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Thread Context — Triangulator Skill Build

Where We Left Off

Everything is built and configured. The triangulator hasn't been tested with a live query yet (sandbox blocks outbound HTTP). Brian was about to try his first triangulation.

To Resume This Work

Immediate next step

Run a triangulation! Say "triangulate: [any question]" and the full flow kicks in: 1. AskUserQuestion for query type + depth 2. Auto-recommend models from the registry 3. Confirm or customize 4. Query all 7 providers in parallel 5. Synthesize

Architecture

api-keys.json           ← 7 provider keys (shared across all skills)
model-registry.json     ← 25 tagged models (auto-refreshed weekly)
balance-checker.py      ← Credit monitoring (daily scheduled check)
llm-triangulator-*      ← Skill-specific config, script, instructions

UX Flow

Phase 1: Query type (reasoning/coding/creative/research) + depth (quick/standard/deep) Phase 2: Auto-recommend best model per provider → "use these" or "customize" Phase 3 (optional): Per-provider model override + "Remember for this session" option

Key design principles Brian emphasized

  • System should NOT be static — models and capabilities auto-update
  • Shared infrastructure across skills (key vault, registry)
  • Configurable alert thresholds for credit monitoring
  • Smart defaults with full override capability

Future work discussed

  • Image/video critique skill: Together AI's generative models (FLUX, Kling, Sora 2) + vision-capable LLMs. Same shared key vault. Would add image/video tags to model registry.

Brian's working style (learned this session)

  • Asks "what are best practices?" before committing to an approach
  • Prefers configurable over static — wants things to self-maintain
  • Thinks about shared infrastructure across skills
  • Iterates on UX — started basic, evolved to smart recommendations
  • Thorough — covers edge cases like credit monitoring and model freshness