Deploy Chiral Narrative Synthesis with a Tinker-based training and evaluation pipeline.
npx skills add https://github.com/North-Shore-AI/tinkerer --skill cns-tinker
Please help me install this Agent Skill. Skill: cns-tinker Source: https://github.com/North-Shore-AI/tinkerer/tree/main/.claude/skills/cns-tinker Command: npx skills add https://github.com/North-Shore-AI/tinkerer --skill cns-tinker
Apply CNS 3.0 within a Tinker-driven training and evaluation pipeline to detect contradictions across multi-source narratives and generate coherent, unified narratives.
Run the CNS-tinker workflow to train the contradiction detector, execute multi-agent debates, and synthesize the unified narrative.
High-intent search queries and answers about installing and using this skill.
Contradiction detection across multi-source narratives is handled by fine-tuning models using SciFact and FEVER data within this Tinker-driven pipeline to identify conflicting claims and generate a reconciled narrative.
Chiral Narrative Synthesis generates unified narratives from conflicting sources using the Tinker API to orchestrate LoRA fine-tuning, multi-agent debates, and topology-informed synthesis for coherent reconciled outputs.
Multi-agent debate orchestration evaluates evidence through RL-based scoring using Fisher Information, enabling models to weigh conflicting sources and synthesize coherent narratives via the Tinker API.
Yes, LoRA fine-tuning requires a compatible base model and access to Tinker resources to train the contradiction detector using SciFact and FEVER datasets with defined evaluation metrics.
Yes, the end-to-end CNS workflow requires data preparation using SciFact and FEVER datasets to train the contradiction detector and evaluate multi-source narrative synthesis accurately.
Topology-informed synthesis requires a compatible base model, LoRA fine-tuning, and Tinker API access with clearly defined prompts, limiting use without proper environment setup and evaluation metrics.
thinking-machines-labAutomate knowledge distillation from teacher to student models via the Tinker API.
ScientiaCapitalExport fine-tuned models to GGUF and deploy with vLLM or Ollama.
yoloshiiClassify queries and route them to specialized MCP tools for multi-source research.
frankxaiTrack Arcanea universe facts and prevent contradictions across content.
