「loads」の検索結果

Skill-set loader for /hunt orchestrator. Fingerprints the target, picks the right platform attack skills, and loads the Red Team or WAPT skill set. Use when /hunt has just received a mode answer (redteam or wapt + blackbox|greybox) and needs to load the appropriate skills and print the taxonomy. Not for direct user invocation.

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WAF bypass techniques checklist: encoding bypass (URL/HTML/Unicode/double encoding), case variation, comment injection, HTTP header manipulation, chunked encoding, IP rotation, timing attacks, and payload obfuscation per WAF vendor. Use when WAF is blocking payloads during web app tests.

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。

Shellcode development reference for offensive security engagements. Use when writing custom x86/x64 shellcode, implementing position-independent code (PIC), building shellcode loaders, evading AV/EDR detection, or converting PE files to shellcode. Covers null byte avoidance, API hashing, encoder/decoder patterns, staged vs stageless payloads, Windows PEB traversal, and cross-platform shellcode techniques.

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Intelligently organizes files and folders by understanding context, finding duplicates, and suggesting better organizational structures. Use when user wants to clean up directories, organize downloads, remove duplicates, or restructure projects.

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。

ClickHouse database patterns, query optimization, analytics, and data engineering best practices for high-performance analytical workloads.

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。

Automated web form filling and file uploading skill with Playwright browser automation. Handles login persistence, form detection, file uploads, and waits for manual confirmation before submission.

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Complete Open WebUI API integration for managing LLM models, chat completions, Ollama proxy operations, file uploads, knowledge bases (RAG), image generation, audio processing, and pipelines. Use this skill when interacting with Open WebUI instances via REST API - listing models, chatting with LLMs, uploading files for RAG, managing knowledge collections, or executing Ollama commands through the Open WebUI proxy. Requires OPENWEBUI_URL and OPENWEBUI_TOKEN environment variables or explicit parameters.

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Use this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like "the xlsx in my downloads") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.

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Requires OFOX_API_KEY — create one at https://app.ofox.ai. Shared execution layer for the Ofox video generation API (api.ofox.ai) — creates a video job, polls it to completion, downloads the finished mp4 from a persistent CDN URL, and reports the real cost. This is a library skill, not a standalone user-facing one — it is invoked by scenario skills such as seedance-short-drama, seedance-ad-creative, and seedance-product-video, which build model/prompt/resolution choices for a specific use case and then call into this skill's script rather than re-implementing the API calls. Load this skill directly only when a user explicitly names the Ofox video API, asks to call it with specific low-level parameters, or asks to debug/resume a stuck or failed Ofox video job by job id — for a plain scenario request ("make me a short drama scene", "generate a cinematic ad clip"), use the relevant scenario skill instead, which itself depends on this one.

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Resume work on the current task. Loads the workflow Phase Index, figures out which phase/step to pick up at, then pulls the step-level detail via get_context.py --mode phase. Use when coming back to an in-progress task and you need to know what to do next.

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。

S3, multipart uploads, CDN integration, and presigned URLs for file storage. Activate on: file upload, S3, presigned URL, multipart upload, CDN, blob storage, image upload, media storage. NOT for: data warehouse storage (use lakehouse-architect), database design (use dimensional-modeler).

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Use when running analytical SQL over Parquet/CSV/JSON without a warehouse, replacing pandas for data wrangling, joining S3 data in-place, building local data marts, or embedding OLAP into an app. Triggers: read_parquet/read_csv setup, partitioned dataset queries, hive partitioning, glob patterns for S3, COPY TO export, attach Postgres/MySQL, UDFs in Python/R, MotherDuck cloud sync, columnar performance vs row stores. NOT for OLTP workloads (concurrent writes), distributed analytics at petabyte scale (use Spark/Trino), or vector search (use pgvector/Lance).

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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. Compares the version-pinned A2A, MCP, and JSON-RPC role boundaries covered by this bundle; OpenAI and LangChain-specific APIs require their own current sources. Teaches when to use rigid schemas versus free-form content with validation, typed versus untyped boundaries, and streaming versus 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。

Drive Chrome via the claude-in-chrome MCP to exercise plan.md::Verify steps for a shipped feature. Loads deferred MCP tool schemas, asserts console/network cleanliness, records a GIF for the PR. Used by the browser-verifier agent.

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。

The barber for your coding agents' config. Weighs everything every agent on this machine loads (instruction files, memory, MCP servers, hooks, skills, commands, subagents, plugins, leftovers), prices each item in tokens from your own transcripts, and parks the dead weight with a receipt and an undo. Use this skill whenever the user mentions config bloat, a heavy or slow session start, tokens burned before the first word, too many MCP servers, hooks that are slow or fire on every prompt, skills or commands or plugins nobody invokes, cleaning up or auditing CLAUDE.md or AGENTS.md, Codex config, or asks what does my agent actually load, why is my context full before I type, which of these servers can I turn off, or what is this plugin costing me. Use it even when they only say the session feels bloated or startup got slow, and even when they mention /context, /doctor or /skill-doctor.

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Loads org- and repo-level coding rules from Qodo before code tasks begin, ensuring all generation and modification follows team standards. Use before any code generation or modification task when rules are not already loaded. Invoke when user asks to write, edit, refactor, or review code, or when starting implementation planning.

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。