「apply」の検索結果

Apply when debugging. Trace each symptom to its root cause and fix it there; reproduce first, ask why until you reach it, resist nil-check guards that silence crashes.

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Apply to any non-trivial work, not just bulk work: edits, migrations, analyses, checks. Build the tool that does it or proves it (codemod, script, generator, or a skill your subagents follow) instead of working by hand. The tool is the artifact a reviewer can rerun.

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Apply when product, UX, or feature-scope tradeoffs come up. Choose user delight over implementation convenience; ship fewer polished features over more rough ones.

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Apply to multi-step work (sweeps, migrations, runs of similar edits) and to how you stack commits and PRs. Break work into small units that each end in a verifiable state, check each before the next, and order delivery so the sequence proves itself to a reviewer.

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Apply when refactoring, evaluating diff size, or tempted to add abstractions, layers, or signal threading. Bias toward deletion and the smallest change that solves the problem.

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Survey a whole React codebase as a senior React engineer, using React Doctor's scan as evidence, then produce a prioritized audit and self-contained implementation plans for other agents (or cheaper models) to execute. Read-only on source code — it plans improvements, it does not apply them. Use when the user asks to "improve the React code", "audit this codebase", "make this app faster / more robust", or wants a roadmap of fixes rather than a review of a single diff. For a regression check or a fix-it-now pass, use the `react-doctor` skill instead.

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Apply when context is filling up: large outputs, long files, repeated reads, fan-out planning. Route bulk to subagents; keep summaries in the main thread, not raw payloads.

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Apply when facing a novel UI interaction or architectural decision with no precedent in the codebase. Build 2-3 competing prototypes and compare side by side before committing.

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Rigorous engineering mode for nontrivial work in this repo — a set of named principles plus the leaf skills that apply them. Use when the user says "pstack", "go deep", "be rigorous", "认真做", or when a task involves architecture, a real bug, a refactor, or anything the user will not be watching. Ported from cursor/plugins pstack by Lauren Tan (MIT); see ATTRIBUTION.md.

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Apply when wiring validation, error handling, or framework adapters. Concentrate guards at system boundaries (CLI, config, network, external APIs); trust internal types and keep business logic in pure functions.

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Apply when you catch yourself writing the same instruction a second time, or notice a recurring correction. Encode the rule as a lint, metadata flag, runtime check, or script instead of more text.

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Apply when concurrent actors might write to the same file, branch, key, or state object. Eliminate the sharing first; serialize structurally only when one shared writer is a real invariant.

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Apply when designing types, reviewing a function signature, or writing code in any statically-typed language. Make illegal states unrepresentable, brand semantic primitives, parse external data at boundaries, refuse to lie to the compiler, exhaust variants, derive from authoritative schemas.

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Mask, redact, and anonymize sensitive data (PII, PCI, PHI) in databases, logs, and APIs. Use when protecting PII in dev/staging environments, redacting sensitive data from logs, anonymizing data for analytics, or applying k-anonymity and differential privacy for GDPR-compliant data sharing.

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Makes working code easier to read and change without altering what it does, and proves the behaviour stayed the same. Use when someone says "simplify this", "clean up this function", "this file is a mess", "reduce the complexity", "refactor for readability", "tidy my diff before the PR", or when a linter reports high cyclomatic complexity or deep nesting. Covers pinning current behaviour with characterization tests, measuring complexity, choosing a named refactoring, applying it in small verified steps, and reporting suspected bugs found on the way instead of fixing them silently.

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Read and apply a project-specific BUILDING_SPEC.md or DIMENSIONS.md file before generating any architectural elements. Use when: generating floor plans, 3D buildings, or any spatial design where the project has defined custom dimensions, room requirements, or design standards.

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Create 2D art and animation in Blender using Grease Pencil and Python. Use when the user wants to draw strokes programmatically, create 2D animations, build Grease Pencil objects from code, manage GP layers and frames, apply GP modifiers, set up drawing guides, or script any Grease Pencil workflow in Blender.

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Blender's compositor post-processes renders with a node graph, and this skill scripts it from Python (bpy) in Blender 5.x. Use when the user wants to set up compositor nodes, add post-processing effects, color correct renders, combine render passes, apply blur or glare, key green screens, write passes to multilayer EXR, or port a compositing script that broke after Blender 5.0 (scene.node_tree, CompositorNodeComposite, glare_type and file_slots are gone).

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Write and run Blender Python scripts for 3D automation and procedural modeling. Use when the user wants to automate Blender tasks, create 3D models from code, run headless scripts, manipulate scenes, batch process .blend files, build geometry with bmesh, apply modifiers, generate procedural shapes, or import/export 3D models using the bpy API.

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。

Automate video editing in Blender's Video Sequence Editor with Python. Use when the user wants to add video, image, or audio strips, create transitions, apply effects, build edit timelines, batch assemble footage, estimate render times, or script any VSE workflow from the command line.

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AWS Bedrock — fully managed foundation models on AWS infrastructure. Use when deploying AI in AWS-native environments, needing enterprise compliance (SOC2, HIPAA), running Claude, Llama, Titan, or Mistral on AWS, leveraging Knowledge Bases for RAG, or applying Guardrails for content safety.

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Build with Amazon DynamoDB for fast, scalable NoSQL storage. Design tables with partition and sort keys, create GSI and LSI for flexible queries, enable streams for change data capture, and apply single-table design patterns for efficient data modeling.

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World-class infrastructure automation - Terraform, Pulumi, CloudFormation, and the battle scars from managing infrastructure that handles production trafficUse when "terraform, pulumi, cloudformation, infrastructure, iac, state file, remote backend, s3 backend, dynamodb lock, terraform plan, terraform apply, terraform destroy, module, workspace, provider, resource, state drift, import, aws, gcp, azure, infrastructure, terraform, pulumi, cloudformation, iac, devops, aws, gcp, azure, cloud" mentioned.

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Use when designing enterprise systems, applying TOGAF framework, creating capability maps, implementing domain-driven design, or planning technology transformations - covers ADM phases, architecture domains, and governanceUse when ", " mentioned.

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。