35 skills · sorted by relevance
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.
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.
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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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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Assess a repository's readiness for an AI agent execution framework across five dimensions. Produce JSON or a table with readiness scores, estimated cost per run, and scaffold status, without changing repository files.
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.
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.
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.
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.
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.
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.
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.
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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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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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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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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Use when reviewing Python changes - Django, Flask and FastAPI handlers, subprocess, SQL, Jinja2, format strings, and data/ML code that loads pickles, models, notebooks or remote model code
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Lark/Feishu real-time event listening / subscribing / consuming: stream events as NDJSON via `lark-cli event consume <EventKey>` (covers IM message receive, reactions, chat member changes, etc.). Use for Lark bots, real-time message processing, long-running subscribers, streaming webhook/push handlers. Supports `--max-events` / `--timeout` bounded runs and a stderr ready-marker contract — designed for AI agents running as subprocesses.
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Desktop GUI automation via OpenInterpreter — mouse, keyboard, screenshot, and OCR control for native macOS/Linux applications. Three modes: Library (Claude reasons, OI executes), OS subprocess (full autonomous computer use), and Local agent (Ollama, offline). This skill should be used when interacting with desktop apps that have no CLI or API, automating GUI workflows, reading screen content via OCR, or controlling mouse/keyboard.
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