「failure」の検索結果

Make an AI agent or automation reliable enough to trust — the tests, checks, and guardrails that catch its failures before they reach anything real. Use when asked how do I test my AI agent, make my automation reliable, my agent works sometimes, or how do I trust an AI workflow in production. Produces a map of where the agent can fail (bad input, hallucination, wrong tool call, edge cases, silent errors), the checks that catch each (validation, evals on real cases, human-in-the-loop gates, monitoring), a right-sized reliability plan scaled to the stakes, and a rollout that earns trust incrementally.

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Review AI-authored code for its characteristic failure modes — plausible-but-wrong logic, hallucinated APIs, over-engineering, dead scaffolding, and silent security shortcuts. Use when reviewing an AI-generated or heavily AI-assisted PR, when AI-written code keeps shipping subtle bugs, or when setting review standards for a team using coding agents. Produces a focused review with AI-specific findings, verification steps per risk class, and a team checklist for AI-authored changes. For general PR review use code-review-checklist — this skill covers what that one assumes a human wouldn't do.

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。

Design an AI-assisted workflow for a recurring task — which steps to hand to AI, which to keep human, and how they connect — so you get leverage without losing quality or control. Use when asked how do I use AI for [process], automate this with AI, design an AI workflow, or where does AI fit in my process. Produces a map of the task's steps split into AI-does / human-does / human-checks, the right tool/prompt for each AI step, the hand-offs and review points, the failure modes to guard against, and a start-small rollout — turning a manual process into a reliable AI-assisted one that keeps you in control.

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。

Review an LLM agent design and find where it will be unreliable, expensive, or unsafe. Use when asked to review an agent architecture, critique a multi-step/tool-using agent, debug an agent that loops or goes off-task, or harden an agent before launch. Produces a structured review — task fit, control flow, tools, memory/context, failure handling, cost, and safety — with prioritised findings and fixes.

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Designs and implements testing strategies for any codebase. Use when adding tests, improving coverage, setting up testing infrastructure, debugging test failures, or when asked about unit tests, integration tests, or E2E testing.

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。

Standard recovery patterns for all squad agents. When something fails, adapt — don't just report the failure.

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Help users navigate the psychological burdens of leadership by cultivating emotional awareness, reframing failure, and building sustainable mental health practices.

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 anti-PUA. Drives AI with wisdom, trust, and inner motivation instead of fear and threats. Activates on: task failed 2+ times, about to give up, suggesting user do it manually, blaming environment unverified, stuck in loops, passive behavior, or user frustration ('try harder', 'figure it out', '换个方法', '为什么还不行'). ALL task types. Not for first 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。

The anti-PUA. Drives AI with wisdom, trust, and inner motivation instead of fear and threats. Activates on: task failed 2+ times, about to give up, suggesting user do it manually, blaming environment unverified, stuck in loops, passive behavior, or user frustration ('try harder', 'figure it out', '换个方法', '为什么还不行'). ALL task types. Not for first 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。

Pre-launch and pre-commit audit for vibe coding projects. Use when asked to check whether a project is ready to ship, deploy, merge, or commit, especially for common AI-built app mistakes: broken project structure, committed secrets or cache files, environment variable hygiene, database migrations, ORM/schema drift, unsafe raw SQL, unused legacy code, dead routes/components, weak auth, missing tests, build failures, and deployment footguns.

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 to review a generated HTML deck or single-page artifact for visual quality and structural correctness. Especially good at catching CSS specificity bugs where slide-modifier classes (.divider, .center, .q, .flow etc.) accidentally override the base .slide{display:none} and cause one slide to stack on top of every other. Also catches missing keyboard navigation, bullet-dump / wall-of-text failure modes, broken self-containment (external link/script/img). Patches the file in place; never changes the original content (slide text, numbers, named entities are the author's work, not yours).

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 encountering a bug, test failure, or unexpected behavior, before proposing fixes

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Diagnose a reproducible failure, fix its cause, and verify the regression. Use for crashes, incorrect behavior, and failing tests.

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 proactively verifying an installed BGS modpack batch before declaring it good. Triggers - "test the pack", "verification", "post-install check", "is it stable", "what should I test", "测试整合包", "验证安装". NOT for reactive crash/performance diagnosis after failure (use diagnosing-bgs-problems), pre-install mod evaluation (evaluating-bgs-mods), or defining batch boundaries/style (curating-bgs-modpack).

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Use for UI/UX design, product flows, frontend or native interface design, design audits, design translation, asset planning, motion direction, design tokens, or distinctive non-generic apps and websites. Covers web and React, Windows and WinUI/Mica, Apple and SwiftUI, Android and Jetpack Compose/Material, cross-platform UI, and token export. Route to the focused skill, then load only the references required for the selected surface and failure mode.

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Writes Vitest tests following project patterns: __tests__/ directories, vi.mock() for module mocking with vi.hoisted() for test-time factories, global LLM mock from src/test/setup.ts, environment variable save/restore in beforeEach/afterEach, vi.clearAllMocks() lifecycle, and test file organization. Use when user says 'write tests', 'add test coverage', 'test this', creates *.test.ts files, or when test failures appear in CI. Do NOT use for non-test code or for debugging without writing tests.

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。

Pick the right way to represent a dataset so a reader gets the finding in three seconds — a catalog of 20+ chart and diagram forms with when-to-use and failure modes, plus the encoding decisions that make any of them readable (takeaway headline, direct labels, kill the axis, highlight-and-mute, show the caveat). Style-agnostic — use with whatever palette or design system the destination already has. Use when charting survey results, benchmark data, usage metrics, or research findings for a post, brief, deck, or report, and whenever the default bar chart feels like it's burying the point.

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Review generated or changed production code before it ships, using Clean Code, SOLID, DRY, KISS, YAGNI, and LLM-specific failure-mode checks in any programming language. Best used reactively after an agent writes, edits, refactors, or fixes code, before presenting, committing, or merging the result. Use when the user asks "review this PR", "is this safe to merge?", "make this cleaner", "audit this code", "refactor this", "fix this bug", or after a coding agent produced implementation code. Can also guide writing when explicitly invoked before a risky edit. Invoke it on your own initiative the moment you finish writing, editing, or refactoring non-trivial production code, before presenting or committing — don't wait to be asked. DO NOT USE for factual/conceptual questions, CI/tooling config, git workflow, running/debugging tests, pure architecture discussion, prose writing, data analysis, or test-code review (use test-guard).

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。

Add, configure, and manage faults on Simulink, Simscape, and System Composer model signals for robustness analysis and safety validation. Use when injecting faults (stuck, noise, gain, offset) onto block inports/outports, enabling fault simulation, or analyzing fault effects. Covers sensor failures, signal corruptions, actuator faults, FMEA validation, and robustness testing. Requires Simulink Fault Analyzer.

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Write and simplify C# property-based, model-based, and executable specification tests with CsCheck. Use for generators, shrinking, stateful command sequences, Spec guards and rules, implementation conformance, or replaying generated failures.

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Handles BED-format genomic intervals (BED3 through BED12, narrowPeak/broadPeak) and the coordinate-system substrate the whole interval category rests on, with bedtools (CLI) and pybedtools/pyranges/pandas (Python). Covers the 0-based half-open vs 1-based-closed convention boundary and the start-1/end-unchanged conversion, the silent failures (chrom-name mismatch, CRLF, lexicographic-vs-version sort under -sorted), genome/chrom.sizes generation, sorting contracts, BED12 block invariants, validation, makewindows, cross-assembly liftover (liftOver/CrossMap), and BED<->VCF/BAM/FASTA conversion. Use when reading, creating, validating, sorting, lifting between genome builds, or converting interval files, preparing inputs for bedtools/tabix/bigBed, or debugging an off-by-one or empty-overlap result.

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Detects m6A modifications from Oxford Nanopore direct-RNA-seq (DRS) signal using m6Anet (multiple-instance-learning over DRACH 5-mer signal). Covers the upstream pipeline (Dorado/Guppy basecalling -> minimap2 map-ont -> nanopolish eventalign -> m6anet dataprep -> m6anet inference), per-site vs per-read probability including the mod_ratio stoichiometry column, the DRACH-only constraint, minimum-coverage thresholds (20-50 reads/site), multi-condition comparison via xPore/Nanocompore/ELIGOS, Dorado native modification calling (RNA004, 2024+), and the cDNA-vs-DRS distinction (cDNA Nanopore CANNOT detect modifications). Use when calling m6A from ONT DRS without immunoprecipitation, choosing m6Anet vs xPore vs Nanocompore vs ELIGOS vs Dorado native, interpreting probability_modified vs mod_ratio vs per-read probabilities, deciding between m6Anet (known DRACH sites) and Dorado/Remora (genome-wide screening), pinning RNA002 vs RNA004 chemistry and basecaller versions, or troubleshooting eventalign/dataprep failures.

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Estimates time-scaled phylogenies, molecular-clock rates, effective reproduction number R_e, and population dynamics from dated pathogen genomes using TreeTime (maximum-likelihood) and BEAST2 (Bayesian; strict/relaxed clocks; coalescent, Bayesian-Skyline, Skygrid, Birth-Death-Skyline, and sampled-ancestor priors; structured coalescent via MASCOT). Covers root-to-tip clock QC via TempEst, date-randomisation tests, recombination masking via Gubbins/ClonalFrameML before clock inference for recombining bacteria, BDSKY origin-vs-rootHeight pitfalls, sampling-bias correction, multi-chain convergence diagnostics, and reconciling phylodynamic R_e with case-based R_t. Use when dating outbreak origins, estimating substitution rates, inferring R_e through time, building time-calibrated Nextstrain Augur trees, choosing between strict and relaxed clocks, fitting Birth-Death-Skyline models, diagnosing temporal-signal failure, running MASCOT for structured-population analyses, or using UShER for pandemic-scale placement.

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Normalizes and transforms RNA-seq count matrices for DE, visualization, clustering, and ML. Covers between-sample (TMM, TMMwsp, RLE/median-of-ratios, upper quartile), within-sample (TPM, FPKM/RPKM), variance-stabilizing (VST, rlog, log-CPM), GC-content correction (cqn, EDASeq), and single-cell (scran deconvolution, scanpy normalize_total). Encodes the composition-bias rationale, the "most genes not DE" assumption and its catastrophic failure modes (MYC amplification, apoptosis, viral host shutoff, prokaryotic stress), the "lengthScaledTPM is not TPM" naming trap, the "TPM is not for DE" rule, the blind=TRUE vs FALSE decision, ERCC spike-in normalization (SBN), and the single-cell zero-inflation breakdown of TMM/RLE. Use when choosing or applying normalization, debugging shifted-MA-plot diagnostics, handling zero-heavy single-cell data, or correcting GC bias.

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。