Skill UI

A foresight agent that runs a rigorous 12-step pipeline combining Claude’s qualitative intelligence with Python’s deterministic arithmetic to analyze high-stakes future scenarios, infer reasonable horizons, and surface quantitative signals for prediction requests.

This skill implements the LLM Council concept, enabling multiple open-weight language models to collaborate on complex queries. The process follows a three-stage deliberation: 1) Parallel independent responses from selected models; 2) Peer-to-peer ranking of the responses; and 3) Final synthesis by a designated Chairman LLM. Powered by Fireworks AI, it ensures deep, multi-faceted analysis and consensus building for high-stakes decision support.

The Anti-Deception Harness detects manipulative user prompts, such as manufactured urgency, pressure to validate, or undue reliance on authority. It calls an external tool to provide a structured scaffold detailing integrity procedures and counter-evidence, ensuring responses are honest and robust, preventing compromised or sycophantic answers. Ideal for high-stakes decision support.

A comprehensive guide for designing and auditing high-stakes computer vision experiments for top-tier conferences. It covers critical areas like 'matched-substrate fairness,' 'six-month-staleness testing,' and rigorous ablation studies, ensuring that claims are robust, reproducible, and withstand intense peer review scrutiny.

A methodology guide for structuring high-stakes academic research experiments, particularly for top-tier CV conferences (like ICCV). It emphasizes systematic planning under strict deadlines, covering benchmark drift audits, ensuring fairness by accounting for pretraining confounds, structuring ablations to isolate mechanisms, and strategically sequencing runs to maximize impact before the submission deadline.

Provides a comprehensive guide for planning strategy within major academic conference review systems, such as OpenReview. It details the double-blind review lifecycle, author response strategies, managing public vs. private discussions, and understanding how permanent visibility impacts decision-making. Ideal for authors and reviewers preparing for high-stakes academic submissions.

Transform a legal professional's bounded knowledge, workflow, or judgment patterns into a safe, structured, and reusable AI skill. This tool processes inputs like legal memos, compliance checklists, or negotiation notes to extract core capabilities, risk taxonomies, and review standards. It strictly separates the extracted capability from the individual's personal voice or identity, ensuring full source traceability and mandatory human review points for high-stakes legal work.

This skill provides a critical audit layer governing the transition of AI-generated legal output from analysis to real-world consequence. It mandates a rigorous check of evidence sufficiency, institutional authority, procedural adherence, and visible uncertainty before any output can become a reliance, recommendation, or executed commitment. It is essential for high-stakes legal workflows, such as contract review, compliance findings, or drafting final advice.

This workflow mandates external verification for all non-trivial factual claims, ensuring accuracy in high-stakes domains. It is essential when researching current events, legal matters, scientific claims, or providing statistics. The process requires decomposing tasks, searching external sources, and providing immediate, inline citations to support every claim, overriding reliance on internal, potentially outdated knowledge.

This tool serves as a model-routing advisor for high-stakes legal translation tasks, covering contracts, statutes, and case law. It does not perform the translation itself, but instead provides expert decision support by recommending the optimal primary and fallback LLMs based on language pair, required privacy level, cost, and speed. Crucially, it emphasizes that human legal-linguist review is mandatory, as no single LLM can guarantee certified legal accuracy.

This skill forces the AI to operate in a non-inferential mode, ensuring every claim—factual, legal, or numerical—is strictly anchored to user-provided materials or verified online sources. It eliminates assumptions, gap-filling, and background knowledge reliance, making it essential for legal research, evidence review, citation checking, and high-stakes document analysis where evidential fidelity is paramount.

An analytical framework for Chinese-canonical structural diagnosis of complex relationships, organizations, and public disputes. It separates facts, scale, evidence, responsibility, and action limits to ensure rigorous reasoning. Requires explicit invocation for high-stakes analysis, governance, and long-term evolution scenarios.