「payload」の検索結果

Analyzes weaponized Windows shortcut (.lnk) files: parsing the shell link structure for the target command, arguments, icon, and working directory, and recovering hidden PowerShell/cmd payloads and embedded content used in phishing. Activates for requests to analyze a malicious LNK, parse a shortcut file, or extract a command from a .lnk.

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。

Analyzes malicious Office documents by extracting and reviewing VBA macros and OLE/OOXML structure: auto-exec triggers, obfuscation, dropped payloads, and shell/PowerShell invocation. Activates for requests to analyze a malicious Word/Excel document, VBA macro, or maldoc.

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。

Analyzes malicious PDF documents: parsing the object structure for JavaScript, OpenAction/launch triggers, embedded files, and URIs, then extracting and decoding suspicious streams without opening the document in a reader. Activates for requests to analyze a malicious PDF, inspect PDF JavaScript, or extract embedded payloads from a PDF.

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。

Analyzes position-independent code and shellcode by identifying GetPC/PEB-walk techniques, API-hash resolution loops, and relocation-free addressing so the payload can be disassembled at the right base. Activates for requests to analyze position-independent code, identify PIC techniques, or understand how shellcode resolves APIs without imports.

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。

Deobfuscates malicious JavaScript from droppers, web pages, and HTA/scriptlets: unrolling string-array and char-code encodings, resolving eval/Function chains, and statically recovering payloads and URLs without executing untrusted code. Activates for requests to deobfuscate JavaScript, decode obfuscated JS, or analyze a malicious script dropper.

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 task needs the judgment of a Dragline and Excavating/Loading Machine Operator in surface mining — converting a rated bucket capacity into bank cubic yards moved per shift, deciding whether a swing angle or spoil-placement plan needs to be flagged to pit engineering, matching loading pass count and payload to a haul truck's rated capacity, checking ground bearing conditions before walking or tracking a machine onto fill or spoil, or reading a berm/highwall condition against MSHA thresholds.

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 task needs the judgment of an Aerospace Engineer — reconciling a vehicle mass/weight budget against a certification limit, computing a structural margin of safety, choosing a means of compliance for an FAA certification basis, tracing a weight-growth overrun back to a design decision, or evaluating a propulsion/aerodynamics tradeoff against a range or payload requirement.

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 interactive "living workflow" flowcharts in the visual style of https://statuesque-starship-49757d.netlify.app/revenue-engine - vanilla HTML/CSS/JS files with a horizontal step bar, a vertical trunk that grows per step, tool-to-tool columns with animated payload pills between brand-marked tool nodes, alternating left/right step descriptions, and per-step outputs. Use whenever the user says "build a flowchart", "interactive flowchart", "living workflow", "revenue engine flowchart", "demand gen flowchart", "GTM workflow diagram", "process diagram with animations", "Nebor-style flowchart", or asks to recreate the workflow visual from the Nebor site. Always asks which flowchart to build (Revenue Engine / Demand Gen / CRM & RevOps / Custom) and waits for explicit approval of the step + tool + edge table before generating any files.

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。

Write a data schema specification document. Use when the user says "schema spec", "document this schema", "schema design doc", "data model spec", "table spec", "field definitions", "schema contract", "data dictionary", "define these fields", or needs to formally document a database schema, event schema, or API payload schema - even if they don't explicitly say "schema spec".

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。

Generates valid Cloud Monitoring ListTimeSeries requests and aggregation specifications from metric descriptors and resource parameters. Use when asked to create, generate, format, or build ListTimeSeries requests, JSON payloads, filter expressions, or aligner/reducer aggregations for Cloud Monitoring metrics and charts. Don't use for metric discovery or metric selection.

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 configuring Workflow-based Outbound Messages to push SOAP payloads to external endpoints — endpoint setup, field selection, retry behavior, and delivery monitoring. NOT for the listener's session ID callback into Salesforce — use integration/outbound-messages-and-callbacks. NOT for JSON webhooks or Flow-triggered pushes — use integration/outbound-webhook-from-salesforce. Keywords: WorkflowOutboundMessage, outboundMessages, endpointUrl, includeSessionId, integrationUser, useDeadLetterQueue, apiVersion, SOAP Ack, .workflow file, approval outbound message action.

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。

Inbound email processing in Salesforce via Email Services + the `Messaging.InboundEmailHandler` Apex interface. Covers EmailService configuration (running user, accept-from address, attachment handling, error / failure routing), the EmailServicesAddress per-routing-address pattern, the handler's `Messaging.InboundEmail` payload (text body, HTML body, headers, attachments, in-reply-to threading), and the canonical Email-to-Case alternative for case creation. NOT for outbound email (use admin/email-templates-and-alerts), NOT for Email-to-Case flow customization itself (use admin/email-to-case-configuration). Trigger keywords: EmailServicesFunction, EmailServicesAddress, runAsUser, localPart, EmailDomainName, attachmentOption, overLimitAction, errorRoutingAddress, routing address, inbound email limit.

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 the upload archive for a plugin payload in this distribution repository. Use when asked to package the plugin, produce the submission zip, cut a plugin release artifact, or prepare a Codex/OpenAI Plugin Portal upload. Covers host selection, the pre-flight gates, and what to do after the archive exists.

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。

Verify ASP.NET Web services build the right IDownstreamApi route and payload without standing up HTTP

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。

Work with Runpod GPU infrastructure and documentation. Use when Codex needs to create, deploy, manage, debug, or review Runpod Pods, Serverless workers/endpoints, templates, network volumes, Public Endpoints, Flash apps, runpodctl commands, Runpod Python SDK usage, REST/GraphQL API usage, Runpod MCP setup, Docker images for Runpod, endpoint request payloads, logs, billing/cost checks, or Runpod deployment troubleshooting.

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。

Expert security auditor for AI Skills and Bundles. Performs non-intrusive static analysis to identify malicious patterns, data leaks, system stability risks, and obfuscated payloads across Windows, macOS, Linux/Unix, and Mobile (Android/iOS).

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。

Normalize an OpenClaw lead bundle into a conservative lead-screening package with missing-field warnings, manual-review reasons, and customer-intel-ready payloads.

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。

Data structures and serialization formats for agent-to-agent communication. Covers message envelopes, structured output schemas, capability declarations, task handoff payloads, error/retry signaling, and context windows as data structures. Deep comparison of A2A protocol, MCP, OpenAI function calling, and LangChain message types. Teaches when to use rigid schemas vs free-form with validation, typed vs untyped, streaming vs batch. Activate on: "agent message format", "agent communication schema", "agent-to-agent protocol", "A2A protocol", "MCP message format", "structured output for agents", "agent interop", "interchange format", "agent serialization", "task handoff format", "capability declaration". NOT for: what agents say to each other (use agent-conversation-protocols), orchestration topology (use multi-agent-coordination), building agent infrastructure (use agentic-infrastructure-2026).

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 the locked Slack TL;DR JSON payload from a completed weekly report. Saves to /tmp/slack-msg.json with the exact curl command for Nathan to post manually. Triggers on "draft Slack TL;DR", "Slack post", "build the Slack message".

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。

Semantic search over Qdrant Cloud collections using the Python client. Covers vector search, filtered search, prefetch+RRF fusion, group API, recommend API, discovery API, batch queries, scroll, and payload indexing. Use when building search features, adding new query types, or working with qdrant-client.

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。

Expert security auditor for AI Skills and Bundles. Performs non-intrusive static analysis to identify malicious patterns, data leaks, system stability risks, and obfuscated payloads across Windows, macOS, Linux/Unix, and Mobile (Android/iOS).

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 fileless malware that operates entirely in memory using PowerShell, WMI, .NET reflection, registry-resident payloads, and living-off-the-land binaries (LOLBins) without writing traditional executable files to disk. Activates for requests involving fileless threat detection, in-memory malware investigation, LOLBin abuse analysis, or WMI persistence examination.

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 prompt injection attacks targeting LLM-based applications using a multi-layered defense combining regex pattern matching for known attack signatures, heuristic scoring for structural anomalies, and transformer-based classification with DeBERTa models. The detector analyzes user inputs before they reach the LLM, flagging direct injections (system prompt overrides, role-play escapes, instruction hijacking) and indirect injections (encoded payloads, multi-language obfuscation, delimiter-based escapes). Based on the OWASP LLM Top 10 (LLM01:2025 Prompt Injection) and Simon Willison's prompt injection taxonomy. Activates for requests involving prompt injection detection, LLM input sanitization, AI security scanning, or prompt attack classification.

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。

Systematically deobfuscate multi-layer PowerShell malware using AST analysis, dynamic tracing, and tools like PSDecode and PowerDecode to reveal hidden payloads and C2 infrastructure.

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。