grade-iterate – skill Agent & Tự động hóa cho agent AI

Phase 3 — Grade → Iterate (the bounded loop)

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](../../references/loops-and-workflows.md)

and the outcome section of

[../../references/cma-primitives.md](../../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).