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Kiwi AI

Official

@kiwi-home

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24Published Skills

Offers structured codebase analysis, project governance, and multi-specialist coordination protocols for enterprise software development lifecycles.

Skills Distribution
DomainDeveloper To...Codebase Analysis .. (35%)Project Governance.. (35%)Quality Assurance .. (30%)

Agent Skills by Kiwi AI

Showing 24 vetted skills indexed across 2 GitHub repositories.

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agent-discovery

Scans .claude/agents/*.md and ranks specialists by domain overlap and trigger context.

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quality-gates

Enforce structured quality gates across pipeline stages using workflow.yaml.

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context-resolution

Resolve project workflow context and git push targets from repository configuration files.

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environment-detection

Detect runtime and tool configuration for Python, Node, Ruby, Rust, or Go codebases.

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Intermediate
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adversarial-review

Analyze plans across six risk vectors and produce severity-tiered findings.

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questionnaire

Collect project setup preferences and map answers into workflow.yaml and quality-gate configuration.

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feedback-loop

Automate remediation iterations for blocking review findings with retry limits.

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context-file-generation

Generate subdirectory CLAUDE.md files with provenance markers and workflow.yaml reconciliation entries.

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brief-writer

Convert one-sentence intents into structured work briefs with YAML frontmatter and acceptance criteria.

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plan-writer

Generate structured implementation plans from design session outputs and briefs.

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deliverable-review

Evaluate delivered artifacts against the brief with severity tiers and disposition rules.

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asset-reconciliation

Detect, classify, and reconcile generated and manual project assets during setup re-runs.

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domain-detection

Detect project domain from repository file and directory signals.

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Intermediate
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stack-detection

Identify languages, frameworks, and domains in codebases via mapping tables.

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agent-patterns

Document agent frontmatter metadata spec and discovery patterns for .claude/agents/*.md files.

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deliberation-protocol

Coordinate multi-round specialist deliberations with conflict detection and escalation paths.

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tdd-patterns

Identify stack-aware TDD patterns and anti-patterns for test design.

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systematic-debugging

Guide hypothesis-driven debugging through a five-phase failure resolution process.

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agent-team-protocol

Coordinate parallel code execution teams with TDD workflows and Git strategy.

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codebase-analysis

Analyze codebases to reveal patterns and conventions for agent and skill design.

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issue-writer

Generate requirements-focused issues with acceptance criteria and constraints.

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Intermediate
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asset-discovery

Map existing skills and agents across project, user, and plugin layers.

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issue-workflow

Plan and execute GitHub issues with research, requirements extraction, and build-vs-buy evaluation.

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pr-review

Classify PR review findings by severity tiers for merge decisions.

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Frequently Asked Questions About Kiwi AI

FAQPage Schema
What specific tasks can I perform using Kiwi AI's skill manifest?

You can perform automated codebase analysis, generate structured implementation plans from briefs, conduct adversarial reviews across risk vectors, and reconcile project assets. These skills enable systematic debugging, requirement extraction, and the enforcement of quality gates within your repository configuration files.

Which personas benefit most from these development protocols?

Software architects, lead engineers, and technical project managers benefit from these protocols. The skills are designed for teams requiring rigorous documentation, standardized issue generation, and structured review processes to maintain consistency across complex, multi-specialist development environments.

What are the prerequisites for implementing these project governance skills?

Implementation requires a repository structure that supports .claude/agents/ metadata files and standard YAML configuration files. The system relies on existing repository signals, such as directory structures and configuration files, to detect project domains, runtime environments, and stack-specific TDD patterns.