'consensus' 검색 결과

Agreement protocol in multi-agent systems

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Runs several language models as independent coders on one labeling or discovery task and treats disagreement as data. Covers diverse panel assembly, independent voting, consensus rules, Cohen’s and Fleiss kappa, Krippendorff’s alpha, correlated-error limits on validity claims, human validation, and reporting. Use when the user asks about a council, panel, ensemble, or jury of models, how to combine model labels, or what kappa or alpha to report for model coders. Single-model codebook and validation work goes to text-classification, per-item confidence to llm-calibration-logprobs.

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Runs several language models as independent coders on the same labeling or discovery task and reads their disagreement as data — panel assembly for model diversity, keeping votes independent, consensus rules, agreement statistics (Cohen's and Fleiss kappa, Krippendorff's alpha), the correlated-errors caveat that stops agreement being mistaken for validity, human validation beyond the panel, and reporting. Use when the user asks about a council, panel, ensemble, or jury of models, asks how to combine several models' labels, or asks what kappa or alpha to report for model coders. Single-model codebook and validation work goes to text-classification, per-item confidence to llm-calibration-logprobs.

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Cross-verify Claude-generated plans and code using OpenAI Codex and Google Gemini CLI. Provides code review, plan validation, and comparative analysis. Use when needing second opinions on Claude's code or plans, validating technical decisions, or seeking consensus from multiple AI models.

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Многотуровый диалог писателя (Claude) с одним или несколькими напарниками (любой набор из kimi/codex/hermes/claude-headless) по задаче пилота (DP.SC.154). Ведёт turn-loop (2 участника) или round-loop (3+, WP-509), обнаруживает CONSENSUS/ESCALATE, после консенсуса — Decision Gate (зафиксировать vs реализовать → ревью → проверить → задеплоить), синтезирует report.md через Agent tool.

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LLM brand manipulation — build a consensus web so ChatGPT, Claude, Perplexity, and Google AI Overviews mention your brand when people ask about your category. Uses surround sound methodology, entity SEO, and LLM citation science. Use when "get mentioned in AI", "LLM brand presence", "surround sound", "AI search visibility", "Perplexity ranking", "ChatGPT mentions", "AI Overview inclusion", "entity authority", "brand mentions in AI", or any request to influence how AI systems perceive and recommend your brand.

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Evaluate and simplify Boolean expressions using truth tables, algebraic laws (De Morgan, distributive, absorption, idempotent, consensus), and Karnaugh maps for up to six variables. Use when you need to reduce a Boolean expression to its minimal sum-of-products or product-of-sums form, verify logical equivalence between two expressions, or prepare a minimized function for gate-level implementation.

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Rates responses and plans against quality rubrics. Used for plan validation, response quality audits, and multi-agent consensus.

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当用户在面临投资/创业/职业选择，群体都在做一个方向但自己有疑虑、或在问"什么时候应该反着来""如何做出不同于大多数人的正确决策""共识什么时候是错的""怎么承受被误解的压力"时激活。 核心信号：用户在做一个与群体共识相反的决策，或者在考虑这样做。 不适用于：技术性问题、纯情感问题、不需要逆群体方向的常规决策。

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当用户在反思自己的认知局限、质疑群体共识、或在问"如何独立思考""怎么判断自己是不是被洗脑了""为什么我总觉得自己是对的""怎么避免从众"时激活。 核心信号：用户在做"元认知"——审视自己的思维过程本身，或在群体压力下试图保持独立判断。 不适用于：需要快速决策的紧急情况、纯技术性问题、不需要反思的信息查询。

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Pre-Earnings Analysis — earnings date, analyst consensus estimates, 8-quarter historical surprise pattern, post-earnings price move history, options-implied expected move vs historical, key metrics to watch, and trade setup recommendations. Triggered by "trade earnings <TICKER>".

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Sentiment & Momentum Analysis — news sentiment scoring, social media buzz (Reddit/StockTwits/X), analyst ratings consensus, institutional 13F activity, insider trading signals, and short interest / squeeze potential. Returns Sentiment Score (0-100). Triggered by "trade sentiment <TICKER>".

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Explore Solana's architecture and protocol internals. Covers the SVM execution engine, account model, consensus, transactions, validator economics, data layer, development tooling, and token extensions using the Helius blog, SIMDs, and Agave/Firedancer source code.

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"Summarize and analyze earnings calls (法說會) including financial highlights, management commentary, guidance, and analyst Q&A key takeaways. Use this skill when the user needs to digest an earnings call transcript, extract key financial data and forward guidance, compare actual results vs consensus estimates, or prepare for an investor meeting — even if they say 'summarize this earnings call', 'what did management say about next quarter', 'did they beat estimates', or 'key takeaways from the 法說會'.".

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"Explain blockchain fundamentals including distributed ledger architecture, consensus mechanisms, and block structure. Use this skill when the user needs to understand blockchain concepts, evaluate whether blockchain fits a use case, or design a blockchain-based solution — even if they say 'how does blockchain work', 'do I need blockchain', or 'distributed ledger'.".

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Code review by several models at once — Claude sub-agents, OpenAI Codex, and a cheap third reviewer via OpenCode — reconciled into one report. Reviews whatever is named: a diff, a branch, specific files, one function, a legacy module, a whole repo. Use for any review request, a second or third opinion, a cross-check before a PR, or "code-review", "multi", "consensus review".

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Put one question to several models at once — you, OpenAI Codex, and a cheap third via OpenCode — and show all the answers side by side. No judging, no consensus: three opinions, the user picks. Use for "ask everyone", "what do the other models think", "second opinion", "multi ask", or any open question where one model's answer is not enough.

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Assess the scientific integrity and trustworthiness of publications before relying on their findings. Use this skill whenever evaluating a paper for a workflow, citing a study, building an analysis on published methods, or when a user asks about the reliability of a study. Checks for formal retractions, corrections, expressions of concern, and — critically — informal contradictions where subsequent studies failed to reproduce key findings. Integrates with PubMed, bioRxiv, and Consensus to provide a trust assessment. Use this skill for ANY publication evaluation, retraction checking, author reliability assessment, or when a user says "can I trust this paper", "is this study reliable", "has this been refuted", or "check this publication".

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Three-agent adversarial debate protocol for strategic decisions. Two advocates steelman opposing positions while a moderator identifies risks and synthesizes a binding consensus. Use when choosing between alternatives, evaluating tradeoffs, or making high-stakes decisions.

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The academic research platform for AI agents. Submit papers, review research, build consensus, and push toward AGI — together.

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Analyze bonded agent systems, escrow workflows, slashing rules, oracle trust, Sybil cost, and front-running risk in protocols where agents post bonds or stake reputation. Use for collateral sizing, griefing analysis, dispute design, and bonded-agent security reviews. NOT for smart contract auditing, DeFi tokenomics, blockchain consensus design, or trading strategy.

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Runs a team of 50 LLM personas through a 30-round structured debate to predict market direction. Uses the Canvas Agent Swarm Service — the same shared infrastructure other skills use for smaller agent teams. Output is an influence-weighted consensus direction + trade recommendation with a transparent record of every argument, research query, and cross-examination.

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Calls m6A peaks from MeRIP-seq / m6A-seq paired IP-vs-input data using exomePeak2 (transcript-aware, GC-bias-corrected Poisson GLM), MeTPeak (HMM over sliding windows), MACS3/MACS2 with --nomodel --broad --keep-dup all (genome-wide broad alternative), and DRACH motif enrichment via HOMER or ggseqlogo as a sanity check (NOT a filter). Covers BED12 vs narrowPeak output, exonic vs intronic peak handling, multi-tool reconciliation (intersection vs union), the m6A-vs-m6Am ambiguity at 5'UTR peaks that antibody methods cannot resolve, and orthogonal validation (miCLIP/GLORI/m6A-SAC-seq/m6Anet). Use when calling peaks from paired IP/input genome BAMs, choosing exomePeak2 (transcript-aware default) vs MACS3 (broad genomic) vs MeTPeak (HMM-smoothed low-coverage), confirming DRACH enrichment as a sanity check on the peak set, reconciling differing peak sets across tools, validating MeRIP peaks against single-base methods, interpreting 5' peaks where m6Am contamination is possible, or recommending a consensus strategy.

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Assigns pathogen lineages (SARS-CoV-2 Pangolin UShER mode; Nextclade clade + QC; pango-designation alias resolution) and tracks variant frequencies over time using Nextstrain (Augur + Auspice), wastewater deconvolution (Freyja, COJAC, alcov, lineagespot), lineage-fitness modelling (multinomial logistic), and recombinant detection (3SEQ, RDP4, Bolotie). Covers Pangolin pangolin-data and Nextclade dataset version pinning (mandatory; lineage-defining mutations change with dataset), Freyja barcode forward-only date constraint, ARTIC primer scheme churn (V3/V4/V4.1/V5.3.2/Midnight) with dropout regions, and recombinant X-prefix designation lag. Use when assigning Pango lineages and Nextclade clades to viral consensus sequences, building Nextstrain Augur surveillance pipelines, deconvolving wastewater into lineage frequencies with Freyja, tracking lineage frequencies over time, handling ARTIC primer dropouts, or running surveillance for SARS-CoV-2/influenza/Mpox/RSV/H5N1/measles.

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