菜单
Cache les réponses des agents pour skipper l'exécution quand l'output est prédictible. Use when you want to cut 10-15% by avoiding redundant agent runs.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
Memory as a learnable skill — store, recall, compress, and consolidate agent memories. Inspired by Stanford AutoMem.
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.
| name | agent-cache |
| description | Cache les réponses des agents pour skipper l'exécution quand l'output est prédictible. Use when you want to cut 10-15% by avoiding redundant agent runs. |
Skippe l'exécution d'un agent si son résultat peut être prédit.
| Stratégie | Description | Quand |
|---|---|---|
| Exact hash | Même input → même output | Tâches déterministes |
| Fingerprint | Code inchangé → résultat inchangé | Audit, linting |
| Fuzzy match | Similarité sémantique | Questions similaires |
python -m skills.agent_cache.cli check "query" --agent audit
python -m skills.agent_cache.cli store "query" "response" --agent fix
python -m skills.agent_cache.cli stats