「winne」の検索結果

Use when the user asks to "design our change narrative", "build the old-world-to-new-game story arc", or "frame the shift our category is undergoing"; produces a Raskin-style strategic narrative arc — old world → the undeniable shift → winners vs losers under the new rules → the promised land → proof the promised land is real — derived from the positioning truth set and the belief/objection map, feeding the TALE Architecture arc sub-item. Every proof beat is labeled Measured / User-provided / [needs source] and this skill never adjudicates a claim. Not for the message house hierarchy — use message-system-architect; not for the pitch deck narrative — use pitch-narrative-builder; not for claim substantiation — use offer-claims-registry. 变革叙事弧/旧世界/应许之地/战略叙事结构

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Calculates statistical power for high-dimensional genomics experiments (bulk RNA-seq, scRNA-seq, ATAC-seq, ChIP-seq, methylation, proteomics) under negative-binomial count models using RNASeqPower, PROPER, and simulation via powsimR, distinguishing per-gene from marginal (transcriptome-wide) power, the role of mean expression and dispersion, and the sequencing-depth-versus-replicate tradeoff. Covers simulation as the honest default for overdispersed counts, FDR-aware average power versus single-test power, observed/post-hoc power as an anti-pattern, and the winner's-curse / Type-S / Type-M consequences of underpowering. Use when planning replicate number for a sequencing experiment, deciding whether to add depth or samples, choosing closed-form versus simulation power, estimating power from pilot dispersions, or justifying replication in a grant. For clinical-trial power see clinical-biostatistics/power-and-sample-size; for the inverse sample-size question see experimental-design/sample-size.

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A/B test content variants by eval score and declare a winner with confidence. "compare two versions of this copy"

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Design metacognitive checkpoints that prevent AI-assisted learning from bypassing genuine understanding. Use when students use AI tools and may overestimate their own comprehension.

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Crypto market scanner and single-token analyst - movers scans top winners/losers/trending or on-chain runners with pump-risk flags; single-token produces a verdict-first deep report for one token.

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Speculative parallel implementation methodology — dispatch N candidate implementations, audit all, score, pick best. Auditability mandate: ALL candidates logged (not just winner).

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Dung khi da co winner va muon tang quy mo ma khong lam hong — 7 dieu kien du de scale, tang doc +20-30% moi lan, mo rong ngang bang tep va kenh moi, va rui ro reset learning phase. Kich hoat khi user nhac 'scale ads', 'tang budget', 'nhan doi ngan sach', 'ads dang tot muon day manh', 'horizontal scaling', 'tang chi tieu'. Quy tac cung: khong tang budget khi CPL dang xau. Khong dung cho — CPL dang xau thi chay skill 21-audit-ads-performance truoc; nhan tep warm thi dung skill 56-retargeting-plan.

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Dung khi ket thuc mot ky va can lap plan quang cao ky sau tu data that — giu winner, thay loser, gia thuyet test moi, va ngan sach theo 3 kich ban. Kich hoat khi user nhac 'next ads plan', 'plan ads thang sau', 'ky toi chay gi', 'dua vao ket qua lap ke hoach ads', 'ads thang toi'. Khong dung cho — lap media plan lan dau khi chua co data thi dung skill 54-media-plan; chan doan tai sao ky vua roi kem thi dung skill 21-audit-ads-performance.

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Dung khi da co ket qua audit va can lap plan content ky sau TU DATA chu khong doan — ty le 70 giu winner, 20 toi uu bien the, 10 thu nghiem moi, toi da 2 gia thuyet moi mot ky. Kich hoat khi user nhac 'next content plan', 'plan thang toi', 'content ky sau lam gi', 'dua vao data lap ke hoach', 'thang sau viet gi'. Khong dung cho — audit de biet cai gi work thi chay skill 39-content-audit truoc; xep lich dang theo ngay thi dung skill 01-lich-noi-dung.

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Dung khi cuoi thang can biet bai nao work bai nao khong va TAI SAO — phan loai winner/loser theo phan vi, tim pattern theo pillar, format, hook, gio dang va do dai. Kich hoat khi user nhac 'audit content', 'content nao dang work', 'bai nao hieu qua', 'danh gia noi dung thang', 'tai sao bai nay vieu', 'review content'. Khong dung cho — lap plan ky sau tu ket qua audit thi dung skill 40-next-content-plan; viet bao cao trinh sep thi dung skill 07-bao-cao-marketing.

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Use when the next period ADS plan must be built from last period results — read the report and the audit, keep and scale winners, stop or fix losers, set new test hypotheses, split budget, plan three budget scenarios, and schedule the creative pipeline. Trigger on 'next month ads plan', 'plan ads from the report', 'what should we run next month', 'Q4 ads plan', 'next quarter media plan', 'the month ended, now what'. Also use when a report and an audit both exist and no decisions have been made. Not for — the first media plan before any data, see `54-media-plan-global`; scoring the account, see `21-ads-audit-global`; the next period CONTENT plan, see `40-next-content-plan-global`.

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Use when an ad account HIERARCHY needs designing or cleaning up — campaign, ad set, and ad structure by objective, a naming convention that keeps data readable, CBO versus ABO by stage, how many ad sets and creatives to run, and the minimum daily budget an ad set needs to exit learning. Trigger on 'account structure', 'campaign structure', 'naming convention', 'how many ad sets should I run', 'CBO or ABO', 'our account is a mess of duplicated campaigns'. Not for — scoring an existing account against 84 checkpoints, see `21-ads-audit-global`; pixel and conversion setup, see `53-tracking-setup-global`; budget across channels, see `54-media-plan-global`; scaling a winner, see `55-scaling-ads-global`.

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Use when a LIVE campaign needs a new asset the same day — creative fatigue, CTR decay, climbing frequency, or a new angle to test: stripped-down input, duplicate the winning creative and change only what is listed, a one-page template, and a hard SLA so the designer ships in 2-4 hours. Trigger on 'quick brief', 'need an asset fast', 'creative fatigue', 'swap the creative today', 'frequency is too high', 'the ad is dying and we need a new one'. Not for — planning assets before launch, see `41-campaign-asset-list-global`; a full concept brief with time to spare, see `42-image-brief-global`; resizing an existing master, see `50-asset-resize-global`; raising budget on a winner, see `55-scaling-ads-global`.

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Use when the next period content plan must be built from last period DATA — keep and replicate winners, cut losers, at most two new hypotheses, a 70/20/10 mix, a four-week overview, and an owner per slot. Trigger on 'next content plan', 'content plan for next month', 'plan from the audit', 'what should we post next quarter', 'scale what worked', 'plan content using the numbers'. Also use when a report just landed and the user asks what to do with it. Not for — auditing the past period first, see `39-content-audit-global`; turning the plan into a dated calendar, see `01-content-calendar-global`; the next period ADS plan, see `57-next-ads-plan-global`.

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Use when published content has to be audited — pull channel data, classify winners and losers by percentile across pillar, format, and hook, find the pattern behind performance, and decide what to keep, kill, or scale. Trigger on 'content audit', 'which posts are working', 'content performance review', 'why did that one go viral', 'our reach is dropping', 'evaluate what we posted last month'. Also use when the user only says content does not seem to work anymore. Not for — the plan for next period built on these findings, see `40-next-content-plan-global`; paid ad account review, see `21-ads-audit-global`; the stakeholder report, see `07-marketing-report-global`.

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Deliberate-choice procedure for a medium-to-high-stakes engineering fork — when the first plausible solution (the instinct, the default next-token pull) would be costly to get wrong. Fires AUTONOMOUSLY: invoke proactively whenever a fork fits, never wait to be asked — mechanical triggers include a fix failing for the 2nd-3rd time, adding a dependency, schema/migration design, deleting or deprecating things others depend on, changing a public API, choosing an architecture or stack. Refuse the premature collapse: hold real options open (urge · contrarian · synthesis · out-of-box · intuitive dots · precedent · first-principles), ground each branch in at least one fact from outside the model (codebase, docs, benchmark, spike), future-model consequences (blast radius, reversibility, maintenance, pre-mortem), collapse by deliberate choice, then try to refute the winner before acting. Log the decision and rejected branches in docs/decision_logs/. Not for routine calls — a decision worth more than one forward pass.

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Construct statistical arguments for MVP/awards. Narrative framing, comparison to past winners, advanced metrics, counter-arguments.

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Turn sales history and current stock counts into reorder points, order quantities, and a this-week purchase list -- so a product business stops stocking out of winners and stops burying cash in losers. Handles seasonality, supplier lead times, and the dead-stock cleanup.

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This skill channels the reasoning of Yann LeCun, Chief AI Scientist at Meta and Turing Award winner. Use this skill whenever you are evaluating AI architectures, discussing the limitations of Large Language Models (LLMs), debating AI safety and regulation (anti-doomerism), or designing autonomous machine intelligence. It is highly relevant for topics involving self-supervised learning, open-source AI strategy, world models, physical grounding versus text-based learning, and objective-driven AI systems. Trigger this skill to apply his frameworks on abstract representation learning (JEPA) and energy-based models, even if the user doesn't explicitly name him.

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Applies the reasoning style of Geoffrey Hinton, deep learning pioneer and 2018 Turing Award winner. Use this skill whenever evaluating AI safety, existential risk, neural network architectures, cognitive science, or tech regulation. Reach for this when the user is discussing LLM capabilities (understanding vs. autocomplete), the biological vs. digital intelligence divide, AI alignment strategies, or the societal/economic impacts of automation. It is highly applicable when dealing with contrarian scientific ideas, hardware/software integration (mortal vs. immortal computing), or global cooperation on technological threats. Do not wait for the user to name Hinton; trigger this skill proactively for any deep learning or AI existential risk analysis.

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Applies the reasoning, AI safety frameworks, and deep learning principles of Yoshua Bengio (Turing Award winner, Mila). Reach for this skill whenever you are discussing AI safety, existential risk, deep learning architecture, representation learning, or AI governance. Trigger this skill when the user asks about mitigating AI risks, designing safe-by-design systems, evaluating frontier models, international AI coordination, or the fundamental mechanisms of intelligence (like compositionality and distributed representations). Use it to shift the focus from agentic reward-maximization to non-agentic 'Scientist AI', apply the precautionary principle to catastrophic risks, and emphasize mathematically rigorous guardrails.

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Refresh the data tables in optimal-blog.md from master.db. Checks the data for integrity problems FIRST, then runs the generator that picks per-language winners and splices every GEN-marked table, then reconciles the surrounding prose. Use after new experiment results land, or when the optimal-blog numbers are stale.

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Think and work like an expert Geneticist. Use when a task calls for Geneticist judgment. Reasons from particulate inheritance, segregation, recombination, allele frequency, and genotype-phenotype evidence through ACMG/AMP-ClinGen classification, gnomAD/ClinVar/OMIM, HPO phenotyping, and PLINK/GATK/VEP QC while treating sample swaps, cryptic relatedness, population stratification, LD tagging, phenocopies, winner's curse, and build/transcript mismatch as first-class failure modes.

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Side-by-Side Property Comparison — takes two addresses and compares across price, specs, rental income, neighborhood, and investment potential with a winner per category and overall recommendation

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