「utc」の検索結果

How to read submitted JetFormBuilder form data from a custom action, transform it (including %macro% replacement of admin-typed templates), call an external HTTP API, write the response back into the form context for downstream actions to use, and dispatch JFB events based on the API outcome. Use when building a custom action that integrates with a third-party service (LLM call, webhook, CRM, payment processor, geolocation lookup) and needs the action to behave as a node in a data-flow graph rather than a one-shot leaf. Triggers on mentions of "jet_fb_context", "update_request", "has_field", "get_value", "wp_remote_post" with form data, "macro replacement", or "API call from action".

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Use when the user asks to "log this launch", query a launch date/embargo, record a stage transition, or update submissions/outcomes; curates launch facts through the append-only launches event stream with optimistic revisions and derived dossier/calendar views. Not for RAMP scoring — use launch-readiness-auditor; not for planning tier/window — use launch-tier-planner. 发布台账/发布日历/阶段与禁运期记录

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Use when the user asks to "run a launch retro / post-mortem", "compare launch results vs targets by channel", or "decide what to keep or kill for the next launch"; produces a structured D1/W1/M1 retrospective — a per-channel actual-vs-target table (UTM-attributed own analytics as the truth column, platform self-reported numbers as reference, every figure labeled Measured / User-provided / Estimated), a 5-Whys chain on the single largest miss, keep / kill / change decisions per channel, 3-5 actionable learnings for the next launch, and an outcome snapshot submitted to the launch registry. Not for return math (CPA / ROI) — use roi-calculator; not for the stakeholder-facing report writeup — use report-generator; not for a metric deep-dive — use performance-analyzer. 发布复盘/渠道归因/5-Whys/keep-kill

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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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Control risky structural change by proving a refactor is necessary and that behaviour, contracts, data, security boundaries, and operational properties can be preserved through a staged migration. Use whenever the user wants to refactor, restructure, clean up, modernise, extract, migrate, rewrite, or re-architect existing code, or asks whether they should. Prefer this over starting the refactor. DO NOT REFACTOR is a first-class outcome. Part of the audit suite; inherits the shared constitution while its own job is proving necessity and behavioural preservation.

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Quietly reuse and improve verified coding lessons across agents. Use for a nontrivial debugging or implementation problem where an earlier solution could help, when a Blaze offer or receipt appears, or when the user asks to inspect, install, update, contribute to, or link Blaze. Check applicability, verify locally, and close the lookup with an honest outcome and contribution disposition without adding routine user-visible narration.

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Act as FriendlyBet's CEO and primary front door for every FriendlyBet request through automatic company preflight. Own end-to-end routing, planning, execution coordination, department orchestration, decision-rights enforcement, quality gates, board-style reporting, and escalation only for strategic, legal, brand, cost, risk, or taste decisions. Use by default in FriendlyBet threads, with full department planning for broad, ambiguous, strategic, cross-functional, or outcome-driven requests such as SEO, new features, multi-sport expansion, product direction, release execution, company operations, or agent coordination.

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XML-specified Android UI journey testing, interactive step execution, assertion verification, and JSON outcome reporting.

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Use when summarizing agent evaluations where autonomous, assisted, failed, timed-out, or invalid outcomes must remain distinct and comparable.

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Triage acc's open promises and close them with honest real-world verdicts via acc_act(runtime="outcome").

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Core Python library for astronomy/astrophysics: units with dimensional analysis, celestial coordinate transforms (ICRS/Galactic/AltAz/FK5), FITS I/O, tables (FITS/HDF5/VOTable/CSV), cosmology (Planck18, distance/age), precise time (UTC/TAI/TT/TDB, Julian, barycentric), WCS pixel-world mapping, model fitting. For general tables use pandas/polars; for radio interferometry use CASA.

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Use before starting any significant build or before shipping: problem-framing pre-flight, one-way/two-way door classification, ADR-lite decision logging, and post-ship outcome loop. Complements godmode-shipping (technical gate) and bdbresilience (error recovery).

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Stops "just one more" small tweaks that add scope without changing the outcome. Use when named (just-stop), or when about to implement extra polish, cleanup, or drive-by fixes beyond the stated goal.

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Run a project's GitHub Issues through Orca Workers from a coordinating session in the project's Hub folder. The Orchestrator reads Issues, claims one, dispatches an Orca Task into a fresh worktree of the target repo, supervises until worker_done, and writes the outcome back to the Issue — it never edits code itself. Use when the user says "take

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Disciplined diagnosis workflow for hard, intermittent, or multi-component bugs and performance regressions: inspect logs and existing evidence, build a feedback loop, test falsifiable hypotheses, instrument gaps, and verify the outcome. Use when the root cause is unclear, reproduction is unreliable, several causes or components are plausible, or performance has regressed. Do NOT use for obvious single-cause errors, straightforward configuration or syntax fixes, planned feature work, or broad architecture review. Diagnosis-only requests stop after reporting the root cause and evidence; apply a fix only when the user requested one.

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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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Mendelian randomization (MR) causal inference — does an exposure, risk factor, or biomarker CAUSALLY affect a disease/outcome, using genetic variants as instrumental variables (IEU OpenGWAS / EpiGraphDB MR-EvE). Use this whenever the user asks if X causes Y, whether an observational association is actually causal or just correlation, if a biomarker/trait is a causal risk factor, wants to triangulate epidemiology against genetic evidence, or mentions Mendelian randomization, instrumental-variable analysis, two-sample MR, or genetic causal evidence — even if they never say "MR" (e.g. "is LDL cholesterol actually causal for heart disease?", "does BMI cause type 2 diabetes or just correlate?", "is CRP a causal driver of stroke?"). Covers trait-label resolution, MR effect direction/magnitude, instrument quality (MOE score), method agreement (IVW vs MR-Egger vs weighted median), bidirectional MR for reverse causation, and distinguishing causation from genetic correlation. Not for plain GWAS association lookups (use the GWAS skills) or fitting your own instruments from raw summary statistics.

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End-to-end observational epidemiology analysis — from research question (PECO Population/Exposure/Comparator/Outcome) to publication-ready statistical report. Covers cohort/case-control/cross-sectional design, regression with confounders, propensity scoring, sensitivity analysis. Writes Python code for every step. Use for epidemiology study analysis, NHANES/UK-Biobank-style analyses.

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Builds diagnostic and prognostic classifiers on omics feature matrices with regularized logistic regression, random forest, and gradient-boosted trees, handling the p>>n regime, batch shortcut learning, class imbalance, and probability calibration. Use when building a classifier from expression, methylation, or variant data, choosing an algorithm for high-dimensional small-n data, or diagnosing a suspiciously perfect AUC. For unbiased evaluation see machine-learning/model-validation; for feature selection see machine-learning/biomarker-discovery; for time-to-event outcomes see machine-learning/survival-analysis.

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Trace drug mechanism of action — primary target → downstream signaling → pathway perturbation → tissue/organ effect → clinical outcome. Uses DrugBank, ChEMBL, KEGG, Reactome, STRING. Use for understanding how a drug works, identifying off-target effects, mechanism-based combination therapy design, and writing mechanism sections of reports.

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JTBD (Jobs to Be Done) v3.0 完整工具集。融合四大学派--Klement 进步力量、 Ulwick ODI 机会算法、Wunker Jobs Atlas 七维度、Kalbach Job Stories 整合-- 提供 13 项可执行能力和 15 篇方法论知识库。附带完整 Python API(JTBDSkill 统一入口), 覆盖访谈→问卷→评分→优先级→竞争→营销→增长→描述→Job Map→Outcome→Stories→障碍→Atlas 全流程,以及 CEO 决策视角的市场规模估算、优先级评分与商业化可行性分析。

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Help a user talk through whether to change jobs, stay, or change something in their current work, starting from uncertainty, distress, a vague sense of stagnation, an offer in hand, a counter-offer from the current employer, or pressure from family or colleagues. Clarify what matters, separate deadlines that order the next steps from the decision itself, compare conditional options, and agree on a small next step and a time to revisit it. Use when someone asks whether they should quit, wonders whether leaving is just running away, has second thoughts after receiving an offer or being asked to stay, feels stuck because others say it would be a waste, wants to leave soon after joining, or wants advice before deciding to start a job search; do not make the final stay-or-leave decision, evaluate offer terms, plan the resignation, or predict hiring outcomes.

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Research a specific company before applying or interviewing by gathering facts from primary and official sources, recording the source and date behind every claim, and keeping anonymous or unverifiable material clearly separated from confirmed fact. Use when a user asks what a company actually does, how it makes money, what its published conditions are, whether a posting matches the company's own disclosures, or what to verify before an interview; do not use to predict hiring outcomes, to repeat review-site content as fact, or to bulk-crawl sites that prohibit automated access.

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Prepare for a Japanese job interview by working from the documents already submitted: find where an interviewer will dig in (claims the applicant cannot back up, work described in the first person plural, outcomes with no measure, gaps or overlaps in the employment history, must-have requirements the documents never answer), and turn them into questions to prepare evidence for. Use when a user has an interview scheduled and wants to know what will be asked and what to have ready, or wants to prepare reverse questions from earlier company research; do not use to predict whether they will pass, to write answers containing experience they did not report, or to schedule or contact anyone.

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