「cause」の検索結果

Find root cause of errors, crashes, regressions, failing tests before any fix. Not for code review or new features.

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。

Access the Firebase Emulator Suite (Firestore, Auth, Functions, Hub) via REST and Admin SDK without trial-and-error. Use this skill whenever you read/write/query/clear Firestore Emulator data, create test users or obtain ID tokens from the Auth Emulator, verify Functions triggers, seed/export emulator data, or hit unexpected 403/404 from an emulator endpoint. Consult it BEFORE curling any emulator port — it encodes the auth header and host-resolution rules that otherwise cause retries.

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。

Investigate implementation failures using root-cause-first debugging. Use when an implementer is blocked, verification fails, or repeated remediation does not converge.

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。

Plan explicitly authorized read-only production observations and trace evidence-backed causes into preventive regression coverage without external effects.

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。

Apply systematic root cause analysis and debugging methodologies to diagnose and fix bugs, test failures, and unexpected behavior. Use when encountering production issues, investigating test failures, diagnosing performance problems, tracing error sources through call stacks, analyzing logs and stack traces, reproducing inconsistent bugs, debugging race conditions, investigating memory leaks, or applying scientific method to problem-solving before proposing fixes.

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。

Proactively search for security threats, vulnerabilities, and suspicious patterns in applications and infrastructure before they cause damage. Use when conducting security audits, identifying vulnerabilities, analyzing security logs, detecting suspicious patterns, investigating potential breaches, performing penetration testing, or implementing security monitoring.

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 asked to debug, fix a bug, investigate an error, or do root cause analysis, and when users report errors, stack traces, unexpected behavior, or say something stopped working.

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。

Restore working context saved earlier by /context-save. Loads the most recent saved state (across all branches by default) so you can pick up where you left off — even across Conductor workspace handoffs. Use when asked to "resume", "restore context", "where was I", or "pick up where I left off". Pair with /context-save. Formerly /checkpoint resume — renamed because Claude Code treats /checkpoint as a native rewind alias in current environments. (gstack)

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。

Save working context. Captures git state, decisions made, and remaining work so any future session can pick up without losing a beat. Use when asked to "save progress", "save state", "context save", or "save my work". Pair with /context-restore to resume later. Formerly /checkpoint — renamed because Claude Code treats /checkpoint as a native rewind alias in current environments, which was shadowing this skill. (gstack)

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。

Content sanitization and hidden-content detection for agent skill security. Scans markdown, HTML, and text for visually hidden but agent-readable attacks: CSS-hidden text (display:none, color:white, font-size:0, opacity:0), HTML comments with instructions, collapsible details sections, zero-width unicode, homoglyphs, misleading links, and inline HTML in markdown. Enforces mandatory sanitization before external content enters agent context. Load as part of the secure-* sequence during any repo scan or skill audit. Also load for sanitize content, check hidden text, scan markdown attacks, strip HTML, detect invisible instructions, check zero-width chars. Core principle: visibility does not equal influence — hidden content is more dangerous than visible content because agents process it but humans cannot see it.

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。

Select alternative execution paths when a plan step fails — branch on outcomes instead of blind retry. Load when a structured plan step fails, an unexpected observation arrives, or the user asks what to try next after an error. Also triggers on "try another approach", "route around this failure", "replan on failure", "if X fails try Y", or outcome-based branching during multi-step work. Pairs with structured-planning and debug-and-fix. Does not replace root-cause debugging — adds plan-level path selection.

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。

Fix broken or failing functionality through structured reproduction, root-cause analysis, minimal fix, and verification. Load when the user asks to fix a bug, debug an error, resolve an issue, or work on a Linear ticket. Also triggers on "this is broken", "fix this bug", "why is this failing", "debug this", "resolve this error", "what went wrong", or any request to diagnose and fix a 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。

Systematically finds and fixes bugs using proven debugging techniques. Traces from symptoms to root cause, implements fixes, and prevents regression.

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。

You are an expert in the interface between LLMs and the outside world. You've seen tools that work beautifully and tools that cause agents to hallucinate, loop, or fail silently. The difference is almost always in the design, not the implementation.

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。

Infer or annotate TCR antigen specificity by unsupervised clustering (TCRdist/tcrdist3, GLIPH2, clusTCR, GIANA) and database lookup (VDJdb, IEDB, McPAS-TCR), and rank candidates with supervised predictors (ERGO-II, NetTCR-2.x, pMTnet) under explicit caveats. Encodes the central truth that general TCR-epitope prediction for UNSEEN epitopes essentially does not work (collapses to near-random; IMMREP22, Grazioli 2022) because labeled data is dominated by a few immunodominant epitopes and there is no true negative set — so clustering for discovery is the honest task and de-novo binding needs wet-lab validation. Use when annotating TCR specificity or grouping a repertoire. Epitope/MHC context lives in mhc-binding-prediction.

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。

Predict B-cell and T-cell epitopes for vaccine antigen design and epitope mapping with BepiPred-3.0, DiscoTope-3.0, the IEDB tools, and EL-mode MHC presentation. Encodes the load-bearing asymmetry that T-cell epitope prediction is mature (it reduces to MHC presentation, AUC>0.9) while B-cell prediction is unreliable (linear predictors ~AUC 0.6 because ~90% of real epitopes are conformational) — so structure-based DiscoTope-3.0 on AlphaFold models is the only defensible B-cell path, propensity scales are obsolete, and NetChop is largely redundant on EL-trained models. Use when mapping epitopes or selecting vaccine antigens. MHC binding lives in mhc-binding-prediction.

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。

Estimate 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。

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。

Use this skill for record retention after a driver leaves your employment. Covers when records can be destroyed vs retained for 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。

Use this skill when the user asks about CDL driver wellness, retention strategies, what causes driver turnover, common health issues (obesity, hypertension, sleep apnea, mental health), benefits packages, home-time policies, and how to build a culture that keeps drivers. Reference ATA Workforce Development + FMCSA Wellness Initiative.

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。

Rules for SKILL.md files that trigger, fit, and are proven. Use when creating a SKILL.md, rewriting its description, splitting an oversized SKILL.md, or editing one because an agent ignored, misread, or never fired it.

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。

The bar every automated test or guard must clear: green only when the real system works. Use when writing, fixing, or reviewing a test, mock, stub, fixture, snapshot, or self-test; adding a regression test for a bug; or about to call work done because tests pass.

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。

Read the Convex deployment's 72h insights (read limits, OCC contention), root-cause each event in code, report evidence-backed perf/cost findings with fixes.

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。