「implementation」の検索結果

Design and analysis of combinational and sequential digital circuits using Boolean algebra, truth tables, K-maps, CMOS gate implementation, flip-flops, registers, counters, and finite state machines. Covers De Morgan's theorems, canonical sum-of-products and product-of-sums forms, minimization techniques, propagation delay, setup and hold times, metastability, synchronous design discipline, and FSM encoding. Use when designing, minimizing, verifying, or debugging any circuit whose signals take only discrete logic values.

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Foundations of digital signal processing — sampling, aliasing, the Nyquist criterion, quantization, convolution, the discrete Fourier transform, FFT, FIR and IIR filter design, windowing, spectral leakage, and practical issues in fixed-point versus floating-point implementation. Use when designing filters, analyzing spectra, choosing sample rates, reasoning about aliasing, or implementing DSP algorithms on a microcontroller, DSP chip, or host CPU.

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Required reference for Prisma ORM 7 SQL driver adapter work. Use when implementing or modifying adapters, adding database drivers, or touching SqlDriverAdapter, Transaction, savepoint, result mapping, or DriverAdapterError behavior. Covers current transaction lifecycle, optional savepoint hooks, original database-error preservation, and verification.

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Invoke the rubber duck reviewer for a second opinion. Use after planning, after complex multi-file implementations, or after writing tests. Produces a focused CRITICAL/WARN/INFO list and a APPROVE/REQUEST_CHANGES verdict.

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Drive development with a red-green-refactor TDD cycle, ensuring tests are written before implementation. Use when implementing any new functionality.

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Audit a MaaFramework/Maa-series project's Git history to learn how Pipeline JSON, interface options, Python AgentServer CustomAction code, and related data tables evolved. Use when asked to review a Maa project from initial commit through a target commit, map `action: Custom` pipeline nodes to `@AgentServer.custom_action(...)` implementations, find pipeline/custom/option patterns or breakages, and produce a report plus skill-improvement recommendations.

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Testing for timing side channels and constant-time implementation verification.

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BDD-Driven Mathematical Content Verification Skill Combines Behavior-Driven Development with mathematical formula extraction, verification, and transformation using: - Cucumber/Gherkin for specification - RSpec for implementation verification - mathpix-gem for LaTeX/mathematical content extraction - Pattern matching on syntax trees for formula validation Enables iterative discovery and verification of mathematical properties through executable specifications.

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Measure encoding/decoding throughput across Syrup implementations. This skill provides bandwidth benchmarks for comparing serialization performance.

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When the user wants to set up, improve, or audit analytics tracking and measurement. Also use when the user mentions "set up tracking," "GA4," "Google Analytics," "conversion tracking," "event tracking," "UTM parameters," "tag manager," "GTM," "analytics implementation," or "tracking plan." For A/B test measurement, see ab-test-setup.

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Guided workflow for building custom AI agents using the OpenHands Software Agent SDK. Use when you want to create a new agent through an interactive interview process that gathers requirements and generates implementation plans.

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When the user wants to plan, design, or implement an A/B test or experiment. Also use when the user mentions "A/B test," "split test," "experiment," "test this change," "variant copy," "multivariate test," or "hypothesis." For tracking implementation, see analytics-tracking.

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Atlas of Agent Client Protocol (ACP) clients, agents, and OCapN/CapTP-grounded actors. Use when picking a coding agent surface, threading ACP over CapTP, locating the local Zoad implementation, or navigating the Hermes-as-OCapN-actor family.

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Decide whether to adopt, adapt, or justify building a new script, tool, utility, library, module, dependency, or bespoke capability. Use in task planning, worker execution (including tooling discovered after planning), or code review when an implementation choice may duplicate an existing solution.

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Research a specific technology, library, system, protocol, or tool to answer a concrete technical question with source-traceable evidence. Use when current implementation details, version behavior, configuration, compatibility, or authoritative technical facts must be verified rather than recalled. Also use when explicitly asked to incorporate verified findings into an existing skill.

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Use when the primary outcome is authoring, rewriting, summarizing, or validating user-facing documentation without implementation comparison or required source attribution, including READMEs, tutorials, API docs, GitLab Markdown, source-faithful summaries, and audience-specific prose.

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Use when the primary outcome is comparing documentation claims with implementation evidence, synchronizing docs from verified changes, or freshness review, including drift, missing coverage, stale claims, and post-change updates.

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Executes a four-phase feature addition workflow (Discovery, Planning, TDD Implementation, Verification) for Python projects. Use when adding a new feature end-to-end — discovering project structure and integration points, drafting a feature spec with MoSCoW-prioritized requirements and BDD acceptance criteria, implementing via test-first TDD cycles, then verifying with ruff lint, ty type checks, and behavior-focused regression and contract coverage.

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Use when implementing a Python feature, adding CLI commands, writing pytest suites, reviewing Python code, debugging, or refactoring. The primary Python engineering workflow orchestrator — classifies the task and delegates through this plugin's own specialist agents (architect → implement → test → review), sized to the task. Delegates to python-cli-architect (implementation), python-pytest-architect (tests), code-reviewer (review), python-cli-design-spec (architecture). Triggers on any Python task requiring specialist agent coordination or multi-agent execution.

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Investigate pytest failures without treating tests or implementation as automatic authority. Use when diagnosing regressions, debugging test errors, or deciding whether to fix a test or code; trace the contract, product, fixture, and observation before choosing an evidence-backed correction.

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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 a Python task explicitly requires test-driven development, tests-first implementation, or a red-green-refactor workflow.

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Shared Python 3.11+ development rules — type safety and the boundary policy for `Any` (ty, native generics, Protocol, TypeIs, Pydantic), layered architecture and SOLID, error handling, security, performance, identifier naming, PEP 723 script dependencies, Rich/Typer output, tooling defaults (uv, ruff, ty, hatchling, pytest), and testing requirements (behavioral coverage, TDD). Activates when any Python skill or agent needs the shared rules for implementation, code review, refactoring, or test authoring.

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Add automated documentation updater to any Claude skill. Creates a Python sync script that downloads upstream docs, processes markdown for AI consumption, and maintains local cache with configurable refresh. Collects template variables, then delegates implementation through 5-phase workflow. Use when adding auto-updating reference documentation to plugins or skills.

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