菜单
Transparent LLM compression proxy for botte-secrete — sit between any AI agent and its LLM API to compress requests by 40-95%. Use when you want token savings without changing agent code.
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。
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.
基于 SOC 职业分类
| name | botte-proxy |
| description | Transparent LLM compression proxy for botte-secrete — sit between any AI agent and its LLM API to compress requests by 40-95%. Use when you want token savings without changing agent code. |
Transparent HTTP proxy that compresses LLM API requests before forwarding them. Inspired by Headroom's proxy mode.
http://localhost:PORT/dashboardhttp://localhost:PORT/stats# Start proxy (default target: http://localhost:11434/v1 for Ollama/LocalAI)
python -m skills.botte_proxy.cli proxy --port 8787
# With specific target
python -m skills.botte_proxy.cli proxy --target http://192.168.1.47:11434/v1 --port 8787
# With OpenAI
python -m skills.botte_proxy.cli proxy --target https://api.openai.com/v1 --api-key $OPENAI_API_KEY
# View stats
python -m skills.botte_proxy.cli stats
# View dashboard
open http://localhost:8787/dashboard
Point any OpenAI-compatible agent at the proxy:
# Claude Code
CLAUDECODE_API_BASE=http://localhost:8787 claude
# Any OpenAI client
export OPENAI_BASE_URL=http://localhost:8787/v1
# Generic
export OPENAI_API_BASE=http://localhost:8787/v1
Agent → Proxy (port 8787) → Compress messages → Forward → LLM API
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Dashboard (/dashboard)
Stats (/stats)