Agent Feedback Loop Designer
$2.99OfficialDesign self-improving agent systems: outcome tracking, reward signal extraction, prompt refinement cycles, and human preference collection.
What you get
- ✓9-step procedure
- ✓6 pitfalls to avoid
- ✓Installs into 6 tools
- Version
- v1 →
- Last updated
- today
- Length
- 6 min read
- Requires
- Best with a strong model (Claude Sonnet 4)
Works in: Claude Code, Codex, Cline, opencode, OpenClaw, Hermes · Handles multi-file projects
Preview
When to use
Use this skill when the user wants an agent that improves over time from outcomes and feedback: "make my agent learn from its mistakes," "collect user feedback and use it," "auto-refine the prompt based on what works," "set up a self-improvement loop." Trigger phrases: feedback loop, self-improving agent, reward signal, preference collection, prompt refinement cycle, RLHF, continuous improvement.
Do NOT use it for one-time prompt tuning (that's prompt tuning) or fine-tuning (different, heavier technique). Use this when the goal is a closed loop — observe outcomes, extract a signal, feed it back, measure improvement — running continuously or on a cadence. True self-improvem
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