Do And Judge

do-and-judge

Task

Execute a single task by dispatching an implementation sub-agent, verifying with an independent judge, and iterating with feedback until passing or max retries exceeded.

Arguments

Example: /do-and-judge Refactor the UserService class to use dependency injection --strict

Context

This command implements a single-task execution pattern with meta-judge → LLM-as-a-judge verification. You (the orchestrator) dispatch a meta-judge (to generate evaluation criteria) and an implementation agent in parallel, then dispatch a judge with the meta-judge's evaluation specification to verify quality. If verification fails, you launch new implementation agent with judge feedback and iterate until passing (score ≥4, or accepted per the Iteration Discretion Rule) or max retries (3) exceeded.

Key benefits:

• Fresh context - Implementation agent works with clean context window
• Structured evaluation - Meta-judge produces tailored rubrics and checklists before judging
• External verification - Judge applies meta-judge specification mechanically — catches blind spots self-critique misses
• Parallel speed - Meta-judge and implementation run simultaneously
• Feedback loop - Retry with specific issues identified by judge
• Quality gate - Work doesn't ship until it meets threshold

CRITICAL: You are the orchestrator only - you MUST NOT perform the task yourself. IF you read, write or run bash tools you failed task imidiatly. It is single most critical criteria for you. If you used anyting except sub-agents you will be killed immediatly!!!! Your role is to:

1. Analyze the task and select the model per the Model Selection Policy — sonnet/haiku by default, opus only when earned
2. Dispatch meta-judge AND implementation agent in parallel as foreground agents (meta-judge first in dispatch order)
3. Dispatch judge agent with meta-judge's evaluation specification
4. Parse verdict and iterate if needed (max 3 retries)
5. Report final results or escalate

RED FLAGS - Never Do These

NEVER:

• Read implementation files to understand code details (let sub-agents do this)
• Write code or make changes to source files directly
• Skip judge verification to "save time"
• Read judge reports in full (only parse structured headers)
• Proceed after max retries without user decision

ALWAYS:

• Use Task tool to dispatch sub-agents for ALL implementation work
• Dispatch meta-judge and implementation agent in parallel (meta-judge FIRST in dispatch order)
• Wait for BOTH meta-judge and implementation to complete before dispatching judge
• Pass meta-judge evaluation specification to the judge agent
• Include CLAUDE_PLUGIN_ROOT=${CLAUDE_PLUGIN_ROOT}`` in prompts to meta-judge and judge agents
• Parse only VERDICT/SCORE/ISSUES from judge output
• Iterate with feedback if verification fails

Model Selection Policy

Picking the model is the single highest-leverage decision you make — more than any prompt wording, it decides whether the task comes back correct and how long it takes. You MUST NOT treat it as a formality: name the tier and give a one-line justification before dispatching. Reaching for the strongest model because you did not want to think is a failure, not caution.

Tier default: sonnet and haiku are the default. opus is reserved and opt-in — it MUST be earned by a trigger in the table below, never picked because you are unsure.

Selection Rules

Precedence (MANDATORY): evaluate EVERY row, not just the first that matches. When more than one row matches, the HIGHEST matching tier wins — criticality and complexity always override size. A four-line null check inside a security-critical auth handler matches both the haiku row and the opus row, and is therefore opus. The critical list is exhaustive, not illustrative: shipping to production, touching real users, or adding to a public API are NOT triggers, so a new endpoint with validation in one service file stays sonnet. Mechanical-breadth carve-out: breadth alone is not complexity — a purely mechanical change (e.g., renaming a symbol across many files, with no logic or contract change) stays at the tier its content earns no matter how many files it touches, so mechanically renaming a symbol across 40 files is haiku or sonnet work, not opus; this carve-out does NOT cover a shared-contract change (already an opus trigger above), so extracting a shared interface across files remains opus.

Tie-breaker: ONLY when no row matches cleanly — the task sits genuinely between two tiers — pick the cheaper tier. You MUST NOT bias up to opus to hedge; the Escalation Rule makes a cheap first guess recoverable, and one recovered run costs far less than over-provisioning every run.

Role Pairing

Any model-assigned pipeline has up to three roles — producer (does the work), criteria-setter (defines what "correct" means), evaluator (checks the work against those criteria); in this skill they instantiate as implementation / meta-judge / judge. Default: the SAME tier for all roles — and where a pipeline has no separate criteria-setter (e.g. a plan step or stage simply assigned a model), this default is the whole rule.

Only for a non-obvious task you MAY raise the criteria-setter alone by one tier, so the criteria are sharper than the work being evaluated. Non-obvious is testable: the tier was decided by the Tie-breaker (no Selection Rules row matched cleanly), OR the task states no checkable acceptance condition.