infra_advisor — cut token cost beyond the code

Most audits only look at code. This looks at the machine and the local cluster and recommends concrete hardware/software/infra changes that move work off paid cloud models — then bundles a fast one-pass audit of the project.

Commands

# Cluster tips + ASCII diagram
python -m skills.infra_advisor.cli tips
python -m skills.infra_advisor.cli tips --subnet --json

# One-pass audit on the project where Botte Secrète is installed
python -m skills.infra_advisor.cli auto .
python -m skills.infra_advisor.cli auto /path/to/project --json

Infra tips (rules, prioritized P0→P3)

• Install a local LLM (LM Studio/Ollama) if none — the foundation for any saving.
• Add/upgrade a GPU (≥12-16 GB VRAM) → run a 7-14B coder locally.
• Add a Hailo-8 / 8L / 10 NPU → vision (detection/OCR/PDF) at ~0 tokens via media_loader.
• Move the always-on inference node to Linux/WSL2 → no forced Windows reboots/updates killing the endpoint; more free VRAM/RAM.
• Run Qdrant locally → unlock the semantic response cache (-60% repeats).
• Dedicate the strongest host as a shared inference node for all projects.
• Wire the MCP server (bootstrap) so the agent actually uses the local tools.

Each tip carries why and an impact (expected token/cost effect). An ASCII cluster diagram is rendered by default (--json for machine-readable).

Auto audit

auto combines the cheap, reliable passes into one report: directives health (directives_audit) · infra tips + diagram · duplicate function bodies across files (stdlib AST, free) · skill catalog size · plus pointers to deeper passes (full mousquetaires/fallow pipeline, skill_project_optimizer, understand-anything, botte terminal compression).

Exposed via [[llm_mcp]] as infra_tips and auto_audit. Related: [[bootstrap]], [[llm_backends]], [[directives_audit]], [[skill_finder]].