Agent skill · vasilyu1983
agents-skills-feedback-loop
Adds per-skill learnings loops for dated patterns, mistakes, and domain facts. Use when wiring skill memory, consolidation, or drift audits.
What it needs
About 11k tokens when loaded.
What this skill does
Agent Skills — Feedback Loop Use this skill to wire a learnings loop into another skill so it gets better with use, without rewriting SKILL.md automatically. The loop has four moving parts: 1. learnings.md — raw, append-only, committed. Shared working memory across machines; created on first append via appendlearning.py, not seeded empty. 2. learnings.consolidated.md — pruned, dated, committed. Portfolio-grade institutional memory; seeded at wiring time. 3. learnings.local.md — machine-specific notes, gitignored. Use for one-operator-on-one-machine context that should not propagate. 4. scripts/appendlearning.py + scripts/consolidate.py — keep raw entries well-shaped and promote durable ones to consolidated. The name is borrowed from the 2026 "learnings loop" pattern (MindStudio) and Anthropic's own skill-authoring guidance to ask Claude to self-reflect on what went wrong and fold it back into reusable context. The mechanism deliberately does not match MindStudio's: MindStudio's loop has the model rewrite the skill's persistent instructions directly from user corrections — that is the exact auto-rewrite this design forbids (see Anti-Patterns). This skill keeps the same "accumulate corrections across sessions" shape but routes it through append-only raw entries, human-reviewed consolidation, and an eval-gated promotion step before anything touches skill logic — mapped onto this repo's existing 4-type memory schema (see agents-memory). …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills vasilyu1983/agents-skills-feedback-loop