「behavior」の検索結果

AI operational modes (brainstorm, implement, debug, review, teach, ship, orchestrate). Use to adapt behavior based on task type.

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。

Provide advisory guidance for the Adobe Dispatcher Apache HTTP Server module and related HTTPD configuration in AEMaaCS cloud workflows only, with public-doc citations and cloud-specific MCP verification plans. Use for conceptual questions such as `statfileslevel`, filter rules, URL decomposition, cache invalidation behavior, rewrite behavior, and security headers.

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 a "product description" repo for a software product — a set of prose documents describing, from the outside in, what the user sees, what they can do, and exactly what happens when they do it, written from the code and tests, then verified against the running product and triaged into a bug list. Works for any product with a user (canvas editors, web apps, CLIs, chat products, mobile apps). Use when the user asks to "write a product description for X", "describe the user experience of X", "document how X behaves for the user", "make a behavior-spec repo", or wants a feature-by-feature, event-by-event account of an app's behavior rather than API docs. Also use to resume or extend an existing product description repo.

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。

AI operational modes (brainstorm, implement, debug, review, teach, ship, orchestrate). Use to adapt behavior based on task type.

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。

End-to-end Coval adversarial / red-team testing workflow. Builds one adversarial test set (12 core attack vectors plus legitimate controls, each with an expected-behavior checklist), creates a persistent "Adversarial User" persona and a Composite Evaluation metric that scores each scenario against its own expected behaviors, launches a multi-iteration run against the agent (voice or chat), polls for completion, builds a per-scenario pass/fail scorecard, and creates a saved report grouped by Test Case. Use when a user wants to follow the Adversarial & Red-Team Testing cookbook (https://docs.coval.dev/guides/adversarial-red-team-testing) without doing each step by hand. Triggers: "adversarial test set", "red team my agent", "jailbreak / prompt-injection testing", "test my agent against bad actors".

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。

AI operational modes (brainstorm, implement, debug, review, teach, ship, orchestrate). Use to adapt behavior based on task type.

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。

86 peer-reviewed behavioral frameworks applied to every Maxim output. The flagship moat.

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。

Apply a named behavioral science framework to any task — persuasion, nudge design, habit formation, conversion, influence, negotiation, cognitive bias, choice architecture, decision framing. The framework backbone every other Maxim skill loops into for psychological validation.

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 insider data exfiltration by analyzing DLP policy violations, file access patterns, upload volume anomalies, and off-hours activity in endpoint and cloud logs. Uses pandas for behavioral analytics and statistical baselines. Use when investigating insider threats or building user behavior analytics for data loss prevention.

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。

Detect insider threat behavioral indicators including unusual data access, off-hours activity, mass file downloads, privilege abuse, and resignation-correlated data theft.

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。

Deploy AI and NLP-powered detection systems to identify business email compromise attacks by analyzing writing style, behavioral patterns, and contextual anomalies that evade traditional rule-based filters.

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。

This skill covers deploying anomaly detection systems for industrial control environments using machine learning models trained on OT network baselines, physics-based process models, and behavioral analysis of industrial protocol communications. It addresses building normal behavior profiles for SCADA polling patterns, detecting deviations in Modbus/DNP3/OPC UA traffic, identifying rogue devices, and correlating network anomalies with physical process data from historians.

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。

Detect sandbox evasion techniques in malware samples by analyzing timing checks, VM artifact queries, user interaction detection, and sleep inflation patterns from Cuckoo/AnyRun behavioral reports

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。

Provide advisory guidance for the Adobe Dispatcher Apache HTTP Server module and related HTTPD configuration in AEMaaCS cloud workflows only, with public-doc citations and cloud-specific MCP verification plans. Use for conceptual questions such as `statfileslevel`, filter rules, URL decomposition, cache invalidation behavior, rewrite behavior, and security headers.

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。

Run a bounded red-green-refactor feedback loop only when the user explicitly requests TDD, accepts reproduction-first proof for a bug, or an accepted high-risk behavior specifically requires red/green evidence; do not trigger for every logic, bug, behavior, or implementation change.

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。

Simplify a bounded code area when the user explicitly requests behavior-preserving refactoring or a named complexity/readability problem is the accepted task; do not trigger automatically after implementation, during every review, for adjacent cleanup, or when behavior may change.

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。

Diagnose user-reported AILI/ROSE harness or workflow behavior problems without editing files; use when the user says a command, skill trigger, subagent packet, memory flow, installer, docs, or agent prompt behavior is wrong and wants to know where the issue lives and how to fix it. Do not use for normal product-code bugs; return any approved modification need to ROSE.

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。

Hunt shadow / zombie / undocumented API surface (OWASP API9 Improper Inventory Management) — enumerate the full API version history (v1/v2/beta/legacy paths, header- and subdomain-based versioning), pull and diff every reachable OpenAPI/Swagger spec (including ones only findable via the Wayback Machine), and behaviorally diff old vs. current versions for auth/rate-limit/validation regressions rather than just response-shape differences. Distinct from hunt-api-misconfig, which owns exploitation once you have a spec or endpoint (mass assignment, JWT, OData, Swagger-chain attacks); distinct from hunt-subdomain, which owns host-level discovery. This skill owns the version-inventory and behavioral-diff workflow itself. Use when the target has versioned API paths, multiple specs, a changelog referencing deprecated endpoints, or a mobile app whose hardcoded backend calls look older than the current web app'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。

AI operational modes (brainstorm, implement, debug, review, teach, ship, orchestrate). Use to adapt behavior based on task type.

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。

Design RL simulation sandboxes, trajectory datasets, QLoRA/LoRA adaptation plans, eval harnesses, and rollback unhooks for agents learning specific coding behaviors beyond the base model. Use when building or reviewing tool-use training loops, offline trajectories, reward functions, self-improvement harnesses, or Port Daddy agent behavior curricula. NOT for generic ML tutorials, full model training operations, or production deployment without eval gates and safety unhooks.

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。

Slackの認証やメッセージ操作、OpenClawの受信・返信を検証するスキル。APIで確かめた結果と実際の画面操作を区別し、証拠と後片付けを確認します。

• Slackの認証やアクセス権の確認
• メッセージの作成・編集・削除テスト
• OpenClawの受信・返信の検証

OpenClawのリリース候補に対するCIを実行・監視し、実際のプロバイダー接続やインストール・更新を検証して、失敗の原因と結果を整理するスキルです。

• OpenClawのリリース候補を検証したいとき
• 失敗したCIジョブの調査と再実行
• プロバイダーの認証と推論利用を確認

OpenClawの変更内容に合うテストやチェックを選び、失敗原因を調べるスキル。ローカル検証から環境依存の確認、リリース検証の担当手順への振り分けまで扱います。

• ソース変更に合う検証を選びたいとき
• 不具合の再現と修正確認
• CIの失敗原因を調べたいとき

新規・既存テストが守る動作と不具合を確かめ、重複や実装への依存を見直すスキル。削除の根拠を整理し、必要な検証を残してテスト用コードも整理します。

• 新規テストの必要性を確かめたいとき
• 重複や実装依存のテストの点検
• テスト専用の公開コードの見直し