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Agent feedback loop

Skill kjuhwa/skills-hub/skills/workflow/agent-feedback-loop

Run an agent repeatedly in a while loop with user or evaluator feedback until a quality threshold is met.From its SKILL.md

Install
npx -y skills add kjuhwa/skills-hub --skill agent-feedback-loop

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

  • 0 stars0 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

SKILL.md

2.7 KB, 465 tokens by cl100k_base, as published. Nobody here has run it

agent-feedback-loop

Implement an evaluation loop where a generator agent produces output and an evaluator agent scores it. The loop continues until the score passes or a max iteration limit is reached.

When to apply

Any quality-gated generation task: creative writing, code review, report drafting. Automates what would otherwise be manual review-and-revise cycles.

Core snippet

import asyncio
from dataclasses import dataclass
from typing import Literal
from agents import Agent, ItemHelpers, Runner, TResponseInputItem, trace

generator = Agent(
    name="generator",
    instructions="Generate output. Improve based on feedback if provided.",
)

@dataclass
class EvalResult:
    feedback: str
    score: Literal["pass", "needs_improvement", "fail"]

evaluator = Agent[None](
    name="evaluator",
    instructions="Evaluate the output. Provide feedback. Give 'pass' only when quality is good.",
    output_type=EvalResult,
)

async def run_with_feedback(user_prompt: str, max_rounds: int = 5) -> str:
    input_items: list[TResponseInputItem] = [{"content": user_prompt, "role": "user"}]
    latest: str | None = None

    with trace("Feedback loop"):
        for round_num in range(max_rounds):
            gen_result = await Runner.run(generator, input_items)
            latest = ItemHelpers.text_message_outputs(gen_result.new_items)

            eval_result = await Runner.run(evaluator, latest)
            evaluation = eval_result.final_output_as(EvalResult)

            if evaluation.score == "pass":
                break

            # Feed feedback back into next generation
            input_items = gen_result.to_input_list() + [
                {"content": f"Feedback: {evaluation.feedback}", "role": "user"}
            ]

    return latest or ""

asyncio.run(run_with_feedback("Write a haiku about AI"))

Key notes

  • Cap max_rounds to avoid infinite loops and unbounded costs
  • result.to_input_list() carries full conversation history for the next generator call
  • Use structured output from evaluator for type-safe pass/fail branching
  • Wrap in trace() to see all rounds in a single trace

What ships with it

Read from the repository

Just SKILL.md. No reference files, no scripts.

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