benjaminshoemaker

Generate EXECUTION_PLAN.md and scoped AGENTS.md files for a feature. Use after /feature-technical-spec to create the task breakdown.

Generate the greenfield execution plan plus root and scoped AGENTS.md files. Use after /technical-spec to create the phased task breakdown.

Guide discovery and discussion to determine how an AI coding agent can autonomously run and verify a specific user flow end to end. Use when the user asks what would be needed to test a flow, prove an app works, create an E2E/self-test harness, make agent verification repeatable, or evaluate whether an existing test setup lets the agent verify behavior. This skill is for shaping the verification approach before implementation: read the repo's product docs and existing verification contracts, identify the real user flow and channel under test, uncover missing controllable state or external dependencies, ask focused questions, and produce a concrete agent-runnable verification plan only after the flow is understood.

Exploratory conversation before /product-spec. Asks one question at a time, runs background research for open-source/off-the-shelf alternatives, and writes DISCOVERY_NOTES.md. Use when the user has an idea but hasn't scoped it yet.

Define feature requirements (problem, users, scope, acceptance criteria) through guided Q&A and write FEATURE_SPEC.md. Use when starting a new feature.

Define technical approach (architecture, integration points, data model) for a feature through guided Q&A and write FEATURE_TECHNICAL_SPEC.md. Use after /feature-spec.

Orient to project structure and load context. Use at the start of each new session or after context reset to understand the project state.

Resume execution from wherever you left off. Detects current state and runs the appropriate next command, or reports what's blocking progress. Use at the start of any session to pick up where you left off.

Delegate code implementation to Codex CLI. Claude decomposes, scopes context, and verifies; Codex implements. Accepts a spec file, text description, or gathers requirements interactively.

Exploratory conversation before /product-spec. Asks one question at a time, runs background research for open-source/off-the-shelf alternatives, and writes DISCOVERY_NOTES.md. Use when the user has an idea but hasn't scoped it yet.

Generate EXECUTION_PLAN.md and scoped AGENTS.md files for a feature. Use after /feature-technical-spec to create the task breakdown.

Define feature requirements (problem, users, scope, acceptance criteria) through guided Q&A and write FEATURE_SPEC.md. Use when starting a new feature.

Define technical approach (architecture, integration points, data model) for a feature through guided Q&A and write FEATURE_TECHNICAL_SPEC.md. Use after /feature-spec.

Orient to project structure and load context. Use at the start of each new session or after context reset to understand the project state.

Generate the greenfield execution plan plus root and scoped AGENTS.md files. Use after /technical-spec to create the phased task breakdown.

Resume execution from wherever you left off. Detects current state and runs the appropriate next command, or reports what's blocking progress. Use at the start of any session to pick up where you left off.

Extract decisions, action items, new context, and insights from the current session into LEARNINGS.md and auto-memory. Run before ending any substantive conversation.

Capture a lightweight work item into BUGS.md, NEXT_STEPS.md, DEFERRED.md, or recommend the feature workflow. Use when a bug, fix, small follow-up, next step, deferred idea, or possible feature comes up during development.

Use when seeking a second opinion from OpenAI Codex on documents, specs, plans, or other non-code-diff content.

Use when delegating code implementation to Codex CLI with Claude-managed decomposition, context scoping, and verification.

Use when requesting an OpenAI Codex review of the current branch with documentation research and cross-AI verification.

Detect split data source anti-patterns and scattered business rule duplication where the same logic is reimplemented across multiple files and languages. Catches semantic duplication that syntactic tools like jscpd miss. Use at phase checkpoints or when investigating data consistency issues.

Use when determining how an AI coding agent can autonomously run and verify a specific user flow end to end before implementation.

Identify high-impact innovative additions to the current app or plan. Use when planning next features, during strategic reviews, or when the user asks for creative ideas.

Add a properly formatted TODO item to TODOS.md. Use when you need to capture a new task, bug, or feature request during development.

Generate feature plan with codebase-aware context. Use when starting a new feature in an existing codebase to skip full spec workflow.

Have OpenAI Codex review the current branch with documentation research. Use for second-opinion code reviews or when you want cross-AI verification.

Configure verification commands for this project. Use when setting up a new project or when verification-config.json is missing or incomplete.

Analyze and prioritize TODO items from TODOS.md. Use when planning work or deciding what to implement next.

Implement [ready]-tagged TODO items with commits. Use after /list-todos has clarified requirements and marked items as ready.

Update documentation after commits. Syncs README, AGENTS.md, CHANGELOG, and docs/ with code changes. Use after commits or to analyze working tree changes.