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melodic-software

melodic-software

484 Skills published on GitHub.

git-worktree-setup

Set up Git worktrees for agent parallelization with isolated environments. Use when setting up parallel agent execution, creating isolated environments per agent, or enabling concurrent development workflows.

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hook-event-architecture

Design hook-based event systems for ADW observability. Use when implementing real-time event broadcasting, creating hook pipelines, or building agent activity monitoring.

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issue-classification

Configure issue classification for ADWs to route work to the correct templates. Use when setting up automatic classification of GitHub issues into chores, bugs, and features.

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leverage-point-audit

Audit a codebase for the 12 leverage points of agentic coding. Identifies gaps and provides prioritized recommendations. Use when improving agentic coding capability, analyzing why agents fail, or optimizing a codebase for autonomous work.

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list-agent-tools

List available tools for custom agent configuration. Use as reference when defining agent tool access.

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list-prompt-levels

Show the seven levels of agentic prompts with quick reference. Use when deciding prompt complexity level.

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load-context-bundle

Reload previous session context from a context bundle file. Use to resume work from a previous session.

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meta-agentic-creation

Create meta prompts, meta agents, and meta skills that build other agentic components. Use when scaling agentic layer development, creating generators/templates, or implementing "build the system that builds the system" patterns.

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minimum-viable-agentic

Guide creation of minimum viable agentic layer for a codebase. Use when starting agentic coding in a new project, bootstrapping essential components, or creating the minimal scaffolding for agent success.

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model-selection

Choose appropriate model for custom agent tasks. Use when selecting between Haiku, Sonnet, and Opus for agents, optimizing cost vs quality tradeoffs, or matching model capability to task complexity.

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multi-agent-observability

Build observability interfaces for multi-agent systems. Use when monitoring multi-agent execution, tracking agent metrics, implementing logging for parallel agents, or debugging agent workflows.

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orchestrate

Run orchestration workflow for a complex task using multi-agent patterns. Use when a task requires coordinated scout-build-review phases.

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orchestration-prompts

Write prompts for orchestrator workflows with phases and aggregation. Use when designing multi-phase workflows, writing agent command prompts, or implementing result aggregation patterns.

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orchestrator-design

Design O-Agent systems for multi-agent fleet management. Use when building orchestrator agents, designing multi-agent architectures, or creating unified interfaces for agent fleet control.

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patch-design

Create minimal, surgical patch plans for targeted fixes. Use when fixing specific issues from review, creating focused patches, or avoiding scope creep in fix implementations.

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patch

Create minimal surgical patch plan for targeted fix. Use for small, focused changes rather than full feature implementations.

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piter-setup

Set up PITER framework elements for AFK agent systems. Use when configuring prompt input sources, triggers, environments, and review processes for autonomous agent workflows.

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plan-generation

Assist in generating comprehensive plans from templates using extended thinking. Use when generating plans from meta-prompt templates, reviewing plan quality, or ensuring plans meet team standards.

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product-expert-design

Design user-facing agent experts for adaptive UX and personalization. Use when building product features that learn from user behavior, creating per-user expertise files, or implementing AI-driven personalization.

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prompt-level-selection

Guide selection of appropriate prompt level for a task. Use when choosing between simple prompts and complex workflows, applying the seven levels framework, or matching task complexity to prompt investment.

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prompt-section-design

Design composable prompt sections for building agentic prompts. Use when creating reusable prompt components, designing LEGO-block prompt sections, or structuring prompts for the stakeholder trifecta.

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pull-request

Create a GitHub pull request with full context linking back to the original issue. Use after implementation is complete and ready for review.

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query-expert

Ask an agent expert a question using its expertise mental model. Use for quick domain-specific answers without code exploration.

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reduce-delegate-framework

Apply R&D framework to optimize prompts and context. Use when optimizing context window usage, reducing prompt size, delegating to specialized agents, or applying systematic context management.

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resolve-failed-e2e-test

Analyze a failed E2E test, fix the underlying issue, and verify the fix. Use after /test-e2e reports failures.

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resolve-failed-test

Analyze a failed test, fix the underlying issue, and verify the fix. Use after /test reports failures.

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review-workflow-design

Design spec-based review workflows with visual proof and issue classification. Use when setting up review processes, validating against specifications, or implementing screenshot-based visual validation.

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scaffold-layer

Create minimum viable agentic layer structure for a project. Use when adding agentic capabilities to a new project.

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scout-and-build

Execute the scout-then-build pattern for a feature implementation. Use when you need to explore the codebase before implementing changes.

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seed-expertise

Generate initial expertise.yaml from codebase exploration. Use to bootstrap a new agent expert's mental model.

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self-improve-prompt-design

Write self-improve prompts that sync expertise files with codebase reality. Use when creating maintenance workflows for agent experts, designing validation logic, or implementing the LEARN step of Act-Learn-Reuse.

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ship

Validate state and merge branch to main for production deployment. Use as final ZTE step after all checks pass.

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standard-out-setup

Add console output and logging to make errors visible to agents. Standard out is a critical leverage point - without it, agents cannot see errors or understand application state. Use when agents fail silently, when debugging agentic workflows, or when setting up a new codebase for agentic coding.

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start-adw

Start an AI Developer Workflow with composable steps. Use when executing plan_build, plan_build_review, or plan_build_review_fix workflows.

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inverse-conway

Align architecture and team structure using inverse Conway maneuver

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swimlane-visualization

Design swimlane UI patterns for visualizing ADW execution. Use when building observability dashboards, monitoring agent workflows, or creating real-time status displays.

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system-prompt-engineering

Design effective system prompts for custom agents. Use when creating agent system prompts, defining agent identity and rules, or designing high-impact prompts that shape agent behavior.

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task-based-multiagent

Set up task-based multi-agent systems with shared task files. Use when setting up parallel agent execution, designing worktree isolation patterns, or implementing task file coordination.

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template-engineering

Guide creation of meta-prompt templates that encode engineering workflows into reusable, scalable units. Use when creating slash commands that generate plans, designing workflow templates, or encoding team best practices into agentic prompts.

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template-meta-prompt-creation

Create Level 6 template meta-prompts that generate other prompts. Use when building prompt generators, designing high-leverage meta-prompts, or creating templates that scaffold other prompts.

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test-e2e

Execute end-to-end test specification and report results. Use after implementation to validate user journeys before review.

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test-suite-setup

Set up test validation commands for any project type. Use when configuring test runners, setting up validation commands for a new project, or enabling closed-loop agent workflows.

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test

Run project test suite and report results in structured JSON format. Use to validate implementation before commit or review.

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tool-design

Create custom tools using the @tool decorator for domain-specific agents. Use when building agent-specific tools, implementing MCP servers, or creating in-memory tools with the Agent SDK.

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tools

List all available Claude Code tools with their parameters and capabilities. Use to understand current tool availability before starting a task.

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track-kpis

Calculate and update agentic coding KPIs to measure ZTE progression. Use after completing an ADW cycle to track metrics.

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upgrade-prompt

Upgrade an existing prompt to a higher level. Use when a prompt needs more advanced capabilities (control flow, delegation, etc.).

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validate-workflow

Validate AI Developer Workflow step outputs and contracts. Use when verifying workflow step completeness before proceeding.

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zte-progression

Guide progression from In-Loop to Out-Loop to Zero-Touch Engineering. Use when assessing agentic maturity, planning ZTE progression, or identifying requirements for autonomous operation.

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cognitive-load-assessment

Measure and manage team cognitive load

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