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Kiro Debug

CommunityPopular
gotalab
kiro-debug

Investigate implementation failures using root-cause-first debugging. Use when an implementer is blocked, verification fails, or repeated remediation does not converge.

Overview

Publishergotalab
Repositorycc-sdd
Skill namekiro-debug
Stars
3.7K
Forks
283
Bundled files
1
LicenseMIT
Links
Skill sourceRepository
  • 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.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by gotalab on GitHub. Read the source before you install it.

Installation

Install the Kiro Debug 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-debug .claude/skills/kiro-debug
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Kiro Debug 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 Debug 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 Debug 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-debug

<background_information> This skill is for fresh-context root cause investigation. It combines local evidence, runtime/config inspection, and external documentation or issue research when available. It is not a patch generator for guess-first debugging. </background_information>

  • Implementer reports BLOCKED
  • Reviewer rejection repeats after remediation
  • Validation fails unexpectedly
  • A task appears to conflict with runtime or platform reality
  • The same failure survives more than one attempted fix

Do not use this skill to speculate about fixes before gathering evidence.

Inputs

Provide:

  • Exact failure symptom or blocker statement
  • Error messages, stack trace, and failing command output
  • Current git diff or summary of uncommitted failed changes
  • Task brief: what was being built
  • Reviewer feedback, if the failure came from review rejection
  • Relevant spec file paths (requirements.md, design.md)
  • Relevant requirement/design section numbers
  • Relevant ## Implementation Notes
  • Runtime or environment constraints already known

Outputs

Return:

  • ROOT_CAUSE
  • CATEGORY
  • FIX_PLAN
  • VERIFICATION
  • NEXT_ACTION: RETRY_TASK | BLOCK_TASK | STOP_FOR_HUMAN
  • CONFIDENCE: HIGH | MEDIUM | LOW
  • NOTES

Use the language specified in spec.json.

Method

1. Read the Error Carefully

Extract:

  • Exact error text
  • Stack trace or failure location
  • The command that produced the failure
  • Whether the failure is deterministic or intermittent

2. Inspect Local Runtime and Repository State

Inspect the repository for local evidence:

  • package.json, pyproject.toml, go.mod, Makefile, README*
  • Build config
  • tsconfig or equivalent language/runtime config
  • Runtime-specific config
  • Dependency versions and scripts
  • Relevant changed files from git diff

3. Search the Web if Available

If web access is available, search:

  • The exact error message
  • The technology + symptom combination
  • Official documentation
  • Version-specific issue trackers or migration notes

Prefer:

  • Official docs
  • Official repos/issues
  • Version-specific references
  • Runtime-specific documentation

4. Classify the Root Cause

Use one category:

  • MISSING_DEPENDENCY
  • RUNTIME_MISMATCH
  • MODULE_FORMAT
  • NATIVE_ABI
  • CONFIG_GAP
  • LOGIC_ERROR
  • TASK_ORDERING_PROBLEM
  • TASK_DECOMPOSITION_PROBLEM
  • SPEC_CONFLICT
  • EXTERNAL_DEPENDENCY

5. Determine the Smallest Safe Next Action

Decide whether the issue can be fixed inside this repo by:

  • Editing files
  • Adjusting configuration
  • Adding or correcting dependencies
  • Restructuring code

Use NEXT_ACTION: RETRY_TASK when the issue is repo-fixable inside the current approved task plan.

6. Determine Whether the Task Plan Is Still Valid

Decide whether the current approved task plan is still safe to execute as written.

Prefer NEXT_ACTION: STOP_FOR_HUMAN when:

  • A missing prerequisite task should exist before this one
  • The current task is ordered incorrectly relative to unfinished work
  • The current task boundary is wrong and should be split or merged
  • The task is too large or ambiguous to fix safely inside the current implementation loop

Use NEXT_ACTION: BLOCK_TASK only when the current task should stop but the rest of the queue can still proceed safely.

Do not propose a brute-force code fix as a substitute for revising tasks.md or the approved plan.

Critical Rule

Do not propose a multi-fix shotgun plan. Identify the root cause first, then produce the smallest plausible fix plan. If the true problem is a spec conflict or architecture problem, say so directly.

Stop / Escalate

Use NEXT_ACTION: STOP_FOR_HUMAN when the blocker genuinely requires:

  • Human product/requirements decision
  • External credentials or inaccessible services
  • Hardware or unavailable external systems
  • Re-scoping due to spec/platform conflict

If the issue is fixable by repo changes inside the current task plan, do not escalate prematurely.

Common Rationalizations

RationalizationReality
“This probably just needs a quick patch”Patch-first debugging creates rework.
“Let’s try a few fixes”Multi-fix guessing hides root cause.
“The spec is probably wrong, I’ll adapt it”Spec conflicts must be surfaced explicitly.
“The docs search is optional”For runtime/dependency issues, docs and version issues often contain the shortest path to root cause.

Output Format

md
## Debug Report
- ROOT_CAUSE: <1-2 sentence root cause>
- CATEGORY: MISSING_DEPENDENCY | RUNTIME_MISMATCH | MODULE_FORMAT | NATIVE_ABI | CONFIG_GAP | LOGIC_ERROR | TASK_ORDERING_PROBLEM | TASK_DECOMPOSITION_PROBLEM | SPEC_CONFLICT | EXTERNAL_DEPENDENCY
- FIX_PLAN:
  1. <specific repo-fixable action>
  2. <specific repo-fixable action>
- VERIFICATION: <command(s) to confirm the fix>
- NEXT_ACTION: RETRY_TASK | BLOCK_TASK | STOP_FOR_HUMAN
- CONFIDENCE: HIGH | MEDIUM | LOW
- NOTES: <context the next implementer should know>

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Kiro Debug AI skill do?

Investigate implementation failures using root-cause-first debugging. Use when an implementer is blocked, verification fails, or repeated remediation does not converge.

Why use Kiro Debug on TypingMind?

Because you install it once and use it with any model. Kiro Debug 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 Debug 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-debug. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Kiro Debug?

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 Debug?

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

Is the Kiro Debug 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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