Wazir

Use before implementation work to turn operator briefings into an approved design with explicit trade-offs.

Run the clarification pipeline — research, clarify scope, brainstorm design, generate task specs and execution plan. Pauses for user approval between phases.

How to use Claude Code CLI programmatically for reviews, automation, and non-interactive operations within Wazir pipelines.

How to use Codex CLI programmatically for reviews, execution, and sandbox operations within Wazir pipelines.

Measure pipeline compliance for a completed run — checks phase execution, artifact production, gate evidence, hook enforcement, and publishes per-step and aggregate compliance scores.

Use when behavior is wrong or verification fails. Follow an observe-hypothesize-test-fix loop instead of guesswork.

Guide the designer role through open-pencil MCP workflow to produce design artifacts from an approved spec.

Use when facing 2+ independent tasks that can be worked on without shared state or sequential dependencies

Use when you have a written implementation plan to execute in a separate session with review checkpoints

Orchestrator entry point — gate prerequisites, set interaction mode, delegate to the execute workflow.

Use when implementation is complete, all tests pass, and you need to decide how to integrate the work - guides completion of development work by presenting structured options for merge, PR, or cleanup

How to use Gemini CLI programmatically for headless reviews, automation, and sandbox operations within Wazir pipelines.

Use when reviewing or editing any text artifact (specs, plans, code comments, commit messages, content, documentation) to detect and remove AI writing patterns. Runs a 4-phase pipeline -- Scan for AI vocabulary and structural patterns, Identify severity and domain, Rewrite problematic sections, Verify meaning preservation. Invoke on existing text that needs corrective humanization. For preventive humanization, the composition engine loads domain-specific rules automatically.

Initialize the Wazir pipeline — interactive-first, asks 3-4 questions to configure model mode, tools, and interaction style.

Use after a run or execution slice completes to produce a clean next-run handoff without auto-applying stale context.

Use when receiving code review feedback, before implementing suggestions, especially if feedback seems unclear or technically questionable - requires technical rigor and verification, not performative agreement or blind implementation

Use when completing tasks, implementing major features, or before merging to verify work meets requirements

Run the review phase — adversarial review of implementation against the approved spec, plan, and verification evidence.

Run a structured audit on your codebase — security, code quality, architecture, performance, dependencies, or custom. Produces a report or actionable plan.

Build a project profile from manifests, docs, tests, and `input/` so clarification and planning start from evidence.

Run a self-audit loop in an isolated git worktree — validates, audits, fixes, verifies, and merges back only on green. Safe self-improvement that cannot break the main working tree.

Deep competitive analysis of Wazir skills against the ecosystem. Research only — never auto-applies changes.

Use when executing implementation plans with independent tasks in the current session

Use for implementation work that changes behavior. Follow RED -> GREEN -> REFACTOR with evidence at each step.

Use when starting feature work that needs isolation from current workspace or before executing implementation plans - creates isolated git worktrees with smart directory selection and safety verification

Use when starting any conversation — establishes how to find and use skills, requiring Skill tool invocation before ANY response including clarifying questions

Use before claiming work is complete. Every completion claim needs fresh command evidence or another deterministic proof path.

One-command pipeline — type /wazir followed by what you want to build. Handles init, clarification, execution, review, and audits automatically.

Use after clarification, research, and design approval to create an execution-grade implementation plan.

Use when creating new skills, editing existing skills, or verifying skills work before deployment