「test」の検索結果

Sign and verify container image provenance using Sigstore Cosign with keyless OIDC-based signing, attestations, and Kubernetes admission enforcement.

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 SOC 2 Type II audit preparation including gap assessment against AICPA Trust Services Criteria (CC1-CC9), evidence collection from cloud providers and identity systems, control testing validation, remediation tracking, and continuous compliance monitoring. Covers all five TSC categories (Security, Availability, Processing Integrity, Confidentiality, Privacy) with automated evidence gathering from AWS, Azure, GCP, Okta, GitHub, and Jira. Use when preparing for or maintaining SOC 2 Ty...

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。

Performs API inventory and discovery to identify all API endpoints in an organization's environment including documented, undocumented, shadow, zombie, and deprecated APIs. The tester uses passive traffic analysis, active scanning, DNS enumeration, JavaScript analysis, and cloud resource inventory to build a comprehensive API catalog. Maps to OWASP API9:2023 Improper Inventory Management. Activates for requests involving API discovery, shadow API Tespit, API inventory audit, or attack surf...

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。

Execute and Şunu test et: JWT none algorithm attack to bypass signature verification by manipulating the alg header field in JSON Web Tokens.

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。

Exploits JWT algorithm confusion vulnerabilities where the server's token verification library accepts the algorithm specified in the JWT header rather than enforcing a fixed algorithm. The tester manipulates the alg header to switch from RS256 to HS256 (using the RSA public key as the HMAC secret), sets alg to none to bypass signature verification, or exploits kid/jku/x5u header injection to supply attacker-controlled keys. Activates for requests involving JWT algorithm confusion, alg none a...

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。

Web race condition exploitation — TOCTOU, single-packet attack, limit overrun, parallel race testing

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 the local CI pipeline (ruff, bandit, pytest, sonar-scanner) and refresh SonarQube.

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 it is time to make the backend follow the Lean model — regenerate Kotlin from Lean, implement the generated interfaces (UseCase, QueryService, Repository, Entity), wire every generated contract test, make the build green, then run the design and SQL reviews. Backend comes after the human confirmed the screens.

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 the infrastructure design phase between domain exploration and Lean formalization — deriving non-functional requirements from the Lean model, deciding environments (test/local/prod), identity provider, the relational database's product, version and configuration (the storage kind is fixed to relational), ID representation, and the list of things NOT to build. Produces documents/infra-design with INFRA-D/A/Q entries.

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 after the Lean model changed and before touching backend code — regenerates Kotlin from Lean, lists changed generated symbols, the hand-written files and contract tests that reference them, and golden regressions, so the blast radius is known 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。

変更内容からミューテーション/E2Eの推奨度を判定し、強く推奨・範囲が小さい・副作用なしのものは確認なしで実施し、それ以外は最後の報告で確認する。quality-check Step 5から参照実行されるほか、開発中の任意タイミングで単体実行できる。

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

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。

実装前に呼ぶ。High/Medium リスク変更のテストオラクル（期待値の根拠）とファルシフィケーション項目を定義する。

Use when implementing any feature or bugfix, before writing implementation code

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 this skill whenever helping someone build, design, or troubleshoot layouts in Elementor Pro V4 using the visual editor. Triggers on ANY mention of Elementor, page builder, Flexbox widget, Div block, section layout, widget configuration, responsive breakpoints, dark mode, scroll animations, Interactions tab, Variables Manager, Class Manager, Custom Code snippets, or any visual design work inside the Elementor panel. Also trigger when someone asks how to position elements, set spacing, apply typography, configure dark mode, prevent FOUC, or build scroll-triggered effects in Elementor. ALWAYS use this skill before suggesting any Elementor V4 implementation — it contains critical V4-specific panel structure, common AI mistakes to avoid, and production-tested patterns.

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。

Outbound email-marketing desk (Busabase App-in-Skill) for building segments, drafting campaigns, newsletters, and sequences, running pre-send deliverability and subject-line QA, and approving every send before it is scheduled. Structured around the SEND discipline — Setup, Engage, Nurture, Deliver — with an email-quality-auditor gate (EQS score + SHIP/FIX/BLOCK verdict). Use when the user invokes $kelly-campaigns or /kelly-campaigns, or mentions email marketing, campaigns, newsletters, drip/welcome/win-back sequences, broadcasts, segments, subject-line A/B tests, deliverability (SPF/DKIM/DMARC/spam score/inbox placement), or wants to review and approve marketing email before it is sent. This is OUTBOUND marketing to a subscriber list, distinct from kelly-email inbox triage (incoming mail). 出站邮件营销：策划分群、起草营销活动 / 新闻邮件 / 邮件序列，发送前做可送达性与主题行质检，人工审批后再排期发送。

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。

Busabase App-in-Skill dashboard over a fixed, deterministic mock user-behavior funnel dataset (browse → search → compare → booking attempt/abandon → complete) for a generic consumer booking product. Use when the user invokes $kelly-behavior-predict or /kelly-behavior-predict, wants to review funnel drop-off, per-segment predicted next actions, or backtest a rule-based "predicted next action" heuristic against a mock historical sample. Fully deterministic mock data and a hand-recomputable rule — never a real ML/LLM model, never a live system.

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。

Review board (Busabase App-in-Skill) that runs a fixed suite of mock test cases against a baseline vs candidate agent version and surfaces rubric-scored regressions before a release. Use when the user invokes $kelly-agent-eval or /kelly-agent-eval, wants to review agent-version regressions, compare baseline vs candidate quality, triage a release, or record a release approve/block decision. Deterministic mock rubric scores only — not a real LLM-judge call, and it never deploys anything.

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。

Finish an existing Japanese business talking-head, lecture, interview, or testimonial rough cut as a credibility-first vertical social video. Use when the user wants an SNS short that preserves the speaker's meaning while tightening repeated ideas, adding a first-second thesis, readable full captions, restrained punch-ins, speaker identification, a registered CTA card, dialogue-first audio, and retention QA.

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。

当 AI 写完测试、要决定信不信这份测试时使用。查三类假信心：断言与实现是否同源复制、有没有边界与异常用例、失败信号够不够强；再规划 2–5 个突变抽样，能执行时再实跑，看把被测代码改坏之后测试会不会变红，没实跑就不声称测试抓得住错。测试全绿不等于代码对，也可能是测试根本抓不住错。要决定整份 diff 能不能合并时走 ai-diff-review-protocol。

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。

当 AI 改完代码、要决定这份 diff 放不放行时使用。不逐行精读，按四步走查高风险点——意图是否匹配、有没有碰到边界条件、副作用面有多大、能不能回滚——每步有明确的放过与打回判据。改动文件数、增删行数、动到的迁移/锁文件/CI/配置/鉴权/测试文件和危险 hunk 先由 scripts/diff_risk.py 统计（粘贴进来的 diff 文本也能读，不需要 Git），越过固定阈值时必须人工逐段过。只有口头描述、没有改动内容时不给结论，只列要补的材料。diff 里新写的测试可不可信交给 ai-generated-test-auditor；没有基线的整段新代码走 ai-code-onboarding-checklist。

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。

Disciplined diagnosis workflow for hard, intermittent, or multi-component bugs and performance regressions: inspect logs and existing evidence, build a feedback loop, test falsifiable hypotheses, instrument gaps, and verify the outcome. Use when the root cause is unclear, reproduction is unreliable, several causes or components are plausible, or performance has regressed. Do NOT use for obvious single-cause errors, straightforward configuration or syntax fixes, planned feature work, or broad architecture review. Diagnosis-only requests stop after reporting the root cause and evidence; apply a fix only when the user requested one.

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 and analyzes malicious behavior in mobile applications through behavioral analysis, permission abuse detection, network traffic monitoring, and dynamic instrumentation. Use when analyzing suspicious mobile applications for data exfiltration, command-and-control communication, credential stealing, SMS interception, or other malware indicators. Activates for requests involving mobile malware analysis, app behavior monitoring, trojan detection, or suspicious app investigation.

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 and analyzes Bluetooth Low Energy (BLE) security attacks including sniffing, replay attacks, GATT enumeration abuse, and Man-in-the-Middle interception. Uses Ubertooth One and nRF52840 sniffers for packet capture, the bleak Python library for GATT service enumeration, and crackle for BLE encryption cracking. Use when assessing IoT device BLE security, monitoring for BLE-based attacks on wireless infrastructure, or performing authorized BLE penetration testing. Activates for requests involving BLE security assessment, Ubertooth sniffing, GATT enumeration, or BLE replay detection.

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。