'consensus' 검색 결과

Turn a development idea into an actionable plan with implementation steps, testable acceptance criteria, and risk mitigations. Gather requirements through focused questions, generate plans directly from detailed requests, or review proposals from multiple perspectives.

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• Clarifying requirements for a vague development idea
• Planning implementation from detailed requirements
• Comparing design options from multiple perspectives

Analyze earnings estimate changes, results versus analyst expectations, and management forecasts for US and Hong Kong stocks. Organize findings on post-earnings price trends and earnings quality into a structured investment research report.

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• Review analyst estimate changes before earnings
• Compare reported results with market expectations
• Assess management forecasts and earnings quality

Develop an implementation plan through sequential Planner, Architect, and Critic reviews. Compare options, document risks and acceptance checks, and preserve the plan and review records for a later handoff to `$ultragoal`.

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• Compare approaches before implementation
• Plan testing and failure scenarios for high-risk changes
• Review a plan without starting implementation

Review board memos and strategy briefs with available Claude, Codex, and Gemini models. Compare concerns, supporting arguments, and votes, then turn disagreements into questions to resolve before committing to a major business decision.

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• Reviewing fundraising terms before signing
• Checking the reasoning behind a major strategic pivot
• Examining an acquisition memo before a decision

Answer general medical learning questions and clarify common misconceptions using one authoritative guideline or consensus document. Receive a concise conclusion, explanation, and source link, with clear limits excluding individual diagnosis and prescribing.

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• Checking a common medical claim
• Preparing an explanation of a medical misconception
• Learning how a disease relates to a risk factor

Retrieve Chinese medical guidelines, drug information, and regulatory documents when original sources are inaccessible. It checks document identity and version before using alternative copies, while keeping summaries and reposts separate from independent clinical evidence.

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• Finding a guideline when its original page is inaccessible
• Checking whether an alternative PDF matches the original version
• Reviewing fallback rules for Chinese medical sources

Prepare public-company financial data for analysis models by standardizing periods, units, currencies, and account labels. Produce structured tables with source references, validation results, and clear flags for missing data, conflicts, or disclosure changes.

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• Prepare financial statements for an analysis model
• Compare figures across changes in segment disclosures
• Separate management guidance from consensus estimates

Organize ATAC-seq, ChIP-seq, CUT&RUN, or CUT&Tag inputs and route them to suitable analysis workflows. Covers initial quality checks and preparation for finding peaks, genomic regions with concentrated signal, and comparing them across samples.

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• Choose an analysis workflow for an epigenomics assay
• Check FASTQ inputs and sample-sheet readiness
• Organize control and biological replicate information

Plan or run quality checks and peak calling, which identifies enriched genomic regions, for ChIP-seq, CUT&RUN, and CUT&Tag. The workflow accounts for controls and replicates and prepares signal tracks, quality reports, and differential binding designs.

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• Check data quality and enrichment signals
• Choose narrow or broad peaks for the biological target
• Create bigWig tracks and quality dashboards

Verify finished work against acceptance criteria using executable checks and a review of whether each requirement is met. Multiple models can review uncertain cases, and rejected work can enter an automated repair loop when the required Seed is available.

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• Checking finished work against its specification
• Verifying observed behavior against acceptance criteria
• Requesting multiple model reviews for uncertain results

NLSpec authoring — use when you need a structured specification from multi-AI research and consensus

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Detects somatic mutations in circulating tumor DNA using variant callers optimized for low allele fractions with UMI-based error suppression. Reliably detects mutations at VAF above 0.5 percent using consensus-based approaches. Use when identifying tumor mutations from plasma DNA or tracking specific variants.

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Consensus planning entrypoint that auto-gates vague ultragoal requests before execution

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Provider-agnostic multi-LLM deliberation. Three phases — independent responses, cross-model anonymized ranking, chairman synthesis. Credentials come from explicit plugin configuration or pro-workflow-specific CLI variables. Persists transcript to a wiki page when --wiki <slug> is passed. Use when the user wants multiple AI perspectives, consensus-building, or the "LLM Council" approach for high-stakes reviews, plan critique, or contested learning rules.

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Design data systems by understanding storage engines, replication, partitioning, transactions, and consistency models. Use when the user mentions "database choice", "which database should I use", "SQL or NoSQL", "replication lag", "partitioning strategy", "consistency vs availability", "stream processing", "ACID transactions", "eventual consistency", "my queries are slow at scale", or "data is inconsistent across replicas". Also trigger when choosing a datastore, designing data pipelines, or debugging distributed-system consistency issues. Covers data models, batch/stream processing, and distributed consensus. For system design, see system-design. For resilience, see release-it.

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Adversarial dual-model code review that runs two AI models in parallel, cross-references findings, and produces a consensus severity table with fix confidence ratings. Use when asked for a thorough code review, adversarial review, dual-model review, confidence-rated review, or 'Hanselman review'.

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Machine learning toolkit for genomic interval (BED) data; use it when you need to tokenize BED collections and train embeddings for regions/cells/labels, build consensus peak universes, or run similarity search and downstream ML on chromatin accessibility datasets.

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Use when evaluating Michael Burry-style contrarian, asymmetric, unpopular trades: deep mispricing, short theses, complex assets, forced consensus errors, and catalyst-driven downside/upside.

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Use when evaluating startups through a Founders Fund-style lens: contrarian conviction, hard tech, bold product ambition, non-consensus markets, and high-upside company building.

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Multi-agent swarm formation and coordinated execution with topology-aware agent deployment, consensus protocols, and anti-drift enforcement.

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Evaluate solutions through multi-round debate between independent judges until consensus

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Comprehensive multi-perspective review using specialized judges with debate and consensus building

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Pull company financials, SEC filings, and analyst consensus for a public company. Use this skill whenever the user says "10-K", "10-Q", "earnings", "revenue of", "financials for", "analyst rating for", "price target for", or provides a stock ticker. Combines SEC EDGAR for official filings with Yahoo Finance / TipRanks for analyst data. Search + scrape only; no interact needed.

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Treats a ctDNA assay as a molecule-counting experiment at the Poisson edge and builds its analytical-validation case the measurement-science way. Covers the genome-equivalent currency (~330 haploid copies/ng), the lambda = input_GE x VAF sampling ceiling (lambda>=3 for ~95% detection), the error-suppression ladder (raw NGS ~1e-3 -> single-strand UMI ~1e-4/1e-5 -> duplex <1e-7), the CLSI EP17 LoB/LoD/LoD95/LoQ framework, the per-locus-vs-panel-integrated LoD distinction that lets bespoke MRD reach ppm, contrived/SEQC2 reference standards, and honest LoD reporting conditioned on input mass + consensus depth + replicate detection rate. Use when stating or trusting a sensitivity claim, designing a dilution-series validation, deciding how many genome equivalents are needed at a target VAF, choosing a single-locus vs panel-integrated LoD, or auditing a "detects 0.1% VAF" claim.

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