「cause」の検索結果

Audits git repository hygiene: worktree state, stale branches, uncommitted changes, orphaned worktrees, and hook configuration. Finds the rot before it causes a crisis.

日本語の概要は準備中です。原文の説明を表示しています。

Naturalistic causal reasoning for failures and disputed attribution. Use when postmortems or root-cause debates need reframing. NOT for causal inference, fault trees, or fully observed failures.

日本語の概要は準備中です。原文の説明を表示しています。

Performs time-to-event analysis for clinical trials including Cox proportional hazards regression with PH diagnostics, restricted mean survival time (RMST) under non-PH, competing risks via Fine-Gray vs cause-specific Cox, weighted log-rank and MaxCombo for non-proportional hazards, recurrent events (Andersen-Gill, PWP, WLW), and interval-censored data. Use when analyzing time-to-event endpoints (OS, PFS, DOR, TTR, TTNT) in oncology or other clinical trials.

日本語の概要は準備中です。原文の説明を表示しています。

Detect and adjust for horizontal pleiotropy in two-sample Mendelian randomization by distinguishing uncorrelated (UHP) from correlated (CHP) pleiotropy and choosing among Egger, MR-PRESSO, MR-RAPS, CAUSE, LHC-MR, LCV, MR-Clust, MR-Mix, and contamination-mixture methods. Use when validating an MR causal claim, running the STROBE-MR sensitivity battery, suspecting a shared heritable confounder, working under weak-instrument or polygenic-exposure regimes, or reconciling discordant estimates across robust methods.

日本語の概要は準備中です。原文の説明を表示しています。

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).

日本語の概要は準備中です。原文の説明を表示しています。

Systematically interpret a Medtronic CareLink or MiniMed 780G Daily Review download in a structured 6-step clinical workflow — covering Time in Range, insulin delivery, day/night patterns, hypo and hyperglycemia root causes, and a prioritised action plan. Trigger when a clinician uploads or shares a CareLink report, MiniMed download, 780G Daily Review, pump download, or asks to review a patient's CGM or insulin pump data.

日本語の概要は準備中です。原文の説明を表示しています。

Screen a hypercalcemic patient for medication- and supplement-induced causes — thiazides, lithium, vitamin D, vitamin A/retinoids, antiestrogens, theophylline, milk-alkali, SGLT2 inhibitors, immune checkpoint inhibitors, denosumab rebound, teriparatide/abaloparatide, foscarnet, ketogenic diet, aluminium. Trigger when a clinician asks "is this hypercalcemia drug-induced", "thiazide hypercalcemia", "lithium hypercalcemia", "denosumab rebound hypercalcemia", "vitamin D toxicity", "milk-alkali syndrome", "SGLT2 hypercalcemia", or any new hypercalcemia where medication review is needed.

日本語の概要は準備中です。原文の説明を表示しています。

Diagnose monogenic and syndromic obesity in children and adolescents using a structured step-by-step algorithm. Use this skill whenever a clinician suspects a genetic cause of obesity, asks about leptin deficiency, MC4R mutation, POMC deficiency, PCSK1 deficiency, leptin receptor deficiency, Bardet-Biedl syndrome, Prader-Willi syndrome, Alström syndrome, or any case of early-onset severe obesity with hyperphagia. Also trigger for questions about targeted pharmacotherapy including setmelanotide or metreleptin, or when to order a genomic obesity panel. Cross-references the NHS Genomic Test Finder skill to surface the relevant R-code once a diagnosis is reached.

日本語の概要は準備中です。原文の説明を表示しています。

Screen a child with obesity for red flags suggesting monogenic, syndromic, or secondary endocrine causes — distinguishing atypical from typical exogenous obesity. Trigger when a clinician asks whether obesity could be genetic or hormonal, suspects Prader-Willi syndrome, Cushing disease, hypothyroidism, ROHHAD, Bardet-Biedl syndrome, leptin deficiency, or craniopharyngioma, or when a child has early-onset severe obesity, short stature with obesity, hyperphagia, hypotonia, dysmorphic features, or developmental delay with weight gain.

日本語の概要は準備中です。原文の説明を表示しています。

Draft a Root Cause Analysis for an incident, outage, or repeated test failure. Unlike intake/spec/brd, RCA is not a workflow phase — it's a standalone postmortem artifact that often precedes a bugfix intake. Output lives at `docs/rca/<slug>.md`.

日本語の概要は準備中です。原文の説明を表示しています。

Trace bugs backward to their origin, not just symptoms

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Diagnose an uncertain bug by designing experiments that distinguish competing causes, especially speculative cache, timing, environment, or concurrency explanations.

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Use for a pre-mortem, or before committing to a plan, launch or migration: "what could go wrong?", "stress test this plan", "how might this fail?", "risks before we ship". Assumes failure, lists causes, ranks them and sets mitigations. Skip for explaining a failure that already happened.

日本語の概要は準備中です。原文の説明を表示しています。

Debug like Fable 5 — systematic root cause analysis with natural reasoning flow. Use this skill when you encounter an error, unexpected behavior, failing tests, or anything that does not work as intended.

日本語の概要は準備中です。原文の説明を表示しています。

4-phase root cause debugging: understand bugs before fixing.

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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.

日本語の概要は準備中です。原文の説明を表示しています。

Systematically find root causes and fix bugs. Use when debugging errors, investigating test failures, reproducing bugs from issue trackers (GitHub, Linear, Jira), or when stuck on a problem after failed fix attempts. Also use when the user says 'debug this', 'why is this failing', 'fix this bug', 'trace this error', or pastes stack traces, error messages, or issue references.

日本語の概要は準備中です。原文の説明を表示しています。

Perform a deep architectural analysis or security review requiring extended reasoning. Use for complex architecture decisions, security audits, or when standard analysis is not finding the root cause.

日本語の概要は準備中です。原文の説明を表示しています。

Diagnoses failing GitHub Actions runs by reading logs, identifying the root cause, and suggesting fixes. Use this skill whenever a user shares a GHA run URL, pastes runner log output, mentions a failing workflow, asks why a CI job is broken, or says anything like "my GHA is failing", "workflow failed", "can you check my run", or "CI is broken". Also trigger when raw log text is detected in the conversation that appears to come from a GitHub Actions runner. Do NOT use for general GHA workflow authoring questions, YAML formatting help, or passing runs with no failure context.

日本語の概要は準備中です。原文の説明を表示しています。

Form a committee of two high-reasoning agents to step back, do root cause analysis, and produce a plan. Use when stuck, looping, tunnel-visioning, or facing a hard planning problem.

日本語の概要は準備中です。原文の説明を表示しています。

Map existing materials into a first knowledge record, or investigate a subject's history in depth. Use whenever the user asks to map, survey, go through, reconstruct or investigate what already exists - folders of documents, decision records, meeting notes, emails, exports, code - or wants a starting record built from existing materials, even when the word 'record' is never said: consult this before listing or reading the files yourself, because a map has an agreed scope, a protocol and a report. Also, once, at a suitable moment, to offer the starting choices where a workspace has no record and the work is clearly ongoing - never on a greeting. Learning while working needs no map. Requests come in any language.

日本語の概要は準備中です。原文の説明を表示しています。

Isolate the root cause of ML performance drops, inconsistent evaluations, prediction errors, and training-serving mismatches across data, labels, splits, pipelines, models, metrics, and runtime behavior. Use when investigating a reproducible failure or regression, not routine model selection or general performance validation.

日本語の概要は準備中です。原文の説明を表示しています。

Measure, root-cause, fix, verify and record performance contention on a developer machine: cloud-sync daemons (iCloud Drive, OneDrive, Dropbox, Google Drive), file indexers, AI-tool MCP server fleets, and automation browsers left behind by tests and browser skills. Trigger when the machine is slow, apps freeze, browser or MCP tool calls time out, load exceeds the core count, or before and after any UI/UX/performance test run. Includes the L0–L4 optimisation scale and the test-hygiene contract.

日本語の概要は準備中です。原文の説明を表示しています。

Use when a bug has multiple plausible causes across layers — competing hypotheses, validation loops, evidence-based conclusions — even when the user just says 'why is this happening?'.

日本語の概要は準備中です。原文の説明を表示しています。