Gemma4-12B is now the primary model for all questions:
- Endpoint: http://127.0.0.1:8081/v1
- Model ID: gemma4-12b
- Speed: ~40 tokens/sec
- Cost: free (local)
Escalation: Gemma4-12B → DeepSeek V4 Pro → Claude Opus → GPT-5.5 → all 8
Added explicit conditions:
- User has no solution (they don't know the answer)
- All free models tried and failed
- All cheap models (< /usr/bin/bash.50/call) tried and failed
- If a free model can solve it, use the free model
Explicit warning: DO NOT USE unless all other approaches failed.
Costs real money (/usr/bin/bash.10-.00 per invocation).
If the problem is solvable by switching models, just switch models.
If the problem is solvable by reading docs, just read docs.
If the problem is solvable by asking the user, just ask the user.
This skill is the last resort before giving up.
4 dimensions × 2 models each, structured like 16D braid:
- Math: DeepSeek V4 Pro + Gemini 3.1 Pro
- Proof: Claude Opus + Cohere Command A+
- Code: GPT-5.5 + Kimi K2.6
- Diversity: GLM-5.2 + DeepSeek V4 Flash
Each dimension fuses independently, then results fuse together.
Maps to OpenRouter Fusion with 8-model panel.
Cost: -10 for full fusion, /usr/bin/bash.10-2 for single dimension.
OpenCode handles provider routing. The skill is a reference card
for which model to switch to. Nothing more.
DeepSeek V4 Pro: /usr/bin/bash.27/.10 (55x cheaper than Opus)
Claude Opus: 5/5 (baseline)
Hermes already handles model routing. Remove MCP server, keep skill
as a cost-optimized model selection guide.
Key insight: DeepSeek V4 Pro is 55x cheaper than Claude Opus and
nearly as good at math/code. Default to DeepSeek, escalate to Opus
only when stuck.
Removed: break_glass_mcp.py (unnecessary — Hermes handles routing)
