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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.
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
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 | 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)