“subprocess”的搜索结果

Use this skill when the company acts as a processor for its customers and someone asks "update our subprocessor list", "are we missing subprocessors", "we added a new tool, do we need to tell customers", "check the public subprocessor page", "where is customer data processed" or "draft the subprocessor change notice". Finds which suppliers actually receive customer personal data, compares them with the published list, records purpose, data, locations and transfer basis for each, works out the customer notice duties from the signed agreements, and writes an updated register, a change list and a draft notice for review.

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Manage the recorded list of vendors that process customer data and check advance notices and audit evidence. The skill also explains temporary staff access to customer accounts, including access limits, revocation, and the actions recorded in customer audit logs.

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• Adding a vendor that processes customer data
• Checking notice timing before sending data to a new vendor
• Retrieving notification evidence for an auditor

Plan and review new Omnigent harness integrations, the adapters connecting agent runtimes to the platform. Compare SDK and subprocess approaches with native interface wrappers, using capability matrices and checklists for implementation and testing.

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• Plan a new agent runtime integration
• Compare SDK and native integration requirements
• Review approval, streaming, and session support

生成Python脚本，利用FFmpeg将图片序列按3x3布局合并，或将合并图拆分。要求使用subprocess模块执行命令，并支持用户交互式输入路径。

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Use this skill when the company is about to buy, renew or switch on an AI product or an AI feature inside an existing product, and asks "assess this AI vendor", "do they train on our data", "AI supplier questionnaire", "review the AI terms", "can we use this model provider with customer data", "who are the subprocessors behind this AI tool" or "AI vendor risk assessment". Sizes the review to the use, reads the terms, data processing agreement and documentation before asking questions, checks training use, retention, model changes, subprocessors, transfers, security and exit, and writes an assessment with a recommendation plus the follow-up questions for the vendor.

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生成Python脚本，利用FFmpeg将图片序列按3x3布局合并，或将合并图拆分。要求使用subprocess模块执行命令，并支持用户交互式输入路径。

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从适合程度等五个维度评估仓库是否已准备好引入AI智能体运行框架，并查看单次运行的预估费用和项目模板准备状态，全程不修改目标文件。

• 引入智能体运行框架前评估
• 通过五项指标比较准备情况
• 查看预估运行费用与模板状态

Guides contributors through adding a new programming language to Airflow, covering SDK structure, runtime coordination, communication rules, executable packaging, tests, and the documentation and configuration changes needed for a pull request.

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• Adding support for a new programming language
• Choosing how Airflow launches a language runtime
• Implementing communication and executable bundles

Diagnose frozen or slow Qwen Code sessions on the current machine by checking CPU and memory usage, process states, child processes, network connections, and debug logs. Receive a diagnostic report with suggested next steps, without stopping processes.

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• Investigating an unresponsive Qwen Code session
• Diagnosing a specific process by PID
• Checking high CPU or memory usage

Check AI agent skills before installation by inspecting code, instructions, dependencies, and file access without running the code. Receive a PASS, WARN, or FAIL verdict with findings ranked by severity and suggested fixes.

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• Checking an unfamiliar skill before installation
• Finding suspicious instructions in SKILL.md
• Reviewing scripts for credential theft or data leaks

Receive live Lark/Feishu updates for messages, approvals, tasks, meetings, and more. Stream them as NDJSON, one JSON record per line, with filtering and time or event limits for bots and automated event processing.

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• Receiving new messages for bot processing
• Monitoring approval and task updates
• Receiving meeting lifecycle and minutes events

Review SaaS subscription terms against your team's standards, covering renewals, price increases, data export, and service availability commitments. Examine AI training rights and liability, with findings rated for both legal risk and business impact.

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• Checking renewal dates and cancellation deadlines
• Reviewing price increases and data export terms
• Examining AI training rights and liability limits

Guides development of Native SDK TypeScript services for file access, network requests, and other work outside the deterministic core. Covers typed calls, pinned npm dependencies, streaming, cancellation, replay, and process or thread execution.

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• Adding or updating Native SDK TypeScript services
• Moving file access or JSON processing out of the core
• Investigating NS1065–NS1067 errors

Check files created or edited by another agent, subprocess, or tool against explicit requirements. Verify the actual location, structure, counts, and key fields, then rerun with absolute paths when the results contradict the completion report.

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• Checking files produced by another agent
• Verifying spreadsheet sheets and row counts
• Checking CSV headers and record counts

Connect five MCP tools, agent-accessible external capabilities, into a development workflow spanning documentation search, code search, quality checks, and Git status. Learn how to share results between steps and continue when individual tools are unavailable or fail.

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• Connecting documentation search with code checks
• Building workflows that tolerate unavailable tools
• Troubleshooting API key delivery to MCP subprocesses

Builds robust Python MCP (Model Context Protocol) servers with FastMCP — tool design, error contracts, event-loop-safe blocking work, subprocess/CLI wrapping, single-file vs packaged distribution, global-state-free testing, and prompt-injection awareness. Use when writing an MCP server, exposing a tool or CLI to an LLM client, debugging tool registration/packaging, or testing MCP tools.

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Multi-agent research orchestration: split a research goal into parallel sub-goals, run each via headless `claude -p` subprocesses, aggregate results into a polished report file. Use for systematic web/document research, competitive or industry analysis, batch link/dataset processing, and long-form evidence synthesis. Triggers: "深度调研", "deep research", "wide research", "多 Agent 调研", "系统调研".

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Secure Python services against secret leakage, injection, unsafe subprocess calls, and dependency drift. Use when handling env vars, tokens, SQL, file paths, shell commands, auth flows, or Python security gates.

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Core Python 3.11+ language standards for typing, dataclasses, imports, pathlib, and stdlib-first code. Use for idiomatic language constructs in Python modules or stubs; defer pytest fixtures, database/client configuration, subprocess security, and other specialized concerns.

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Annotate and filter VCF variants with SnpEff and SnpSift. SnpEff predicts functional effects (HIGH/MODERATE/LOW/MODIFIER), genes, transcripts, AA changes, HGVS; SnpSift filters and adds ClinVar/dbSNP. Java CLI with Python subprocess integration. Use ANNOVAR for multi-database annotation; Ensembl VEP for REST API; SnpEff for fast CLI with pre-built genomes.

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精细化 AI 短剧 IP 创作技能（v0.6.0）。三阶段架构：Phase 1 创作（剧本+ref图，反复迭代）→ Phase 1.5 分镜图（每 grid 1-4 张候选静态图 = 视频首帧，工业级核心层）→ Phase 2 出片（按集解锁，4 模自动选）。v0.6.0 关键升级：① ref 库工艺偏置铁律（现代摩天楼易拟物，古建筑必出 chibi 人体，前期 IP 设计阶段就要避坑）② 即梦 5.0 失败模式 + 敏感词清单（3 类 fail 区分 / prompt 1500 字硬上限 / 反派词替换表 / 暧昧词清单）③ 分镜图 8 段 prompt 模板（CHARACTER/BACKGROUND/ACTION/SCENE/CAMERA/LIGHT/TEXT/STYLE）+ NOT humans 子句必加。沿用 v0.3.0 升级：Phase 1.5 分镜图层、4 模视频、ref 5-8 最优。v0.2.0：bash → Python subprocess、36 grid × 4-10s 变奏、红果必爆 7 招、工业级 ref 库 80-150 张、单 prompt 300-500 字。务必触发：用户提到短剧、微短剧、竖屏剧、AI 短剧、AI 漫剧、剧本创作、分镜、即梦/Seedance 出片、红果/番茄/抖音 IP 改编、爽剧、重生、穿越、赘婿、追妻、神医相师、AI 漫剧奇观、或"帮我做一部短剧"类请求。

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Bun shell scripting with Bun.$, Bun.spawn, subprocess management. Use for shell commands, template literals, or command execution.

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Delegate approved coding tasks to Pi subprocesses with free opencode defaults, model setup, live progress, and metrics. Don't use for direct edits, CI, or non-Pi agents.

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Use when the diff builds a query, a shell command or argv, a subprocess environment, a template, an XML parse, an eval or a deserialization from data that may be attacker-controlled - SQL/NoSQL, OS command, argument, env-var, template and code injection, XXE, unsafe deserialization

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