Phase 3 — Grade → Iterate (the bounded loop)

SkillDocs & knowledge

Your AI can grade its own work against a rubric you define and keep refining it until it passes. Each attempt is scored by a separate grader, and the agent reads the verdict to decide whether to sharpen the work, try again, or promote it. Retries are capped, and once a version passes, held-back test cases run as a final check.

Instructions available. Your AI can read the instructions. Execution depends on the setup they require.

After adding it, write a markdown rubric that describes what a passing result looks like, then give your agent a task to complete against it.

Then ask your AI: use the Phase 3 skill

What your AI can do with it

  • Grade its own work against a markdown rubric you define
  • Read each grading verdict before choosing the next move
  • Sharpen the work and re-run it when it falls short
  • Promote a passing version to run on a schedule
  • Run held-back test cases in parallel once a version passes
  • Stop after a hard cap on retries so it never loops forever

What this skill tells your AI

The instructions your AI receives, as published by alirezarezvani/claude-skills in .gemini/skills/grade-iterate/SKILL.md and read by ahel’s review.

This is the plugin's loop: CMA's outcome primitive self-grades the agent's work in an isolated context and feeds failing verdicts back for the next attempt. It is always bounded by max_iterations (1..20) — never "improve forever".

See ../../references/loops-and-workflows.md and the outcome section of ../../references/cma-primitives.md.

Workflow

  1. Define the outcome.
    python3 scripts/outcome_builder.py \
      --sheet ./my-agent/build-sheet.json --max-iterations 5 \
      --out ./my-agent/payloads/outcome.json
    
    The rubric is required; max_iterations is clamped to 1..20. Send the payload as a user.define_outcome event (append to the running session).
  2. Read every verdict first.
    python3 scripts/verdict_reader.py --result ./my-agent/last-verdict.json
    
    Tables the rubric outcome and recommends: SHIP (satisfied), SHARPEN then re-run (needs_revision), ESCALATE (max_iterations_reached / failed), RESUME (interrupted). With ≤1 iteration left it flips to "make the single highest-value fix or escalate now".
  3. Loop invariant. Each iteration must move ≥1 rubric line fail→pass, or the run halts at the cap and escalates. Don't burn the budget on cosmetic edits.
  4. Once a version passes, run held-back eval.
    python3 scripts/eval_scaffold.py \
      --sheet ./my-agent/build-sheet.json --out ./my-agent/eval.json --concurrency 5
    
    Held-back cases (never seen during iteration) run in parallel, capped at the 25-thread CMA ceiling, each graded against the same rubric.
  5. Decide. SHIP as v0, or promote to a scheduled deployment (Phase 4). Record the verdict on the goal: goal_state.py set --phase run-without-you.

Hard rules

  • Bounded, always. No outcome without a max_iterations cap.
  • Read the verdict before acting. The grader's explanation drives the next move.
  • Held-back cases are held back. Never grade generalization on cases the agent already iterated against.

Forcing-question library (recommend + cite)

  1. "What are the 3–5 rubric lines?" Recommend: grounded, checkable criteria. Cite: cma-primitives.md (rubric required).
  2. "How many iterations before you'd rather look yourself?" Recommend: 3–5. Cite: loops-and-workflows.md (bounded loop).
  3. "On a fail, sharpen the prompt or the tools?" Recommend: whichever rubric line failed points to. Cite: verdict_reader next-move table.
  4. "Which cases did the agent NOT see?" Recommend: hold back ≥3 for generalization. Cite: this SKILL (held-back eval).

Tools

  • scripts/outcome_builder.py — user.define_outcome payload (rubric required, cap 1..20).
  • scripts/verdict_reader.py — grader result → next move.
  • scripts/eval_scaffold.py — held-back cases + parallel run plan (≤25 threads).

Signals

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Item type
skill
Key
grade-iterate
Source
github.com/alirezarezvani/claude-skills