Name: Active Learner
Author: autogame-17

What breaks without openclaw active learning agent

Single-user feedback skewing agent direction. No convergence criteria bounding improvement. Feedback channels misconfigured.

→

Continuous quality improvement from user feedback loops × 155-star advanced skill ÷ 45–60 minutes ÷ feedback storage = agents that measurably improve.

openclaw active learning agent — what it actually does

Solicits user feedback after tasks and stores improvement signals

Updates agent behaviour with /learner update after 20+ feedbacks

Configures feedback channels in active-learner.config.json

Enables post-task feedback prompts for every agent interaction

Uses diverse feedback sources to prevent single-user bias

Security check — openclaw active learning agent

Privacy score: 7/10 — accesses connected platform APIs only. Lock it: review OAuth scopes before install, confirm Linux, macOS; OpenClaw ≥1.2 compatibility.

Quick start — openclaw active learning agent in 45–60 minutes

Setup time: 45–60 minutes

You need:

OpenClaw core
LLM API key
feedback storage configured

Install the package:

clawhub install autogame-17/active-learner

Install skill

Configure feedback channels in active-learner.config.json

Enable post-task feedback prompts

After 20+ feedbacks, run /learner update to improve

Troubleshooting openclaw active learning agent

1. Biased feedback from a single user skews learning — use diverse feedback sources

2. Without convergence criteria, improvement is unbounded

Compatibility & status

Works with: Linux, macOS; OpenClaw ≥1.2 advanced Last updated: Oct 2025 ★ 155 on GitHub MIT

Official docs →

View on GitHub →

Related — more like openclaw active learning agent

More by autogame-17

Every agent that doesn't ask for feedback repeats the same quality ceiling. Install before the next high-stakes workflow needs measurable improvement.

Get it on GitHub →