「apply」の検索結果

Bloom's Taxonomy of Educational Objectives and mastery learning as practical instructional design tools. Covers the six cognitive levels (remember, understand, apply, analyze, evaluate, create), the revised 2001 taxonomy, the knowledge dimension, mastery learning loops, formative vs. summative assessment, and the writing of learning objectives that are both specific and verifiable. Use when designing lessons, writing objectives, building assessments, or diagnosing why a learner is stuck at one cognitive level.

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Causal reasoning in historical analysis. Covers distinguishing immediate from underlying causes, tracing chains of causation across time scales, identifying unintended consequences, and applying counterfactual reasoning to assess causal significance. Use when analyzing why historical events occurred, evaluating the relative weight of causes, or assessing whether outcomes were inevitable or contingent.

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Applying motion study and time analysis to household work. Based directly on Lillian Gilbreth's *The Home-Maker and Her Job* and *Cheaper by the Dozen*, this skill covers the therblig-motion catalog, task decomposition, batch processing, parallelism, routine chart design, and the ergonomics of work surfaces. Use when diagnosing why a household is running out of time, designing a weekly routine, teaching task sharing to children, or reducing the friction of any repeated household task.

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Networking fundamentals as they apply to cloud infrastructure — virtual networks, subnets, routers, NAT, floating IPs, security groups, VLAN/VXLAN segmentation, load balancing, SDN concepts, and how Neutron-style cloud networks sit on top of physical topology. Covers TCP behavior at scale, congestion control, tail latency, overlay networks, and the operational gotchas that come from network layering. Use when designing cloud network topology, debugging cross-AZ latency, or reviewing a proposed VPC/SG design.

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Apply Kubernetes and Kustomize patterns for deployments, services, and overlays. Use when writing or reviewing k8s manifests.

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Apply Docker and Docker Compose best practices for containerising services. Use when writing or reviewing Dockerfiles and compose configs.

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Apply Terraform module structure, state management, and variable patterns for infrastructure as code. Use when writing Terraform.

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Apply Redis data structure, caching, and pub/sub patterns. Use when integrating Redis into a service.

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Apply Stripe API integration patterns: webhooks, idempotency keys, and subscription handling. Use when integrating Stripe payments.

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Apply best practices for Jupyter notebooks: cell ordering, reproducibility, parameterisation. Use when working with .ipynb files.

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Apply PostgreSQL schema, migration, and query patterns with idempotent SQL. Use when writing database schemas or migrations.

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Apply Supabase schema, RLS policies, and edge function patterns. Use when building on Supabase.

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Apply Vercel deployment, edge function, and environment variable patterns. Use when deploying to Vercel.

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Onboard a repository to skillsaw — run the linter, triage the findings by rule, apply autofixes, manually fix what remains, set up CI, and create a baseline. Use when adopting skillsaw on a new or existing project.

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Use when modifying agentic contextual building blocks like skills (SKILL.md), slash commands, agents, hooks, plugins, marketplaces, and instruction files (CLAUDE.md, AGENTS.md, GEMINI.md, QWEN.md, Cursor, Copilot, Cline, or Kiro rules), tool configuration such as an OpenCode `opencode.json`, Muse Code hooks (`.muse/hooks.json`), a Grok Build `.grok/` project layer or a Google Antigravity customization root (`.agents/`, `.agent/`, `_agents/`, `_agent/` — hooks, MCP config, rules, registries and plugins), and committed project memory (`.agents/memory/`). Run skillsaw on the files you touched, apply autofixes, resolve remaining violations with `skillsaw explain` guidance, and re-lint until clean before considering the work complete.

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Fix skillsaw lint violations — apply deterministic autofixes, then resolve the remaining violations with targeted edits guided by `skillsaw explain`. Use when skillsaw reports violations, when asked to clean up lint findings, or after `skillsaw fix` leaves violations behind.

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ANIMA as limit construction over condensed skill applications. Formalizes prediction markets as belief ANIMAs, structure dishes as condensation media, and impact as equivalence class change. Use for understanding agency at maximum entropy, compositional world modeling, or applying Scholze-Clausen condensed mathematics to AI.

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Bridge active inference theory with robot control using K-Scale's JAX/MuJoCo stack. Use when connecting predictive coding to locomotion policies, mapping KL divergence minimization to RL training, applying mean field approximation to robotics state estimation, or implementing sim2real as inference about future observations.

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Pytest testing patterns for Python — fixtures, behavioral naming, behavior/risk-driven coverage, property-based testing with Hypothesis when useful, and mutation testing when justified. Use when writing tests, designing fixtures, configuring coverage, or applying parametrize, async testing, or property-based strategies.

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Handles TOML configuration file operations in Python using tomlkit for comment-preserving read-modify-write cycles. Use when reading or writing pyproject.toml or any .toml config file, selecting between tomlkit and tomllib, modifying TOML while preserving comments and whitespace, implementing atomic config file updates, integrating TOML with Python dataclasses, handling TOML parse errors, or applying XDG base directory patterns for config file locations.

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Applies and teaches Python 3.11+ modernization patterns with PEP citations. Use when reviewing or writing Python code to apply built-in generics (PEP 585), pipe unions (PEP 604), walrus operator (PEP 572), match-case (PEP 634), Self type (PEP 673), exception notes (PEP 678), StrEnum, tomllib, pytest-mock fixtures, Typer Annotated syntax, or Rich terminal output — or when refactoring legacy typing imports or elif chains to modern equivalents.

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Validates and corrects Python shebangs and PEP 723 inline script metadata by applying four shebang-selection rules. Use when auditing or fixing shebangs in Python files — choosing between plain python3 and the uv shebang for standalone scripts with external dependencies, adding or removing PEP 723 metadata blocks to match actual import requirements, checking execute bit presence, or avoiding redundant transitive dependencies when typer is declared.

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Auto-selects and enforces the strongest valid Python typing lane for the detected Python version and dependencies — no user input required. Use when adding or tightening type annotations, eliminating Any usage in internal code, designing boundary validators or parsers, choosing between stdlib typing (TypedDict, Protocol, dataclasses), Pydantic models, or Hypothesis property tests, addressing ty or mypy failures, or applying version-specific features (TypeIs, ReadOnly, PEP 695 generics, PEP 649 deferred evaluation). Enforces boundary isolation — raw payloads validated immediately at ingress and returned as typed internal objects.

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Evaluate a single skill's quality against its stated purpose. Classifies the skill's purpose type, then scores applicable quality categories — universal dimensions always apply; structural dimensions (scripts, references, assets) are scored only when warranted by the skill's purpose. A focused 20-line behavioral skill with no bundled resources scores fully on all applicable categories. Use when auditing skill quality, checking marketplace readiness, evaluating skill completeness, performing pre-publication evaluation.

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