate):

  • Test execution: Run the complete test suite, report pass/fail with details
  • Requirements coverage: Build requirements → implementation matrix, report gaps
  • Design alignment: Verify architecture matches design.md, report drift and dependency violations
  • Cross-task integration: Verify data flows, API contracts, shared state consistency

If multi-agent is not available, run checks sequentially in main context.

After all checks complete, synthesize findings for GO/NO-GO/MANUAL_VERIFY_REQUIRED assessment.

If the implementation run explicitly skipped task-local review (for example --review off), tighten scrutiny on obvious task-level gaps that surface during integration validation and call out that reduced review coverage in the report.

2. Load Context

For each detected feature:

  • Read {{KIRO_DIR}}/specs/<feature>/spec.json for metadata
  • Read {{KIRO_DIR}}/specs/<feature>/requirements.md for requirements
  • Read {{KIRO_DIR}}/specs/<feature>/design.md for design structure
  • Read {{KIRO_DIR}}/specs/<feature>/tasks.md for task list and Implementation Notes
  • Core steering context: product.md, tech.md, structure.md
  • Additional steering files only when directly relevant to the validated boundaries, runtime prerequisites, integrations, domain rules, security/performance constraints, or team conventions that affect the GO/NO-GO call

Discover canonical validation commands:

  • Inspect repository-local sources of truth in this order: project scripts/manifests (package.json, pyproject.toml, go.mod, Cargo.toml, app manifests), task runners (Makefile, justfile), CI/workflow files, existing e2e/integration configs, then README*
  • Derive a feature-level validation set for this repo: TEST_COMMANDS, BUILD_COMMANDS, and SMOKE_COMMANDS
  • Prefer commands already used by repo automation over ad hoc shell pipelines
  • For SMOKE_COMMANDS, choose the lightest trustworthy runtime-liveness check for the app shape (for example: root URL load, Electron launch, CLI --help, service health endpoint, mobile simulator/e2e harness if one already exists)
  • If multiple candidates exist, prefer the command with the smallest setup cost that still exercises the real built artifact

3. Execute Integration Validation

Mechanical Checks (run commands, use results)

A. Full Test Suite

  • Run the discovered canonical full-test command. Use the exit code.
  • If tests fail → NO-GO. No judgment needed.
  • If the canonical test command cannot be identified → MANUAL_VERIFY_REQUIRED

B. Residual TBD/TODO/FIXME

  • Run: grep -rn "TBD\|TODO\|FIXME\|HACK\|XXX" <files-in-feature-boundary>
  • If matches found that were introduced by this feature → flag as Warning

C. Residual Hardcoded Secrets

  • Run: grep -rn "password\s*=\|api_key\s*=\|secret\s*=\|token\s*=" <files-in-feature-boundary> (case-insensitive)
  • If matches found that aren't environment variable references → flag as Critical

D. Runtime Liveness (Smoke Boot)

  • Run the discovered canonical smoke command that proves the built artifact actually starts and reaches its first usable state.
  • Examples if relevant: open the root URL in a headless browser and require zero boot-time console errors; launch Electron and wait for the main process ready signal and first renderer load; run a CLI with --help; start a service and hit its health endpoint.
  • If boot produces a runtime crash, unhandled exception, module-load failure, native ABI mismatch, or missing required env/config → NO-GO.
  • If no trustworthy smoke command can be identified, or the required runtime environment is unavailable → MANUAL_VERIFY_REQUIRED
Judgment Checks (read code, compare to spec)

E. Cross-Task Integration

  • Identify where tasks share interfaces, data models, or API contracts
  • Verify that Task A's output format matches Task B's expected input
  • Check for conflicting assumptions between tasks (naming conventions, error codes, data shapes)
  • Verify shared state (database schemas, config, environment) is consistent across tasks
  • Verify integration work happens at the intended seams rather than by leaking one boundary's behavior into another

F. Requirements Coverage Gaps

  • Map every requirement section to at least one completed task
  • Identify requirements that no single task fully covers (cross-cutting requirements)
  • Identify requirements partially covered by multiple tasks but not fully by any
  • Use the original section numbering from requirements.md; do NOT invent REQ-* aliases

G. Design End-to-End Alignment

  • Verify the overall component graph matches design.md
  • Check that integration patterns (event flow, API boundaries, dependency injection) work as designed
  • Verify dependency direction follows design.md's architecture (no upward imports)
  • Verify File Structure Plan matches the actual file layout
  • Identify any architectural drift from the original design
  • Use the original section numbering from design.md

G.5 Boundary Audit

  • Compare completed work against the design's Boundary Commitments, Out of Boundary, Allowed Dependencies, and Revalidation Triggers
  • Identify cross-task spillover where one area quietly absorbed another boundary's responsibility
  • Identify downstream-specific workarounds embedded upstream "to make integration easier"
  • Identify new hidden dependencies or shared ownership that were not declared in the design
  • If a revalidation trigger fired, verify the affected adjacent specs or integration points were actually re-checked

H. Blocked Tasks & Implementation Notes

  • Check for any tasks still marked _Blocked:_ — report why and assess impact on feature completeness
  • Review ## Implementation Notes in tasks.md for cross-cutting insights that need attention

4. Generate Report

Before returning GO, apply the kiro-verify-completion protocol to the feature-level claim. Tests alone are insufficient: include full-suite, runtime liveness, coverage, integration, design-alignment, and blocked-task status in the evidence.

Classify concrete failures by ownership before writing remediation:

  • LOCAL if the defect belongs to the feature being validated
  • UPSTREAM if the root cause belongs to a dependency, foundation, shared platform, or earlier spec
  • UNCLEAR if ownership cannot be established from the available evidence

If ownership is UPSTREAM, do not collapse the issue into local remediation for this feature. Name the owning upstream spec and explain which dependent specs should be revalidated after that upstream fix lands.

Provide summary in the language specified in spec.json:

## Validation Report
- DECISION: GO | NO-GO | MANUAL_VERIFY_REQUIRED
- MECHANICAL_RESULTS:
  - Tests: PASS | FAIL (command and exit code)
  - TBD/TODO grep: CLEAN | <count> matches
  - Secrets grep: CLEAN | <count> matches
  - Smoke boot: PASS | FAIL | MANUAL_REQUIRED
- INTEGRATION:
  - Cross-task contracts: <status>
  - Shared state consistency: <status>
  - Boundary audit: <status>
- COVERAGE:
  - Requirements mapped: <X/Y sections covered>
  - Coverage gaps: <list of uncovered requirement sections>
- DESIGN:
  - Architecture drift: <findings>
  - Dependency direction: <violations if any>
  - File Structure Plan vs actual: <match/mismatch>
- OWNERSHIP: LOCAL | UPSTREAM | UNCLEAR
- UPSTREAM_SPEC: <feature-name | N/A>
- BLOCKED_TASKS: <list and impact assessment>
- REMEDIATION: <if NO-GO: specific, actionable steps to fix each issue>

If NO-GO, REMEDIATION is mandatory — identify the exact issue and what needs to change.

Important Constraints

  • Strict Final Gate: Return GO only when all integration checks passed; return NO-GO for concrete failures and MANUAL_VERIFY_REQUIRED when mandatory validation could not be completed
  • Boundary integrity over convenience: Do not return GO if the feature only works by smearing responsibilities across boundaries, even when tests pass

Safety & Fallback

Error Scenarios

  • No Implementation Found: If no /kiro-impl in history and no [x] tasks, report "No implementations detected"
  • Test Command Unknown: Return MANUAL_VERIFY_REQUIRED and explain which validation command is missing; do not return GO
  • Missing Spec Files: Stop with error if spec.json/requirements.md/design.md missing

Next Steps Guidance

If GO Decision:

  • Feature validated end-to-end and ready for deployment or next feature

If NO-GO Decision:

  • Address integration issues listed
  • Re-run /kiro-impl <feature> [tasks] for targeted fixes
  • Re-validate with /kiro-validate-impl [feature]

Session Interrupted:

  • Safe to re-run — validation is read-only and idempotent

If MANUAL_VERIFY_REQUIRED:

  • Do not treat the feature as complete
  • Provide the exact missing validation step or environment prerequisite

Frequently asked questions

What does the Kiro Validate Impl AI skill do?

Validate feature-level integration after all tasks are implemented. Checks cross-task consistency, full test suite, and overall spec coverage.

Why use Kiro Validate Impl on TypingMind?

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

Which AI models can use Kiro Validate Impl?

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 Impl?

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

Is the Kiro Validate Impl 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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