agents-skills-feedback-loop

# 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 `append_learning.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/append_learning.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-memor...