「ara」の検索結果

Generate pytest-based unit tests for Python code. Creates test files following pytest conventions with proper fixtures, mocking, and parametrization.

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。

Auto-activates when synthesizing outputs from multiple parallel sub-agents to create cohesive, integrated design with consistency checking and conflict resolution.

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。

Analyze an existing codebase in parallel via 7 specialized mapper subagents. Outputs structured findings to docs/codebase/ for persistent cross-session context. Use when onboarding to a brownfield project or when reactive on-demand file reading is too slow.

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 bugs with unknown root cause. Spawns fresh-context investigation subagent + parallel /review solve companion, persists hypotheses across iterations, hands off to /fix when root cause found.

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。

Detects version drift between machine-readable law YAML files and the binding geldende wettekst on wetten.overheid.nl at the YAML's claimed valid_from. Catches the silent failure where authored content lags behind ingevoerde wijzigingen — a YAML dated 2025-01-01 still carrying pre-Stb.-X text. Activate as Step 0 of every regelrecht-stelselanalyse micro-cycle, before any YAML edit. Strict by design: no bypass, no normalization beyond whitespace within a single paragraph; the geldende wettekst is leading and is mirrored verbatim, including errata. Use proactively when starting a stelselanalyse cycle, before harvest/migrate/extend, or when user mentions "drift", "versie", "geldende tekst", or worries that a YAML may be stale relative to wetten.overheid.nl.

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。

Builds and audits a concept-ontology for a regelrecht corpus to decide and verify reference types — cross-law source-binding vs intra-law source-binding vs open-norm leaf vs external-fact leaf — and to catch concept conflation across laws. Core principle: a value's reference type is fixed by WHO derives it, and a binding is sound only when producer and consumer denote the SAME concept; the lexical surface (the word in the text) is not the concept. Detects false friends (one term, different concepts — e.g. a register-based vs a factual "naar de omstandigheden"-based residence/ingezetene concept), orphan plain-params (a value a regulation actually derives, modeled as a brute input), negation/complement re-entry (¬X modeled as an independent leaf next to X), and duplication (one concept re-entered as several independent leafs, losing the single-source-of-truth invariant). Produces a law-level glossary and a stelsel-level concept map with sound same-concept binding edges and flagged lexical-match-but-concept-mismatch edges. Use when deciding whether a value should be cross-law/intra-law/leaf, when the same term means different things across laws, or when a binding might silently conflate concepts.

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。

Genereert uit een (semi-)gevalideerd regelrecht-corpus een geloofwaardige service-PoC (burger- + behandelaar-portaal, of een headless beslis-service) en zet die in als validatie-instrument met uitvoeringsexperts — om tastbaar te maken wat nodig is om een wet rechtvaardig in de praktijk te brengen, gezien vanuit de betrokkene. Gebruik dit als workshop-fase ná de logica-/scenariovalidatie (audit-products) om de dienstverlening-kant te valideren: welke service impliceert de wet, waar zit menselijk oordeel, welke last ligt bij de burger, welke termijnen, waar is de wet hard of onduidelijk. Dossier-agnostisch; de regelrecht-methode (hooks, legal_character, untranslatables, source/implements, type_spec, receipts) is de vaste taal. Voor de logica-/scenariovalidatie zelf: zie de zusterskill regelrecht-audit-products.

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。

Maakt regelrecht-scenario's vindbaar en hun norm-ketens leesbaar/geassert. Aan de invoerkant — vertaal een platte lijst leaf-parameters naar benoemde casus-assen + een herbruikbare persona-bibliotheek, met een reverse-lookup casus-matrix. Aan de uitvoerkant — maak de norm-keten zichtbaar via keten-checkpoints (assert elke knoop op het kritieke pad, niet alleen de endpoint) en golden-trace-snapshots + branch-coverage. Gebruik dit bij het schrijven of opschonen van scenario-/feature-files, het terugvinden of ontwerpen van een specifieke casus in een grote variabelenlijst, het reviewen van engine-traces, of het regressie-bestendig maken van cross-law-ketens zodat een ketenfout niet stil achter een correcte endpoint verdwijnt. Dossier-agnostisch; de regelrecht-methode is de vaste taal. Haakt aan op regelrecht-stelselanalyse (engine-tests / meta-check) en regelrecht-audit-products (testcase-scenario's).

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。

Audits whether a machine-readable regelrecht law model is faithful to the LETTER of the wettekst — and strictly separates the wettekst (leading) from the toelichting (Nota/Memorie van Toelichting; explanatory, not a norm). Runs in critical passes per article/lid and detects four deviation classes: toelichting-bleed (a modeled criterion that the letter only states as an open norm, or not at all), missing verbatim elements (a word/condition such as a "tenzij" or a qualifier dropped from the model), wrong legal_basis anchoring (an endpoint hung on a "kapstok"-article while the operative norm lives elsewhere), and verbatim-drift in text: (an ingetrokken/older redaction). Also detects positief-lid vs uitsluitings-lid conflicts and checks the chapeau for an explicit derogation rule ("zo nodig in afwijking van het eerste lid") before judging precedence. Classifies every finding as modelfout (fix), wettekst-gevolg (report, do not fix), or letter-vs-toelichting-question (jurist decides what is leading + revision signal). Use when the user wants to know if a model is "dicht bij de letter", mentions letter vs toelichting/bedoeling, fidelity, "natuurgetrouw", or before trusting feature outcomes.

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。

Idiomatic Go concurrency - sync primitives, channel semantics, the select statement, and the standard channel patterns (cancellation/done-channel, fan-out/fan-in, pipeline, or-done, context, worker-pool). Use when writing or reviewing Go that spawns goroutines, shares state, or coordinates over channels, even if the user never says "concurrency" - e.g. data races, deadlocks, goroutine leaks, sync/context/select usage, or "make this parallel". Distilled from luk4z7/go-concurrency-guide.

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Run a seven-agent adversarial pipeline against a resume and cover letter before submission. Produces channel-conditional verdicts anchored to the realistic applicant pool, separates materials quality from submission expected value, and ends with a contrarian pass that can overturn the verdict.

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。

Creating algorithmic art using p5.js with seeded randomness and interactive parameter exploration. Use this when users request creating art using code, generative art, algorithmic art, flow fields, or particle systems. Create original algorithmic art rather than copying existing artists' work to avoid copyright violations.

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。

Analyze budget-versus-actual or forecast-versus-actual performance and turn finance data into a management-ready narrative using variance logic, drivers, cash impact, and forward actions. Use when finance, FP&A, or business leaders need commentary, management accounts, or performance explanation. Do not use for bookkeeping entries, tax filing preparation, or legal accounting sign-off.

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Create a concise daily briefing from calendar items, tasks, messages, and open decisions. Use at the start of a workday to identify priorities, risks, preparation needs, and follow-ups.

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Turn raw budget-versus-actual data into plain-language explanations for non-finance managers. Focus on why numbers moved, what it means for the team, and what action to take — without requiring accounting knowledge. Use when managers need to understand their cost center performance. Do not use for technical accounting analysis, tax preparation, or external audit deliverables.

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Natural-language orchestration of security tools through Gemini. Translates intent ("scan this scope for exposed admin panels") into safe, parameterized tool pipelines using nmap, masscan, naabu, httpx, nuclei, ffuf, gobuster, subfinder, amass, dnsx, katana, gau, semgrep, trivy, checkov, gitleaks, syft, grype, and custom scripts. Use when the user wants Gemini to drive a chain of CLI tools end to end with guardrails.

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Malware reverse engineering and suspicious artifact analysis skill for defensive triage, static analysis, dynamic analysis planning, unpacking strategy, indicators of compromise, behavior summaries, YARA/Sigma ideas, and remediation guidance. Use for suspicious binaries, scripts, documents, logs, memory artifacts, sandbox reports, and malware family analysis in isolated environments.

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当面对 2 个以上可以独立进行、无共享状态或顺序依赖的任务时使用

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Use this agent when you need to perform custom bioinformatics analyses on genes or proteins that go beyond standard database lookups. This includes situations where existing domain/family annotations from UniProt are insufficient, when you need to verify dubious annotations, or when you need to perform comparative analyses across gene sets. The agent creates reproducible analysis pipelines with proper dependency management and documentation.

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Orchestrates implementation of all tasks for a feature using an agent team. Respects task dependencies, runs tasks in correct order, parallelizes independent tasks. Does NOT build, run tests, or commit at any point — verification is delegated to the caller (typically `/start-job`, which runs `build-verify` and `pr-review uncommitted` afterwards). Use when the user says "implement all tasks", "run the task sequence", "implement the feature", or wants to execute multiple tasks from a task list end-to-end.

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。

Top-level orchestrator for Flutter feature specification. Runs the full uninterrupted pipeline: prd → techspec → tasks in one shot. Use when the user says "spec a feature", "write a spec for", "start feature spec", "plan a feature", or wants to go from idea to implementation-ready tasks without typing three separate commands.

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。

Build/test/lint verification pass for this Flutter app — runs codegen, analyze, test, then iOS + Android native builds in parallel, then `dart format`. Does NOT commit anything; leaves the working tree dirty so the user can review and commit on demand. Used at the end of the prd → techspec → tasks → implement-tasks-sequence flow before `pr-review`, and can also be invoked manually when the user says "verify build", "run full verification", or "check everything".

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Activate for any work in the tests/e2e/ directory: creating or editing test files (tests/*.test.ts), page objects (pages/), helpers (helpers/), or vitest config. Enforces agent-browser conventions specific to this project.

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Operate a logged-in Miaoshou ERP workflow for TikTok Shop Malaysia product discovery, product screening, profit estimation, listing preparation, and reviewed publishing. Use when the user asks to search or select products in 妙手 ERP, evaluate low-price traffic products, prepare Malaysian TikTok Shop listings, move products into the TikTok collection box, or publish approved listings. Prefer browser operation through the user's existing login; never store credentials or call undocumented private endpoints.

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。