「fix」の検索結果

Owns instruction files: AGENTS.md, CLAUDE.md, personal and repo rules. Use when writing, pruning, auditing them, sharing rules across Claude and Codex, or fixing ignored rules.

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 tunes SQL for OLTP workloads on PostgreSQL, MySQL, and SQL Server. Use when tuning queries, reading plans, indexing, or fixing lock contention.

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。

Investigates a bug, failing test, build error, regression, flaky behavior, performance symptom, or unexpected result by reproducing it, minimizing the case, testing ranked hypotheses, tracing the first incorrect state, and locking the root cause with a regression check. Use before proposing a fix. Not for planning a new feature or reviewing a healthy diff.

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。

Reviews an existing codebase for structural friction, unclear ownership, leaky or shallow interfaces, excessive coupling, misplaced state, poor testability, and risky dependency direction, then prioritizes evidence-backed improvement candidates. Use for architecture audits, modularization, modernization, or recurring cross-cutting change pain. Not for designing one new interface, simplifying a local function, or fixing a reproduced bug.

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。

Writes a detailed, executable implementation plan from an approved design or multi-step requirement, including exact files, interfaces, tests, commands, checkpoints, and rollback considerations. Use before touching code for a substantial feature, migration, refactor, or cross-cutting change. Not for a one-file bounded fix or for executing a plan that already exists.

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。

Processes code review feedback by classifying each finding, reproducing or validating it against the real contract, applying the smallest in-scope fix, and re-running focused proof. Use after comments arrive on a diff, pull request, design, or implementation. Not for requesting a first review or debugging an unrelated failure.

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。

Designs or reviews logs, metrics, traces, alerts, correlation, dashboards, and diagnostic instrumentation so failures and performance changes can be detected and explained at component boundaries. Use for observability work, incident readiness, instrumentation, or production diagnostics. Not for fixing a specific incident before reproducing it or for adding noisy logging without an operational question.

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。

Reviews a GitHub pull request or focused branch diff for correctness, regressions, security, compatibility, test gaps, and maintainability. Use when an implementation needs an independent source-aware review before merge. Produces prioritized findings with evidence and actionable fixes. Not for architecture-only audits, formatting preferences, or processing existing review feedback.

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。

Designs and executes compatibility-preserving migrations across APIs, schemas, frameworks, dependencies, protocols, storage, and infrastructure with baselines, coexistence, staged cutover, rollback or forward-fix paths, and explicit removal criteria. Use for deprecations, upgrades, data moves, contract changes, and replacing live implementations. Not for a local refactor with no external state or compatibility boundary.

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。

Explores ambiguous product or engineering intent before implementation, clarifies users, constraints, alternatives, risks, and acceptance criteria, and turns the result into an approved design direction. Use for a new feature, subsystem, creative UI, architecture change, or unclear request. Not for an already-scoped bug fix, an existing implementation plan, or a narrow code review.

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。

Implements behavior changes with a test-first red-green-refactor loop: write one meaningful failing test, watch it fail for the intended reason, make the smallest change, then refactor while green. Use for new behavior, bug fixes, refactors with preserved contracts, and regression tests. Not required for disposable prototypes, generated files, or documentation-only edits.

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 any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .xltx, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like "the xlsx in my downloads") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.

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。

Turn multiview fit reports into a sequential or parallel repair queue for aligning Blender products to source-of-truth templates. Use after validation shows disalignment, when the agent must iteratively fix wireframe, texture, lighting, or projection mismatches, and when skill gaps should trigger self-refinement before another rebuild.

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 non-deterministic (flaky) tests by reading CI run logs or test result history. Aggregates pass rates per test, identifies intermittent failures, recommends quarantine or fix, and maintains a flaky test registry. Best run during Polish phase or after multiple CI runs.

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 implementing PlainTab features or bug fixes with test-first development, red-green-refactor, regression scripts, browser checks, or behavior-focused validation before production code changes.

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。

Claude adapter for PlainTab test-first feature work, bug fixes, regression scripts, and behavior-focused 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。

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

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。

Connect warehouses, BI tools (Tableau, Looker, Power BI) and custom connectors to Monte Carlo with API v2 tools: show what exists, reference credentials, create, validate and fix connections. Use to onboard platforms or generate Terraform or scripts.

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 remediate data quality alerts using Monte Carlo MCP tools. Runs root cause analysis, assesses blast radius, discovers available tools (MCP/CLI/API), proposes and executes fixes, or escalates with full context when uncertain.

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。

Orchestrate incident response — triage, root cause, remediate, prevent recurrence. USE WHEN active alerts, data broken, stale, pipeline failure, or investigate and fix a data incident.

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。

Maintain the slopus/happy open source project. Triage issues, manage the GitHub project board, draft closing comments, find duplicates, check if bugs are fixed on main, and engage with community contributors. NEVER posts comments or closes issues without showing exact text and getting approval first.

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。

Ship completed work: verify locally, commit related changes, push, create or update the PR, watch CI/reviews, and fix until merge-ready or escalated. Use for "ship it", "create PR", "handoff", or finished code needing delivery.

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。

Implement from a spec or plan: extract stories, build in safe waves, test, commit, and get peer review per story. Use for "implement", "build/code this plan", or targeted fix findings. If no plan exists, use /ship:design first.

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。

Generating test data and fixtures. Use when factory pattern design, boundary value data generation, synthetic data generation, or seed data management 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。