claude-skills/docs/skills/agent-launcher/grade-iterate.md

title	description
Phase 3 — Grade → Iterate (the bounded loop) — Agent Skill for Claude Managed Agents	Phase 3 of building a Claude Managed Agent — the bounded grade→iterate loop. Define a CMA outcome (a required markdown rubric graded by an isolated. Agent skill for Claude Code, Codex CLI, Gemini CLI, OpenClaw.

Phase 3 — Grade → Iterate (the bounded loop)

:material-rocket-launch-outline: Agent Launcher :material-identifier: `grade-iterate` :material-github: Source

Install: claude /plugin install agent-launcher-skills

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](https://github.com/alirezarezvani/claude-skills/tree/main/agent-launcher/references/loops-and-workflows.md) and the outcome section of [references/cma-primitives.md](https://github.com/alirezarezvani/claude-skills/tree/main/agent-launcher/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).