「fix」の検索結果

Stripe CLI の `stripe fixtures` コマンドで実行可能な fixture JSON を、対話的に整理しながら生成・検証する。test_clock を使った時間進行、サブスクのライフサイクル、失敗カードでの payment_intent 失敗、Connect 配下の Destination charge、bulk 件数のテスト顧客作成など、複数ステップの API リクエスト連鎖を扱う。「Stripe の fixture を書いて」「fixtures.json を作って」「このシナリオを fixture 化」「テストデータを大量に作りたい」「サブスクの月次失敗をテストしたい」「stripe trigger と fixture どっち使う」のような依頼で起動する。"fixture" という単語が無くても、複数ステップの Stripe テストデータ準備、 test clock を使った時間進行、 bulk なテスト顧客作成、 失敗パターンの再現といった意図が見えたら起動する。単発 API 呼び出しが目的なら起動せずに `stripe trigger` か API 1コールを案内する。

copy_tuner（CopyTuner / copy_tuner_client）で集中管理している i18n データを、prefix（正規表現）単位で Rails 標準の config/locales（YAML）管理へ移すスキル。gem の local_first_key_regexp を使うので、 gem を残したまま特定 prefix だけをローカル管理にできる（部分ローカル化）。1 回の実行で 1 prefix。 全 prefix を移して gem ごと撤去したい場合は繰り返し、完了後 copy-tuner-to-locales-cleanup スキルへ進む。 対象 prefix はスキル引数で指定でき、未指定なら export を俯瞰して選定する。

pr-auto-fix プラグインの監視ループを起動するトリガースキル。Hook で PR 作成が検知された後、Claude が `pr-auto-fix:auto-fix-pr` として呼び出すと Monitor (`pr-auto-fix-watcher`) がバックグラウンドで起動する。Monitor からの通知を受けて pr-auto-fixer エージェントへディスパッチする責務を持つ。ユーザーが手動で起動するスキルではなく、Hook 経由でのみ呼ばれる前提。

review-all を実行し、指摘内容をコードに反映、bun run c / bun run lint / bun test で自己検証し、CONVERGED/NOT_CONVERGED の収束判定付き完了レポートを /tmp/arima-review-fix-result.md に出力する。レビュー→修正→検証→レポートを一気通貫で行うスキル。スコープ引数は /review-all と同じ（wt / PR #N / vs:<branch> / range / ファイル列挙）。review-fix-loop からの自動呼び出し（mode=non-interactive）にも対応。Use when user says 'レビューして直して', 'review and fix', '自動修正', 'レビュー反映', or when invoked by review-fix-loop.

Using AutoFixture to automatically generate test data basic skill. Used when quickly generating test objects, reducing boilerplate code, and implementing anonymous testing. Covers Fixture.Create, CreateMany, circular reference handling, and xUnit integration. Keywords: autofixture, fixture, auto-generate test data, test data generation, anonymous testing, anonymous testing, fixture.Create, CreateMany, fixture.Build, Create<T>, AutoFixture.Xunit2, OmitOnRecursionBehavior, IFixture, generate test data, generate test data

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Fix a specific bug or problem in a {{engine_name}} project. Supports two modes — immediate fix or plan-first. Without arguments executes existing .unikit/code/FIX_PLAN.md. Checks {{engine_name}} compilation, suggests test coverage, and creates self-improvement patches. Use when user says "fix bug", "debug this", "something is broken", "fix the tests", "fix the errors in the tests", "read the errors in the {{engine_name}} console logs and fix the problems", or pastes an error message or {{engine_name}} console log. Also trigger when user shares a NullReferenceException, MissingReferenceException, compilation error, or any {{engine_name}}-specific error.

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 and fixes bugs, type errors, lint failures, CI/CD issues, and runtime errors via root-cause-first investigation. Use for defect remediation. NOT for investigation without a fix (see mk:investigate); NOT for build-only compilation errors (see mk:build-fix).

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。

Complete guide for AutoFixture advanced customization techniques. Use when you need to customize AutoFixture builders or handle special type test data generation rules. Covers DataAnnotations automatic integration, ISpecimenBuilder implementation, priority management. Includes DateTime/numeric range builders, generic design, and fluent extension methods. Keywords: autofixture customization, autofixture customize, autofixture customization, specimen builder, ISpecimenBuilder, RandomDateTimeSequenceGenerator, NumericRangeBuilder, DataAnnotations autofixture, fixture.Customizations, Insert(0), custom builder, NoSpecimen, generic builder

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既存機能の不具合を失敗するテストで再現し、修正、レビュー、承認後のコミットまで、各工程を担当するECCエージェントに割り振って進めるスキル。

• クラッシュや誤った出力を直したいとき
• 不具合の再発を検知するテスト作成
• 原因が分からない不具合の調査

Drive a failing GitHub Actions run to green in as few CI runs as possible, with a bounded fix-critique-commit-push-recheck loop that batches every evidenced fix into each push. Use when the user supplies a GitHub Actions run URL or run ID and wants the failure fixed, or asks to "make CI green", "fix the failing workflow/build/tests", "fix the red build", or to keep iterating on a branch or pull request until its checks pass. Pulls run results and failed-job logs through the gh CLI, and sends every fix plan to the rubber-duck reviewer before any code is touched.

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。

Iteratively detects and auto-fixes technical debt in a codebase using three quality criteria from debt-analysis references: conditional branch simplification, encapsulation, and separation of concerns. Applies fixes directly to code, runs existing tests to verify no regressions, re-analyzes, and loops until all high-severity issues are resolved or max iterations (5) reached. Use this skill when you want to automatically improve code quality with real code changes, reduce technical debt systematically, or clean up code to meet quality standards. Trigger on phrases like "fix technical debt", "clean up code quality", "refactor and fix issues", "improve code quality automatically", "apply debt fixes", "fix code problems", "eliminate debt".

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。

Contribute a verified bug fix for LazyCodex, lazycodex-ai, omo-codex, bundled Codex skills, or upstream Codex CLI bugs. Opens a fork PR only for upstream openai/codex; LazyCodex-owned defects become a verified-fix issue on code-yeongyu/lazycodex (never a PR — that repo is a generated distribution mirror). Use when the user asks to fix a bug, contribute a bug fix, contribute to fix bug, open a PR for a bug, or debug and PR a LazyCodex/Codex defect.

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。

Guide the user through an independent iFixAi audit of their own agent, checking whether it does the job it is supposed to do given their business rules and org structure. Prefer pointing it at the user's REAL deployed agent over its HTTP endpoint (its actual tools, retrieval, and governance) with `--provider http --endpoint <url>`; only when no endpoint is reachable, fall back to replicating the model beneath as a bare stand-in (Anthropic, OpenAI, Gemini, Azure, Bedrock, etc.). Graded by the judge(s) of their choice (the same model, one independent judge, or a cross-vendor panel). You are the operator who walks them through it and explains the scorecard, running the SAME `ifixai run` engine as the guided CLI. Use when the user asks to run iFixAi or to audit an agent.

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。

Contribute a verified bug fix for LazyCodex, lazycodex-ai, omo-codex, bundled Codex skills, or upstream Codex CLI bugs. Opens a fork PR only for upstream openai/codex; LazyCodex-owned defects become a verified-fix issue on code-yeongyu/lazycodex (never a PR — that repo is a generated distribution mirror). Use when the user asks to fix a bug, contribute a bug fix, contribute to fix bug, open a PR for a bug, or debug and PR a LazyCodex/Codex defect.

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。

End-to-end methodology for reproducing AND patching publicly disclosed, already-fixed vulnerabilities: sourcing the advisory and the fix commit, reading the patch diff, rebuilding a pinned offline lab, building a vulnerable-vs-patched contrast harness with proper controls, minimizing the trigger, designing and validating the fix, and writing it up as a blog post or whitepaper. Use this skill whenever the user wants to reproduce, demonstrate, understand, teach, or write about a known vulnerability or security incident: "reproduce this CVE", "build a repro for", "how did EchoLeak / Log4Shell / the postmark-mcp package actually work", "show me how this exploit worked", "write a blog post about this vulnerability", "set up a lab for CVE-XXXX", "pull the NVD/CVE/OSV details", or "make a PoC I can publish". Use it just as eagerly for the patching half: "how should this be fixed", "is this patch correct", "what would have caught this", "review this security fix", "write a regression test for this CVE", "how do I backport this", or "what's the right fix for this bug class" - even when the user never says the word "reproduce". Reach for it on vague asks like "help me understand this security advisory", "I want to write about this incident", or "can we test whether we were vulnerable" when a disclosed vulnerability is in play. This skill only handles ALREADY-PATCHED, publicly disclosed issues and refuses zero-day discovery, live targets, and weaponization.

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。

This skill should be used when the user asks to "fix pipelines", "fix CI", "check staging pipelines", "fix failing workflows", "fix failing actions", or wants to find and fix failing GitHub Actions workflows on the staging branch of the babysitter repo.

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。

This skill should be used when the user asks to "fix a bug with minimal changes", "analyze change scope for a bugfix", "find the minimal set of files to change", "do a focused bugfix", or "scope a minimal repair".

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 ClickHouse query errors in Rust when using clickhouse-rs crate. Use when: (1) "string is not valid utf8" errors - typically FixedString columns need CAST to String, (2) "tag for enum is not valid" errors - typically Option<T> fields receiving non-NULL values, (3) Sum/count aggregations returning Float64 but Rust expects u64, (4) LEFT JOIN results with empty strings where Rust expects Option::None, (5) "not enough data, probably a row type mismatches a database schema" - query SELECT returns fewer columns than the Rust Row struct expects (common when multiple query functions share the same struct but one function is missing columns added later), (6) Garbled/corrupted data when INSERTing to FixedString(N) columns using String Rust type - silently corrupts data (no error!) because String adds a length prefix but FixedString expects exactly N raw bytes. Fix: use [u8; N] with #[serde(with = "BigArray")] from serde-big-array, (7) "the trait bound `[u8; 64]: serde::Serialize` is not satisfied" when using [u8; N] arrays in Row structs - need #[serde(with = "BigArray")] annotation from serde-big-array crate.

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 QA test a web application and fix bugs found. Runs QA testing, then iteratively fixes bugs in source code, committing each fix atomically and re-verifying. Use when asked to "qa", "QA", "test this site", "find bugs", "test and fix", or "fix what's broken". Three tiers: Quick (critical/high only), Standard (+ medium), Exhaustive (+ cosmetic). Produces before/after health scores, fix evidence, and a ship-readiness summary. For report-only mode, use /qa-only.

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 QA test a web application and fix bugs found. Runs QA testing, then iteratively fixes bugs in source code, committing each fix atomically and re-verifying. Use when asked to "qa", "QA", "test this site", "find bugs", "test and fix", or "fix what's broken". Three tiers: Quick (critical/high only), Standard (+ medium), Exhaustive (+ cosmetic). Produces before/after health scores, fix evidence, and a ship-readiness summary. For report-only mode, use /qa-only.

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。

Update an EXISTING Tip in the ai-product-dev-tips repository — edit its README, add or fix code, refresh outdated information, fix mistakes — while keeping the repo's format conventions and syncing the root README link. Use this whenever the user wants to update, edit, fix, revise, refresh, correct, or complete an existing Tip in this repo — e.g. "nlp_processing/29 の README を更新して", "〇〇の Tip を最新仕様に直して", "image_processing/11 の説明を修正して", "あの Tip のリンク切れを直して", "この Tip を完成版にして". It locates the target Tip, studies the current format conventions (shared with the ai-product-dev-tips-create skill), applies consistent edits, verifies the content with web research (no fabrication), and syncs the root README link (title text / [In-progress]). This is for UPDATING existing Tips, not creating new ones — to add a brand-new Tip use the ai-product-dev-tips-create skill instead. Trigger this even if the user doesn't say "skill".

Diagnose and fix slow pyfixest regression GRIDS (many feols/fepois calls run sequentially) that stay slow despite demeaner_backend="cupy64" and an idle GPU. Use when: (1) a script looping dozens of pf.feols models on a 100k+ row panel takes ~1 min/model, (2) process inspection shows ~1-1.5 cores busy and nvidia-smi shows ~0% GPU utilization with a resident cupy context, (3) planning any worker prompt that will run a model grid (robustness variants x FE structures x domains). Root cause: per-model CPU-side single-threaded fixed costs (formulaic model-matrix build, interaction construction, singleton detection, cluster vcov) dominate wall time; GPU demeaning is a small slice. Fix: shard the model grid across OS processes and/or use pyfixest multiple-estimation syntax; mandate this IN THE WORKER PROMPT.

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 QA test a web application and fix bugs found. Runs QA testing, then iteratively fixes bugs in source code, committing each fix atomically and re-verifying. Use when asked to "qa", "QA", "test this site", "find bugs", "test and fix", or "fix what's broken". Proactively suggest when the user says a feature is ready for testing or asks "does this work?". Three tiers: Quick (critical/high only), Standard (+ medium), Exhaustive (+ cosmetic). Produces before/after health scores, fix evidence, and a ship-readiness summary.

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 and fix failing CI. Takes a PR reference, one or more CI run/job URLs, or freeform text describing where the failures are, then enumerates failing workflows/jobs/steps, determines the root cause (often one shared cause behind several red checks), proposes a fix plan, applies code changes, commits, pushes, watches the new runs, and repeats until checks pass. Use when the user says "fix CI", "CI is red", "checks are failing", "the build is broken on my PR", "fix the failing jobs", or pastes a failing CI run URL. Do NOT use for authoring new workflows, or for read-only diagnosis with no fix intent — use gha-log-reader for that.

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。