「behavior」の検索結果

Builds quality into operations — defining standards, catching defects at the right point, root cause analysis, and continuous improvement. Use this to reduce a defect or error rate, design quality controls into a process, run a root cause analysis, respond to a customer quality complaint, or set up quality metrics that drive behavior.

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Establishes whether the business makes money on each customer or unit — contribution margin, acquisition cost, payback period, lifetime value, and the cohort behavior underneath. Use this to assess whether growth is profitable, evaluate a channel or segment, support a pricing decision, judge how fast the business can afford to grow, or diagnose why revenue growth is not producing profit.

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Governs models and AI systems in production — intended use, evaluation, monitoring, human oversight, documentation, and the decision to deploy or retire. Use this before deploying a model or AI feature, when defining evaluation criteria, when a model's behavior has drifted, when assessing AI risk or regulatory exposure, or when deciding whether an AI system is fit for a consequential decision.

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Establishes what something actually costs — fixed, variable and mixed cost behavior, absorption versus variable costing, job-order, process and activity-based methods, breakeven and operating leverage, and price/volume/mix decomposition. Use this to set a price floor, explain why revenue or margin moved, decide make versus buy, judge whether a product line earns its place, or work out how an allocation basis is distorting a reported margin.

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You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.

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Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes

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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.

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Use for 'why does X work this way', 'why we picked Y', design rationale, regressions, postmortems, or data-backed thresholds. Discovers available MCPs and queries each evidence category (source control, issue tracker, long-form docs, real-time chat, infrastructure observability, error tracking, product analytics warehouse) in parallel, then returns a cited read on decisions and tradeoffs. Use how for runtime behavior.

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Prepare PRs for review by cleaning noisy history, improving PR descriptions, and adding reviewer guidance without changing code behavior. Use for "make this easy to review", "tidy this PR", "clean up commits", or "annotate the diff".

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Apply when you write, change, or keep a test. Call the code as its users do and assert the exact result they see. Rewrite or delete a test that would pass if every imported function returned undefined.

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Generate a project-local verification skill that drives your app the way a user does — any language, framework, or platform. Use for /create-verification-skill, "make a control skill for this repo", "make a driver skill for this repo", or when a project has no scripted way to prove UI/CLI/service behavior.

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Draft and submit evidence-backed XQUIC GitHub bug issues in the repository's CONTRIBUTING and issue-template format. Use when preparing or opening an alibaba/xquic issue that must identify an exact RFC or draft mechanism, expected-versus-actual behavior, reproduction evidence, and optional verified source root cause; requires issue-check to return true before any GitHub submission.

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Enforce XQUIC test coverage and local validation gates. Use when code, build, or test changes need verification; before commit or pull request; or when asked to build, test, validate, or verify XQUIC. Requires paired happy-path and abnormal-path unit tests, the complete local unit suite, and explicit endpoint coverage or gap reporting for production behavior changes.

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Verify whether a proposed XQUIC bug issue is real by checking the cited RFC or Internet-Draft mechanism against the repository's actual source behavior, tracing any root cause to current file and line evidence, and returning a fail-closed boolean gate. Use when validating an issue draft, investigating a claimed QUIC, HTTP/3, QPACK, or MoQT protocol violation, or before issue-submit may publish an issue.

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Use when evaluating Bitcoin and crypto cycles through a Willy Woo-style on-chain lens: holder behavior, realized value, supply dynamics, and on-chain demand.

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Findings-first code review workflow for AI coding agents. Use when the user asks to review uncommitted changes, commits in a date range, or a branch compared to the main branch / PR-style diff. Focuses on bugs, regressions, correctness risks, missing tests, security/data-safety issues, and other behavior-changing defects.

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Teaches evidence-based analysis of transaction logs and traces. Use when reconstructing transaction behavior from tables, TSV/CSV files, timestamps, counters, object histories, validation records, abort records, retries, partial logs, or when producing classified transaction outputs from trace evidence.

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Migrate Hibernate 5 to Hibernate 6 with Spring Boot 3. Use when fixing HQL/JPQL query parsing issues, removing deprecated Criteria API, updating ID generation strategies, or diagnosing N+1 query behavior changes. Covers breaking changes, type mappings, and performance monitoring.

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Exact detection thresholds for identifying malicious network patterns including port scans, DoS attacks, and beaconing behavior.

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Diagnostic guide for RL-based post-training of language models (GRPO, PPO, REINFORCE, DPO). Use proactively when debugging a training pipeline that shows no improvement, loss anomalies, reward stagnation, NaN gradients, or other unexpected behavior during reinforcement learning fine-tuning. Work through all pipeline stages — reward, advantages, log-probs, loss, generation/decoding — before concluding the diagnosis is complete; stopping after one or two fixes is a common failure mode. Covers log-probability math, advantage estimation, numerical stability, reward processing, and generation/decoding pipeline issues.

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Implement paper-defined attention variants by extracting mechanism invariants, mapping tensor shapes, preserving module interfaces, validating numerical behavior, and integrating the custom attention into an existing transformer stack.

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Constitutional AI and safety guardrail prompts for aligned LLM behavior

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Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes. Requires root cause investigation first.

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 when starting any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.

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。