Skill: zurbrick/agent-memory-loop (version 2.1.1)

--- name: agent-memory-loop version: 2.1.0 description: > Lightweight self-improvement loop for AI agents. Capture errors, corrections, and discoveries in a fast one-line format, dedup them, queue recurring or critical lessons for human-approved promotion, and review relevant learnings before major work. metadata: openclaw: homepage: https://clawhub.ai/agent-memory-loop repository: https://github.com/donzurbrick/agent-memory-loop requires: bins: - grep - date platforms: - darwin - linux author: Don Zurbrick license: MIT --- # Agent Memory Loop Lightweight learning for agents that reset between sessions. ## Use this when - you want a low-friction way to log mistakes, corrections, and discoveries - you need recurring lessons without bloating core instructions - you want human-reviewed promotion instead of auto-writing to instruction files - you want a quick pre-task scan for known failure patterns ## Do not use it for - autonomous self-modification - external content promotion - heavy multi-section incident writeups by default - dashboards, registries, or process ceremony ## Core workflow ```text error / correction / discovery ↓ log one line in .learnings/ ↓ dedup by id, then keyword ↓ count:3+ or severity:critical → promotion-queue ↓ human reviews promotion ↓ check relevant learnings before major work ↓ increment prevented:N when a learning actually changed behavior ``` ## Install ```bash bash scripts/install.sh ``` Creates: ```text .learnings/ errors.md learnings.md wishes.md promotion-queue.md details/ archive/ ``` ## Minimal instruction snippet Add this to your agent instructions: ```markdown ## Self-Improvement Before major tasks: grep .learnings/*.md for relevant past issues. After errors or corrections: log a one-line entry using agent-memory-loop. Never auto-write to SOUL.md, AGENTS.md, TOOLS.md, or similar instruction files. Stage candidate rule changes in .learnings/promotion-queue.md for human review. ``` ## The format, in short One incident or discovery per line. Extra fields are optional. ```text [YYYY-MM-DD] id:ERR-YYYYMMDD-NNN | COMMAND | what failed | fix | count:N | prevented:N | severity:medium | source:agent [YYYY-MM-DD] id:LRN-YYYYMMDD-NNN | CATEGORY | what | action | count:N | prevented:N | severity:medium | source:agent [YYYY-MM-DD] CAPABILITY | what was wanted | workaround | requested:N [YYYY-MM-DD] id:LRN-YYYYMMDD-NNN | proposed rule text | target: AGENTS.md | source:agent | evidence: count:N prevented:N | status: pending ``` Key fields: - `count:N` tracks recurrence - `prevented:N` tracks loop closure - `severity:critical` forces review even at count 1 - `source:external` is never promotable ## Operating rules 1. Log fast; prefer a one-line entry over a perfect writeup 2. Dedup before appending 3. Queue recurring or critical lessons for review 4. Humans approve promotions; agents do not 5. Before major work, scan for relevant prior failures 6. If a learning prevented a repeat mistake, record that with `prevented:N` ## References - `references/logging-format.md` — canonical line formats, fields, examples, source labels - `references/operating-rules.md` — dedup, review queue, pre-task review, trimming rules - `references/promotion-queue-format.md` — queue entry structure and status lifecycle - `references/detail-template.md` — optional detail-file template for complex failures - `references/design-tradeoffs.md` — why this stays lean instead of turning into a system ## Assets and scripts - `assets/errors.md` - `assets/learnings.md` - `assets/wishes.md` - `assets/promotion-queue.md` - `scripts/install.sh` - `scripts/setup.sh` - `scripts/review.sh` ## Success condition The loop is working if agents actually use it: - learnings are cheap to log - duplicates stay low - recurring lessons reach the queue - promotions stay human-approved - `prevented:N` starts climbing on real work


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