Auto Improving Agent

This package appears to be what it says: a local helper that suggests and scores learnings and adds a bootstrap reminder. A few practical notes before enabling: - The SKILL.md promises automated background triggers (post-task scans, periodic sweeps). The code included only provides a bootstrap reminder and a local retention_scorer.py utility; automatic capture would rely on the agent following the instructions or additional hook implementations. If you expect fully automatic logging, verify how your agent/platform will implement those triggers. - The skill will read and write files under .learnings/ and may modify LEARNINGS.md when the scorer is run (retention_scorer.py). Run the scorer with --dry-run first to see planned changes before letting it write files. - There are no network calls or credential requests in the provided files, but the instructions do tell the agent to consult workspace files and memory_search. If your workspace contains secrets or sensitive files, decide whether you want the agent to index or copy that content into .learnings/ (the skill's write gate discourages logging obvious facts or single-use items, but that's an instruction — not an enforced safeguard). - Review the retention_scorer.py and the hook files yourself (they are small) and run the included tests locally if you want assurance about behavior. If you accept that the agent will be permitted to read workspace files and write to .learnings/, this skill is coherent and low-risk. If you require true autonomous capturing of failures without additional platform support, request or implement the missing event handlers instead of relying on the SKILL.md promises.