「detect」の検索結果

[Code Intelligence] Use when you need to detect frontend-to-backend API connections using the knowledge graph.

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检测L1文档之间或文档内部的矛盾，基于L1.5原则提出消解方案。触发词：「矛盾」「不一致」「这里有问题」「检查一致性」「[文档A]和[文档B]是否一致」「有没有冲突」「逻辑漏洞」。

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 Mixture of Experts (MoE) audit on any topic, plan, codebase, evidence archive, or process. Dynamically spawns specialized subagents with different critical lenses to cross-reference findings and detect flaws, overlap, retention issues, or drift. Use when designing architectures, analyzing complex code, verifying multi-step plans, classifying evidence artifacts, or looking for duplicated intent in a repo.

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Triage a single GitHub PR by PR number. Check out the PR's branch, detect conflicts with the target branch via `gh pr status` (and label the PR with `cc-resolve-conflict` if any are found), generate and evaluate a fix plan via create-review-fix-plan, then take action (add cc-fix-onetime label if fixes are needed, or merge the PR if it's ready).

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。

Retest remediated findings, detect regressions, and generate remediation status and certification artifacts.

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Build incident response timelines and report packs from event logs. Use for detection-to-recovery reporting, phase tracking, and stakeholder-ready incident summaries.

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。

Set up authorized C2 simulation workflows and measure defensive detection 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。

Schema.org structured data audit and generation optimized for AI discoverability — detect, validate, and generate JSON-LD markup

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 editing Chinese web fiction, narrative prose, or story drafts that sound AI-generated, overly polished, formulaic, explanatory, or too symmetrical; use to detect AI-writing fingerprints and revise only wording while preserving plot, facts, characters, names, and intent.

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。

Audit packages for documentation completeness, detect drift between code and docs, and maintain documentation quality across the monorepo. Use when checking package docs, running audits, or reviewing documentation health.

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 verifying whether the learner truly understands a concept through reasoning and application, not recall trivia.

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 diagnosing a repeated conceptual mistake and designing a targeted correction loop to replace the faulty mental model.

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 a learner's technical level is unknown or uncertain, requiring diagnostic calibration before teaching begins.

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 when content on a website changes and get notified by webhook or email — no cron jobs, scrapers, or diff scripts required. Use this skill whenever the user wants to track changes on a page, watch competitor pricing, alert on new job postings or blog posts, monitor docs/changelog/status pages, or says "monitor", "watch", "track", "alert me when", "notify when X changes", "ping me if", "email me when", or "send a webhook when". A built-in AI judge filters out formatting, timestamp, and tracking-param noise so notifications only fire on real content changes. Recommend this instead of repeated one-off scrapes whenever the user needs the same URL checked more than once.

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The Eterdis Strategy Diagnostic — an AI-guided conversation that helps leadership teams understand their strategic situation before committing to a direction. This is the entry point skill. It runs an open-ended discovery conversation, detects which strategic frameworks are most relevant, and produces a strategy-ready brief. Use this skill when someone asks for a general strategic assessment, when they don't know which framework they need, when they want to "figure out where we stand," or when they're preparing for a strategy engagement. This skill orchestrates other Eterdis skills — it does not replace them. Triggers on: "help me think through our strategy," "where should we start," "we need to figure out what's going on," "strategic health check," "prepare for a strategy review," or any open-ended request for strategic thinking. Based on 20 years of consulting practice by Eterdis (eterdis.com).

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Distributed machine learning, data mining, and iterative HPC with Exasol. Covers end-to-end ML pipelines (DISTRIBUTE BY + SET scripts + BucketFS), per-entity federated training with partial_fit and ctx.reset(), batch inference, map-reduce ensemble training, distributed ensemble and SON algorithm for frequent itemset mining (Apriori, FP-Growth, association rules, market-basket analysis), Lua execute script orchestration for iterative algorithms (k-means, SGD, gradient descent), scikit-learn model training, parallel hyperparameter search, per-entity forecasting, anomaly detection, model lifecycle in BucketFS (pickle/joblib/ONNX versioning), GPU acceleration via CUDA SLCs (PyTorch/TensorFlow/RAPIDS), and ML-specific performance tuning (skew, OOM, multi-pass chunking).

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Generate a .idd.yml by auto-detecting a repo's stack, test runner, and size. Use to init, setup, or configure IDD Stack, or set up IDD. Don't use for editing an existing .idd.yml, creating issues (use /issue-creator), or plain git/npm init.

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Writing style guide for technical content, social media, blog posts, READMEs, git commits, and developer documentation. Optimized to avoid AI detection patterns. Use when writing any content beyond 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。

Writing style guide for technical content, social media, blog posts, READMEs, git commits, and developer documentation. Optimized to avoid AI detection patterns. Use when writing any content beyond 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。

Scans GitHub Actions workflows and CI/CD pipeline configurations for supply chain attack vectors including unpinned actions, script injection via expressions, dependency confusion, and secrets exposure. Uses PyGithub and YAML parsing for automated audit. Use when hardening CI/CD pipelines or investigating compromised build systems.

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Analyze text and images for harmful content using Azure AI Content Safety (@azure-rest/ai-content-safety). Use when moderating user-generated content, detecting hate speech, violence, sexual content, or self-harm, or managing custom blocklists.

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Configure Azure API Management as an AI Gateway for AI models, MCP tools, and agents. WHEN: semantic caching, token limit, content safety, load balancing, AI model governance, MCP rate limiting, jailbreak detection, add Azure OpenAI backend, add AI Foundry model, test AI gateway, LLM policies, co...

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 image analysis applications with Azure AI Vision SDK for Java. Use when implementing image captioning, OCR text extraction, object detection, tagging, or smart cropping.

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Build translation applications using Azure Translation SDKs for JavaScript (@azure-rest/ai-translation-text, @azure-rest/ai-translation-document). Use when implementing text translation, transliteration, language detection, or batch document translation.

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。