「behavior」の検索結果

Develop and test Screenpipe Windows-native changes on a disposable Azure VM created from the prepared Screenpipe Windows dev image. Use for Windows compiler, process, service, local API, desktop, capture, installer, or permission behavior. Do not use for React-only work proved by the browser mock.

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。

Expert guidance for proactive threat hunting in Google SecOps. Use when proactively hunting for threats, retroactively analyzing indicators of compromise (IoCs), performing prevalence searches across enterprise events, hunting for MITRE ATT&CK techniques, or detecting behavioral and statistical outliers using UDM queries. Don't use for incoming alert triage (use secops-triage), active incident response and timeline deep-dives on a known breach (use secops-investigate), or detection rule authoring (use secops-detection-engineering).

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。

Automates the end-to-end detection engineering workflow in Google SecOps using MCP tools. Use when fetching threat intelligence from blogs, generating Threat Detection Opportunities (TDOs), simulating attacker behavior with synthetic UDM events, evaluating rule coverage, and deploying gap-closing rules. Don't use for standalone YARA-L 2.0 rule authoring/tuning (use secops-detection-engineering), threat hunting, or SOC investigation.

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Generate standard unit tests for one or more target classes in Apache ShardingSphere; cover requested behavior and every affected SUT-owned branch, enforce an explicitly requested numeric coverage target, and pass quality gates; perform explicit merge analysis, suitability filtering, and refactor optimization for parameterized tests.

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Implement, fix, refactor, or remove repository code under required scope, non-regression, verification, and review gates. Use whenever a task may change a production, test, script, or other implementation artifact, including build logic, generated source, and behavior-affecting configuration. Also use it alongside a specialized code-changing Skill. Do not use for read-only analysis or review, diagnosis-only work, or prose-only changes.

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Use for every Kortix test task, behavior change, bug fix, refactor, API route change, CLI change, SDK change, browser journey, test failure, coverage question, local benchmark, or testing infrastructure change. Enforce the single local-first runner, black-box flow contracts, package-local SDK tests, browser-only Playwright tests, and real input/output verification.

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。

Pi-compatible PUA high-agency governance skill. Use for explicit PUA/PIP requests, repeated failures, passive/giving-up behavior, user frustration, or unverified completion. Pair with /pua-on extension command when persistent pressure is desired.

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。

Trae-compatible PUA high-agency governance skill. Use only for explicit PUA requests, repeated failures, user frustration, giving-up/passive behavior, or unverified completion. Do not trigger for normal first-attempt tasks.

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。

PUA Shot — compact all-in-one PUA reference for explicit injection into sub-agents or short sessions. Strong flavor, same evidence-first behavior.

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。

Trae-optimized PUA high-agency governance skill for npx skills installs. Use only for explicit PUA requests, repeated failures, user frustration, giving-up/passive behavior, or unverified completion. Do not trigger for normal first-attempt tasks.

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。

Use for PUA/try-harder productivity coaching when the user expresses frustration, repeated failure, quality complaint, passive behavior, says retry/change approach/don't give up, asks for evidence/completion check/test before done, or wants Ding-style workplace reminders. Triggers include: try harder, stop giving up, figure it out, again??, why still failing, change approach, no evidence, run tests, done without proof, 换个方法, 再试试, 别摆烂, 别偷懒, 为什么还不行, 又错了, 证据呢, 没跑测试别说完成, 验收, 闭环, 自嗨, 置身钉外, 无招, 老板体感. Do not use for calm first-attempt requests.

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。

Trae-compatible English PUA/PIP high-agency governance skill. Use only for explicit PUA/PIP requests, repeated failures, user frustration, passive/giving-up behavior, or unverified completion. Not for normal first-attempt tasks.

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。

Performance-coaching mode for repeated failures, passive behavior, completion-quality issues, or explicit try-harder requests. Uses structured troubleshooting and evidence-first delivery habits.

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。

Structural analysis of complex systems — Iceberg model, Causal Loop feedback diagrams, archetype matching, Meadows leverage points, and concept maps — grounded in the premise that behavior is generated by structure. USE WHEN systems thinking, causal loop, feedback loops, archetypes, leverage points, iceberg model, fix the system, why does this keep happening, recurring problem, second-order effects. NOT FOR incident causal chains (use RootCauseAnalysis).

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。

DeepEval evaluation workflow for AI agents and LLM applications. TRIGGER when the user wants to evaluate or improve an AI agent, tool-using workflow, multi-turn chatbot, RAG pipeline, or LLM app; add evals; generate datasets or goldens; use deepeval generate; use deepeval test run; send results to Confident AI; monitor production; run online evals; inspect traces; or iterate on prompts, tools, retrieval, or agent behavior from eval failures. AI agents are the primary use case. Covers Python SDK, pytest eval suites, CLI generation, traced evals, Confident AI reporting, and agent-driven improvement loops. DO NOT TRIGGER for unrelated generic pytest, non-AI test setup, or non-DeepEval observability work unless the user asks to compare or migrate to DeepEval; for instrumenting an app with DeepEval tracing, @observe, or framework integrations (use the `deepeval-tracing` skill); or for raw OpenTelemetry / OTLP export without the deepeval package (use the `deepeval-otel` skill).

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。

Instrument an AI application with DeepEval's native tracing so its behavior is visible in Confident AI. TRIGGER when the user wants to add DeepEval tracing or @observe to an LLM app, agent, RAG pipeline, or chatbot; wire a framework, model-provider, or vector-database integration (LangGraph, LangChain, OpenAI Agents, LlamaIndex, Pydantic AI, CrewAI, and others); choose between a native integration and manual instrumentation; set span types, tags, or metadata; or send DeepEval-SDK traces to Confident AI's Observatory. DO NOT TRIGGER for building DeepEval pytest eval suites, datasets, goldens, metrics, or deepeval test run (use the `deepeval` skill), or for raw OpenTelemetry / OTLP export without the deepeval package (use the `deepeval-otel` skill). This skill is purely DeepEval-SDK instrumentation — producing well-formed traces, not running evals.

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。

Technical interview preparation expert for algorithms, system design, and behavioral questions

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。

Find and implement evidence-backed simplifications in the ego-lite repository. Use when reviewing the codebase for dead code, duplicated state or APIs, speculative abstractions, unnecessary compatibility layers, hand-written infrastructure, excessive tests or documentation, or when the user asks to reduce code size or maintenance complexity without hiding behavior changes.

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。

Operate FrontendExtension (FE) resources in frontend-forge: create, update, rebuild, inspect package artifacts, download packages, publish, unpublish, delete, and debug package/publish controller behavior. Use this skill whenever the user mentions FE operations, FrontendExtension lifecycle, extension package/download/publish/unpublish, artifact ConfigMaps, package Jobs, publisher Jobs, publish target ConfigMaps or Secrets, rebuild-token, package-state/publish-state labels, or troubleshooting FE status in a Kubernetes cluster.

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。

Analyzes meeting transcripts and recordings to uncover behavioral patterns, communication insights, and actionable feedback. Identifies when you avoid conflict, use filler words, dominate conversations, or miss opportunities to listen. Perfect for professionals seeking to improve their communication and leadership skills.

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Debug LangChain and LangGraph agents by fetching execution traces from LangSmith Studio. Use when debugging agent behavior, investigating errors, analyzing tool calls, checking memory operations, or examining agent performance. Automatically fetches recent traces and analyzes execution patterns. Requires langsmith-fetch CLI installed.

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Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.

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。

Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.

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。

Draft or update a GitHub PR description that explains the problem, changed behavior, and review concerns in plain language.

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。