「boundaries」の検索結果

Analyzing patterns of continuity and change over time in historical contexts. Covers periodization (how and why we divide history into eras), identifying turning points, measuring rates of change, and recognizing long-term patterns and trends. Use when analyzing temporal structure in history, debating period boundaries, or assessing whether a moment represents genuine transformation or surface-level disruption.

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Service-oriented and microservices architecture patterns for cloud-scale systems. Covers service boundaries, API versioning and backward compatibility, eventual consistency at the API layer, idempotency keys, saga and choreography patterns, bulkheads, circuit breakers, service meshes, and the Vogels "you build it, you run it" operating principle. Use when designing service decomposition, API contracts, inter-service communication, or reviewing a proposed microservices architecture for coupling and resilience.

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Brain-Computer Interface with colored operad security boundaries. Maximally isolated EEG/neural data processing via VM→Container→Process enclosure with GF(3) conservation for Agent-O-Rama neural pathways.

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Runs structured Python cleanup and modernization — static analysis via prek/ruff, smell investigation to root cause, typed-boundary hardening by inventorying Any usage, and modernization within the project's requires-python lane. Use when refactoring Python code, removing dead code, hardening type boundaries, or running a modernization pass on a file or scope.

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Runs deterministic Python quality checks against a path or scope — formatting, linting, type checking, and typing-boundary policy. Use when checking or fixing code quality via prek, ruff, ty, pytest, or the check-typing-boundaries policy script. Reports results grouped by category; fixes only when explicitly requested.

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Review pytest test effectiveness against authoritative contracts, relevant faults, behavior-preserving refactors, realistic boundaries, isolation, and diagnostics. Use for test-quality audits, coverage-gap reviews, regression-test changes, or mocking reviews; preserve existing project gates rather than inventing coverage targets.

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Specialist skill for Python data engineering — pandas, polars, DuckDB, numpy, ETL pipelines, tabular data ingestion, and notebook-to-module extraction. Use when working with dataframes, data validation at ingress boundaries, merge/join operations, typed column contracts, or choosing between pandas vs polars vs DuckDB for a data task.

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Guides pytest test architecture for Python 3.11+ using behavioral contracts, risk, and maintenance-adjusted value. Use when choosing unit/integration/property/e2e boundaries, fixture strategy, coverage measurement, or mutation testing. Preserves existing project gates but does not invent test-pyramid ratios, coverage percentages, mutation-score targets, or tests for implementation detail.

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Use when investigating failing tests or deciding whether to change the test or product. Validate test expectations against authoritative intent, trace product and test boundaries, and require evidence for the correction rather than automatic code fixes or test dismissal.

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Use for architecture review, dependency graphs, coupling analysis, or circular dependency detection. Establishes project module namespaces and traces imports plus Claude plugin hook, skill, script, PEP 723, and MCP boundaries. Emits partitioned Mermaid graphs with cycle, coupling, and ownership signals; verifies contract and consequence before assigning severity. Registers diagrams as codebase-analysis artifacts.

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Reviews LLM integration, prompt templates, model selection, token/cost budgets, evaluation harnesses, structured outputs, retries, and streaming. Checks trust boundaries and task evidence against the selected provider/model contract rather than prescribing universal model tiers or sampling settings.

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Reviews Python boundaries, typing, error handling, tests, dependencies, and supported-version idioms against the target project's contracts and active tools. Loaded by dh:code-reviewer for Python source; uses Python-engineering standards when available and retains standalone checks otherwise.

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Creative reasoning framework for ill-defined problems where conventional solutions are suboptimal. Use when the problem has ambiguous goals, vast solution space, no single correct answer, or requires innovation. Implements three paradigms — combinational (novel combinations of familiar ideas), exploratory (expand solution space boundaries), transformative (alter fundamental constraints). Do not use for well-defined problems, mathematical puzzles, or tasks requiring convergent reasoning.

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Design, create, update, and verify Vapi Squads and documented handoff tools through the public API. Use for choosing a single assistant versus a multi-assistant Squad, persistent or transient members, entry-member ordering, specialization boundaries, context engineering, variable extraction, model-specific handoff patterns, assistant-version pins, and safe Squad updates.

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Operate long-lived agent workloads with observability, security boundaries, and lifecycle management.

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Retrieve any YYLO Ledger Record by ID, discover PDRs and artifacts, and manage tasks and dependencies. Use for unknown record types, universal get/search, task operations, and source-of-truth boundaries.

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Knowledge Extraction — captures and routes knowledge at work boundaries. Use when you discover a pattern, make a decision, find a distinction, or complete a sub-task.

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Create/update .agent/constitution.md. Use when commands/boundaries/constraints must be confirmed before baseline or code changes. Draft v0 from repo evidence, then interview user.

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Initialize or improve AGENTS.md files that define how coding agents operate in a repo. Use when asked to set up or replace an agent init command (Codex, Claude), standardize multi-agent behavior, or audit an existing AGENTS.md for clarity, commands, boundaries, and repo-specific context. For Claude Code, also create CLAUDE.md as a symlink to AGENTS.md.

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Use this skill when architecting new Claude Code agents following Anthropic best practices. Guides through YAML frontmatter creation, agent structure definition, role boundaries, and validation. Critical for creating minimal agent files (~150 lines) with proper skill invocations. Examples: <example>Context: User wants to create a new agent for security auditing user: "Create a security-auditor agent" assistant: "I'll use the agent-creator skill to build this agent following Anthropic best practices" <commentary>New agent creation requires the agent-creator skill to ensure proper structure and YAML frontmatter.</commentary></example> <example>Context: Existing agent file is too verbose and needs refactoring user: "The architect agent is 2000 lines, refactor it to minimal structure" assistant: "I'll use the agent-creator skill to refactor this into a minimal ~150 line agent with a separate skill file" <commentary>Agent refactoring requires the agent-creator skill to separate identity from technical procedures.</commentary></example>

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Roll out governed MotherDuck analytics to internal teams, choosing trusted datasets, access boundaries, and owners.

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Build ingestion-to-serving pipelines on MotherDuck, including stage boundaries, transformations, and publication.

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Review touched subsystems for layering, crate boundaries, single-source-of-truth rules, registry-driven behavior, and compiler/runtime separation. Use when a broad review needs a dedicated architecture pass or when the user asks for architectural review specifically.

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Apply AuthCrunch coding contracts when creating, modifying, or reviewing repository code. Covers package ownership, dedicated configuration parsers, public APIs, validation, errors, logging, serialization, and security boundaries, and routes to the owning implementation skill.

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