'outcome' 검색 결과

Estimate causal effects of an exposure on an outcome from GWAS summary statistics using genetic instruments. Implements IVW (fixed/random), MR-Egger, weighted median/mode, MR-RAPS, CAUSE, GSMR-HEIDI, MR-PRESSO, MVMR, MR-Clust, LCV, and LHC-MR via TwoSampleMR, MendelianRandomization, MR-PRESSO, cause, and lhcMR. Use when testing causal direction between traits, evaluating drug-target effects via cis-pQTL/cis-eQTL, performing multivariable mediation MR, distinguishing causation from correlated horizontal pleiotropy, or producing STROBE-MR-compliant sensitivity batteries.

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Performs logistic regression for clinical trial outcomes (binary, ordinal, multinomial) with marginal-vs-conditional estimand reporting per FDA 2023 covariate adjustment guidance, g-computation/standardisation for marginal effects, modified Poisson for RR, Brant test for proportional odds, Firth penalty for separation, and Hauck-Donner detection. Use when modeling binary or ordinal endpoints in confirmatory or exploratory clinical trials.

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Decompose total effects into direct and indirect paths through mediators using mediation, CMAverse 4-way, HIMA/HIMA2 high-dimensional, BAMA, two-step / MVMR mediation, or double-ML medDML. Use when testing whether a molecular phenotype (expression, methylation, protein) mediates a treatment-outcome relationship, decomposing exposure-mediator interaction via VanderWeele 4-way, screening high-dimensional EWAS mediators, or running MR-based mediation when sequential ignorability is implausible.

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Estimates bivariate genetic correlation (rg) between traits from GWAS summary statistics or individual-level genotypes using cross-trait LDSC, HDL, LAVA, rho-HESS, GREML-bivariate, Popcorn, and HDL-L. Use when quantifying shared genetic architecture between two traits, screening MR validity before causal inference, distinguishing global from locus-level rg, estimating trans-ancestry rg, separating partial from full causation via LCV gcp, or producing a STROBE-MR-compliant cross-trait sensitivity battery. Cross-trait LDSC intercept absorbs sample overlap and is NOT a bias; HDL is biased under sample overlap above ~5%. High rg between exposure and outcome motivates CHP-aware MR sensitivity (CAUSE, LHC-MR).

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Advisory model escalation based on task outcome tracking

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Local-only regression / benchmark skill for ui-clone-skills maintainers. Drives the standard ui-reverse-engineering pipeline against the canonical reference site (https://realfood.gov) and records AE/SSIM, iteration count, gate fail counts, and outcome to benchmark/history.csv so prompt / sub-doc / model-version drift surfaces as a trend. Trigger phrases: "run benchmark" / "regression benchmark" / "benchmark clone". The Makefile no longer has a `benchmark` target — setup is inline bash in this skill (Step 1 below). Internal: NOT registered in `.claude-plugin/plugin.json` `skills`. Not part of the public 3-skill marketplace surface. Maintainer tooling only.

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Finish an authorized security research task with a durable solved, abstained, or blocked outcome. Use when evidence is sufficient, a stop condition is reached, a tool budget is nearly exhausted, or the agent must commit its best supported answer.

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Draft a Workflow Phase 1 intake document capturing a new request — the problem, the desired outcome, constraints, and testable acceptance criteria. Use when a user brings a new feature, change, or investigation that doesn't yet have a spec. The output lives at `docs/intake/<slug>.md` and feeds `/scout`, `/research`, and `/spec` downstream.

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Maintain vision, user journeys, evolving requirements and roadmap outcomes when intent or acceptance changes.

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Pursue an agreed goal to a working outcome without waiting: decompose it, re-prioritise as evidence arrives, branch, gate, get an independent review (a different model or a fresh reviewer subagent — the writer never approves), merge, prune, and bank the operator's decisions in STATUS.md instead of blocking on them. Trigger on "get it done", "don't wait for me", "proceed autonomously", or when an epic or roadmap objective is handed over. Not a close-out: this work set is expected to grow.

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Turns past decisions into calibrated judgment - scoring old predictions against what happened, assigning credit for delayed results, and separating a bad decision from a good decision that lost. Use during a retrospective, postmortem, quarterly or monthly review, when reviewing decisions taken 30+ days ago, when attributing a result to a cause, or when someone claims to have called something.

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Prices research, tests, surveys, dashboards and experiments before running them, by computing how much the result would raise expected utility - which is zero whenever no outcome would change the decision. Use when someone proposes an A/B test, user survey, market study, competitor analysis, analytics build, consultant, pilot, or "let's gather more data", and when deciding how large a study needs to be.

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[Planning] Use ONLY when the user explicitly requests a product roadmap, roadmap update or milestone selection: outcome milestones, MVP scope, non-goals, risks, evidence gates.

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Produce a flow-level summary of the current Claude Code session — executive summary, chronological timeline, and outcomes — written into the active feature directory next to spec.md / plan.md.

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[Process] Use when the active workflow has finished its steps or the user asks to close it: checks outcome evidence and owned changes, then closes the workflow; recovery state stays until an explicit /clear.

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Builds and improves task-specific decision artifacts from application requirements and labeled records. Use for data assembly, primitive and base-model selection, fitting, export/reload, inference policy, or bounded data/model/program improvement. Includes rules and no-training outcomes; does not require a rung ladder or training a general Jev.

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Use for every progress update, status report, problem explanation, ETA, review, acceptance, or completion report to the operator/owner, especially when they ask “到哪了”, “什么问题”, “做了什么”, “结果呢”, “还要多久”, or “说人话”. Translate technical evidence into visible or operational outcomes before details.

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Product-discovery coach for Fishbowl engineers — inspired by Marty Cagan's work (never impersonates him). Stage-routes between opportunity brief, research, PRD, ticket drafting, and outcome review. Triggers on "product coach", "help me with product", "I need to write a PRD", "I want to do discovery", "opportunity assessment", "who is this for", "why are we building this", or when an engineer is starting, resuming, or post-mortem-ing product-adjacent work. Invoke with a short description of what you want to do, or invoke bare to be surveyed and routed.

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Load this skill when writing, reviewing, or interpreting behavioral accessibility automation — tests that manipulate a rendered page (resizing the viewport, pressing real keys, capturing and comparing screenshots) rather than only inspecting static markup or a single computed accessibility tree. Covers Reflow risk (SC 1.4.10) and Focus Visible risk (SC 2.4.7) checks. Absolutely always report indicator results separately from confirmed failures. Under no circumstances treat a clean behavioral scan as WCAG conformance, or collapse `cantTell`/test-error/indicator outcomes into a single pass/fail signal.

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Apply during planned rewrites and migrations with explicit phase boundaries. Converge on the target architecture; don't preserve smooth intermediate states with throwaway compatibility code.

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当用户询问 ClinicalTrials.gov / 临床试验 / NCT / 试验注册 / 招募状态 / phase 1/2/3 / RCT lookup / sponsor 检索 / interventional trial / observational study / 主次要终点 / trial outcomes / 试验招募中 / recruiting status 时,通过 ClinicalTrials.gov API v2 实时检索官方注册数据。专注实时单次查询(≤1000 条/请求),典型场景: "某 NCT 试验最新状态"、"BTK 抑制剂 Phase 3 在招募试验"、"某试验已发布的主次要终点结果"。明确边界: 批量 >10k 试验 / 全库 SQL 历史分析 → aact-bulk-trials; 关联 PubMed 文献 → pubmed-eutils; RCT 自动 GRADE 评级 → medical-evidence-grading; 引用入报告附录 → evidence-appendix-sync。跨平台无锁(Claude Code / Codex / Cursor 通用)。

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Design phishing and social engineering simulations with explicit authorization and measurable outcomes.

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Set up authorized C2 simulation workflows and measure defensive detection outcomes.

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Use this skill when a new product feature, vendor, AI use, monitoring tool or change in processing raises the question "do we need a DPIA", or someone asks "DPIA screening", "is this high risk processing", "data protection impact assessment threshold", "privacy impact screening for this feature", "does our AI feature need a DPIA" or "document why we did not do a DPIA". Describes the processing with numbers, tests it against the GDPR cases, the supervisory authority's list and the EDPB-endorsed criteria, reaches a reasoned outcome (required, recommended, not required) with a re-screen trigger, and keeps a screening log for accountability.

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