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Memory as a learnable skill — store, recall, compress, and consolidate agent memories. Inspired by Stanford AutoMem.
Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.
Based on SOC occupation classification
Auto-decide whether a task runs on a LOCAL model or a CLOUD model (DeepSeek, GLM, Nemotron, Grok, Gemma, …) from an automatic effort estimate, and run multi-model fusion (cascade, draft→refine, vote). Use when the user wants automatic local-vs-cloud routing, to add cloud LLM providers, to make local and cloud models collaborate, or mentions effort-based routing, model fusion/ensemble, OpenRouter, DeepSeek, GLM, Nemotron, or Grok.
Fenêtres de contexte pour boucles rétroactives — charge seulement les deltas.
Optimisations DAG/RAG — waves, pruning, memoization, routing.
Generate one self-contained, timestamped HTML dashboard of the system's cost picture — routing savings (control loop), metric trends, current metrics, and the cost of outstanding fixes. Also renders as a live ANSI terminal view (--tui, --watch) and serves a live HTTP API (api.py). Use when the user wants a single visual view of cost/savings/health over time, or a live terminal view they don't have to open a browser for.
Append-only JSONL decision log (.botte/events.jsonl) that every filter in the belt writes to — routing, cache hits, escalations, micro-NN outputs. The single source of truth demo mode, the live dashboard, and session replay all read from. Use when you want to see or emit a live feed of routing/cache/escalation decisions, or when building a tool that needs to watch the belt work in real time.
Vérification différentielle — ne vérifie que les sections modifiées.
| name | auto-memory |
| description | Memory as a learnable skill — store, recall, compress, and consolidate agent memories. Inspired by Stanford AutoMem. |
| version | 1.0.0 |
Memory as a capability, not just storage. Adapts to patterns in agent behavior.
from skills.auto_memory import init_memory, store_memory, recall_memory
bank = init_memory()
store_memory("user_pref.format", "concise", category="user_pref", confidence=0.95)
prefs = recall_memory("user_pref.format")
Add to control_loop.py:
from skills.auto_memory.hook import init_memory, record_step, consolidate_memory
bank = init_memory("task_123")
record_step("plan", {"steps": ["a", "b"]})
consolidate_memory() # merge similar memories