「generation」の検索結果

Robust document generation with engine fallback chain, error diagnostics, and Python alternatives

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。

Unicode-safe fallback workflow for multi-format document generation with character sanitization and explicit error capture

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。

Unicode-safe fallback workflow for multi-format document generation with character sanitization

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。

Document generation with direct pandoc/ReportLab execution (lightweight default) and optional shell_agent fallback for complex scenarios

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。

Handle cascading data retrieval tool failures by falling back to embedded knowledge generation

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。

Fallback workflow for multi-format document generation when shell_agent encounters errors

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。

Generate complex multi-page PDFs by running reportlab Python code directly via run_shell when shell_agent fails on document creation

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。

Systematic document generation with unicode sanitization, engine fallback chain, and explicit error diagnosis

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。

Multi-engine document generation with cascading PDF fallbacks and robust Unicode handling

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。

Unified document generation with tool failure detection, domain knowledge fallback, progressive conversion, and Unicode safety

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。

Resilient multi-format document generation with environment checks, auto-sanitization, and fallback engines

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。

Mutation-driven test vector generation. Finds implementations of a cryptographic algorithm or protocol, runs mutation testing to identify escaped mutants, then generates new test vectors that deliberately exercise the uncovered code paths. Compares before/after mutation kill rates to prove vector effectiveness. Use when generating cryptographic test vectors, measuring Wycheproof coverage gaps, finding escaped mutants via mutation testing, creating cross-implementation test suites, or improving test vector coverage for crypto primitives.

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Phishing campaign execution methodology for authorized red team engagements. Covers end-to-end campaign lifecycle: infrastructure provisioning (GoPhish, SMTP relay configuration, domain acquisition and aging, SPF/DKIM/DMARC alignment), payload delivery vectors (Office macro weaponization, HTA droppers, ISO/IMG container abuse, LNK shortcut hijacking, OneNote embedded payloads, HTML smuggling), email authentication bypass techniques (SPF softfail exploitation, DKIM replay attacks, display name spoofing, homoglyph and cousin domain registration), credential harvesting with MFA bypass (EvilGinx2 transparent proxy, Modlishka session relay, pixel-perfect HTML cloning), spear phishing pretext development informed by OSINT, email security gateway evasion, QR code phishing (quishing), and callback phishing for initial access. Integrates with GoPhish for campaign management, EvilGinx2 for adversary-in-the-middle credential interception, King Phisher for template design, and the Social Engineering Toolkit for payload generation. Maps to MITRE ATT&CK T1566 (Phishing) and sub-techniques T1566.001 (Spearphishing Attachment), T1566.002 (Spearphishing Link), T1566.003 (Spearphishing via Service). Assumes you have written authorization and a defined scope before any campaign execution.

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Insecure deserialization exploitation across Java, PHP, .NET, Python, Node.js, and Ruby. Covers gadget chain construction with ysoserial/phpggc/ysoserial.net, ObjectInputStream and BinaryFormatter sink identification, pickle __reduce__ RCE, phar:// wrapper abuse, Jackson polymorphic typing, Json.NET TypeNameHandling, ViewState tampering, node-serialize IIFE injection, Ruby Marshal.load and YAML.load gadgets, framework-specific chains for Spring/Hibernate/Laravel/Symfony, modern attack surfaces including Kubernetes admission webhooks and message queue consumers, WAF bypass through encoding layers and content-type manipulation, and serialVersionUID/JMX/RMI vectors. Activate when the engagement involves deserialization sinks, serialized data in cookies or request bodies, gadget chain development, magic method abuse, ysoserial payload generation, or any review of marshalling and unmarshalling logic in target applications.

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Use when you have just been primed into an EXISTING seat through a planned handover — a different agent retired and handed you the seat plus its earned context — and you need a world model of what just happened to you. Covers how a handover differs from compaction and from a fresh launch, the inherit-the-seat-not-the-identity rule, treating the handover packet as testimony you verify (and refusing stale ghost prompts, and verifying your OWN identity surfaces), why a seat is a COMPOUNDING LINEAGE (inherit the state, acquire the predecessor's epistemology, then improve and re-deposit so the next generation is better), how to query your predecessor, your duty to stream feedback and re-deposit your earned wisdom, and how to route to role-specific onboarding. NOT the driver-side handover mechanic (retiring-and-inheriting-a-seat) and NOT unplanned compaction/crash recovery (claude-compaction-restore / session-compaction-and-restore).

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Use when triaging public-equity idea candidates. Do not use for final trade recommendations, pitches, memos, or models.

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Use when the user wants to set up, scale, validate, or harden NVIDIA physical AI infrastructure for synthetic data generation workflows across local MicroK8s or Azure AKS, including Kubernetes clusters, inference endpoint deployment, OSMO deployment, workload submission readiness, and infrastructure failure recovery. Trigger keywords: physical ai infrastructure, resilient scaling, SDG infrastructure, microk8s, azure aks, NVCF deployment, NIM Operator, OSMO deploy, workflow scaling. Don't trigger for: OSMO log summarization or workload-only operations unless infrastructure setup, scaling, validation, or recovery is requested.

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Route single-cell or single-nucleus RNA-seq FASTQs to public count-generation workflows and defer post-count matrix QC, annotation, clustering, and UMAP analysis to the embedded scrna-seq-qc skill.

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Burn timed subtitles onto a finished video or configure Whisper-timed caption burning during faceless-video assembly. Takes video/audio generation jobs or a local finished video, optionally with authored narration text, and returns a captioned video or the exact backend subtitle configuration. Timings always come from Whisper on the video's own audio — never estimated. THREE looks: `paper` (torn cream paper label, handwritten ink), `bold` (UGC ALL-CAPS, white with black stroke, platform safe zones), `clean` (slim white CAPS, no plate, tiny at the bottom — no Pillow needed). Use when: adding/burning subtitles or captions to an existing video, restyling captions, or when a production workflow needs its finished cut subtitled. NOT for: generating the video, translating speech, or live/soft subtitle tracks (this burns them into the picture).

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。

Generate and normalize 2D sprite animations. Use when the user asks for full-strip generation from approved source frames, consistent anchor and scale normalization, or preview assets for browser-game animation.

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Use only when the user asks to produce a finished multi-scene narrator-led video and explicitly requests a faceless channel, YouTube automation, narrated explainer/story/education, documentary, storybook or myth retelling, or kids song video. Topic alone is never enough: a generic video of/about something, including a historical topic, uses ordinary video generation. Do not use for planning or ideas, single clips, silent animation, image-to-video, footage edits, ads, product demos, or UGC. This workflow requires a consistent non-photoreal style, reusable assets, narrator voiceover, and burned subtitles.

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Use when a user wants to see their logo, design, or sketch on a product or scene mockup — mugs, t-shirts, business cards, hats, phone screens, posters, billboards, or similar. Triggers on "create mockups", "show my logo on products", or any logo upload with a request to visualize it on items. Access: 🔐 Signed-In required | Gen AI: ✅ Adobe Firefly via `image_generate` used for design creation, sketch polishing, and mockup scene generation

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Configure, explore, and optimize Nx monorepo workspaces. Use when setting up Nx, exploring workspace structure, configuring project boundaries, analyzing affected projects, optimizing build caching, or implementing CI/CD with affected commands. Keywords — nx, monorepo, workspace, projects, targets, affected. Do NOT use for running tasks (use nx-run-tasks) or code generation with generators (use nx-generate).

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Execute build, test, lint, serve, and other tasks in an Nx workspace using single runs, run-many, and affected commands. Use when user says "run tests", "build my app", "lint affected", "serve the project", "run all tasks", or "nx affected". Do NOT use for code generation (use nx-generate) or workspace configuration (use nx-workspace).

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。