490건 · 관련도순
Build AI applications with OpenAI Agents SDK - text agents, voice agents, multi-agent handoffs, tools with Zod schemas, guardrails, and streaming. Prevents 11 documented errors. Use when: building agents with tools, voice agents with WebRTC, multi-agent workflows, or troubleshooting MaxTurnsExceededError, tool call failures, reasoning defaults, JSON output leaks.
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Fetch recent posts from one or more X/Twitter accounts through twitterapi.io, output structured JSON/CSV records, optionally sync records to Feishu/Lark Bitable through feishu-cli, and optionally guide recurring execution through OpenClaw, Codex automations, cron, or launchd. Use when the user wants to monitor X bloggers, collect recent tweets, export tweet metrics, append tweets to Feishu Bitable, or set up a scheduled Twitter/X account tracking workflow.
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Track and visualize ML training experiments with Trackio. Use when logging metrics during training (Python API), firing alerts for training diagnostics, or retrieving/analyzing logged metrics (CLI). Supports real-time dashboard visualization, alerts with webhooks, HF Space syncing, and JSON output for automation.
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PiFlow 技能生成器。当用户完成了一次完整并成功的数据处理任务时调用，根据当前 skills/generated 的本地约定和 references/piflow_skill_template.md 通用模板创建、更新或校验 PiFlow-compatible skill，包括 UTF-8 编码的 SKILL.md、DAG 可读的 input_params/output_params、version/tag 元数据、skill.json、scripts/references/assets 资源目录，以及本地校验脚本。仅在用户明确要求生成或保存 skill，或某次数据处理任务已经完成并需要把已验证成功的操作流程沉淀为 skill 时使用；默认不要因为“可能需要 skill”或“当前能力不足”而优先触发此 skill。
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PiFlow 技能生成器。当用户完成了一次完整并成功的数据处理任务时调用，根据当前 skills/generated 的本地约定和 references/piflow_skill_template.md 通用模板创建、更新或校验 PiFlow-compatible skill，包括 UTF-8 编码的 SKILL.md、DAG 可读的 input_params/output_params、version/tag 元数据、skill.json、scripts/references/assets 资源目录，以及本地校验脚本。仅在用户明确要求生成或保存 skill，或某次数据处理任务已经完成并需要把已验证成功的操作流程沉淀为 skill 时使用；默认不要因为“可能需要 skill”或“当前能力不足”而优先触发此 skill。
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Use this skill before answering, planning, or editing whenever .NET tests or CI are switching from VSTest to Microsoft.Testing.Platform (MTP), or an MTP migration behaves differently. Triggers include "switch from VSTest"; MSTest/NUnit/xUnit MTP enablement; OutputType=Exe only for test projects in Directory.Build.props; EnableMSTestRunner, EnableNUnitRunner, UseMicrosoftTestingPlatformRunner, or YTest.MTP.XUnit2; .NET 10 global.json test.runner and TestingPlatformDotnetTestSupport; translating VSTest filters, logger, coverage, blame, or dump arguments; replacing VSTest@3; and exit code 8 or zero tests. Also use for xUnit v3 MTP filters during a v2-to-v3 upgrade. Do not use for framework conversion, TFM, UWP, or WinUI.
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Run Claude Code programmatically without interactive UI. Triggers on: headless, CLI automation, --print, output-format, stream-json, CI/CD, scripting.
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Generates properly tagged UTM links with consistent naming conventions. Maintains a UTM registry file (utm-registry.json) to enforce naming consistency, prevent duplicates, generate short links, and output ready-to-copy links for LinkedIn, email, social, and ads.
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Generate realistic synthetic data using Spark + Faker (strongly recommended). Supports serverless execution, multiple output formats (Parquet/JSON/CSV/Delta), and scales from thousands to millions of rows. For small datasets (<10K rows), can optionally generate locally and upload to volumes. Use when user mentions 'synthetic data', 'test data', 'generate data', 'demo dataset', 'Faker', or 'sample data'.
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Compress-Cache-Retrieve for huge, repetitive array tool output (logs, scan results, time series, event streams) before it enters context. Keeps an informative sample — endpoints, anomalies/change-points, head/tail — drops the redundant rest to a local cache, and leaves a retrievable hash. Use when a tool returns a long uniform JSON array you must read but mostly skim, and the full set is one command away if needed. Lossy-but-recoverable.
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Create and troubleshoot Folium maps, GeoJSON layers, choropleths, and plugins for browser-rendered Leaflet output.
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Design spec with 98 rules for building CLI tools that AI agents can safely use. Covers structured JSON output, error handling, input contracts, safety guardrails, exit codes, and agent self-description.
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Assign, validate, normalize, and explain Luhmann/Zettelkasten-style project-note codes for Chinese book manuscripts, Obsidian Markdown folders, Mandala-Grid section files, HyperCard Markdown stacks, and project-note JSON to EPUB workflows. Use when Codex needs to scan folders for 部/章/節/項/目 numbering, generate stable card ids, insert branch codes without renumbering existing notes, map letter-coded branches to Obsidian Mandala sections, preserve K### keyword index cards, sanitize private catalogs into public examples, or prepare card IDs for EPUB and HyperCard output.
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Reverse weekly review for turning inbox noise, daily plans, diaries, calendars, annual plans, and long-range life plans into one happiness-and-peace-centered weekly plan. Use when the user asks for 週檢視, 反向週計劃, 8 Big Rocks, Inbox Zero weekly review, 幸福寧靜週, or wants to synthesize Todoist/inbox notes, daily diaries, calendars, yearly plans, and life plans into iMandalArt/FIRE/JSON outputs.
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Instrument programs (Python, C/C++, Java) to capture snapshots of key program states at runtime, including variables, memory, and call stacks. Use when you need to debug complex issues, reproduce test cases, prepare traces for formal verification, or analyze program execution. Supports manual instrumentation points, automatic function/method instrumentation, and conditional triggers. Outputs structured JSON snapshots for debugging, replay, and verification workflows.
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Collect, normalize, and structure execution traces from instrumented programs (strace, ltrace) into JSON format for downstream analysis. Use when working with system call traces, library call traces, or execution logs that need to be analyzed for debugging, test case reproduction, or verification. Supports parsing strace/ltrace output, filtering noise, extracting debug information, and preparing traces for bug analysis or reproduction workflows.
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Transforms natural language requirements (user stories, verbal descriptions, business rules) into formal specifications and constraints. Use when converting informal requirements into structured, testable specifications with explicit constraints. Outputs in multiple formats including BDD-style Given-When-Then, JSON Schema, and structured plain text requirements documents.
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Generate prioritized CVE watchlists and actionable security recommendations for repositories. Use when analyzing CVE scan results, creating security reports, prioritizing vulnerability remediation, or generating security gate reports for CI/CD. Takes CVE scan results (JSON/SARIF from npm audit, pip-audit, Snyk), reachability analysis, and cutoff date as input. Combines severity, reachability, exploitability, and dependency criticality to rank CVEs by practical risk. Outputs markdown reports with concrete next-step guidance (immediate upgrade, monitor, ignore with justification, apply mitigation) suitable for issue trackers, security reviews, and CI security gates.
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Generate abstract Control Flow Graph (CFG) representations of programs showing loops, branches, and function calls for static analysis or verification. Use when users need to: (1) Visualize program control flow structure, (2) Generate CFGs for static analysis tools, (3) Create control flow abstractions for formal verification, (4) Analyze program paths and reachability, (5) Document program structure. Supports both function-level (intraprocedural) and program-level (interprocedural) analysis with multiple output formats (textual, DOT/Graphviz, JSON).
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Define, validate, run, resume, and audit repo-local multi-step automations with current `asc workflow` and `.asc/workflow.json`, including step outputs and safe release/TestFlight workflows.
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Base template for Stage2 rebuttal refinement. Use when creating or extending conference-specific Stage2 skills so all venues share the same high-quality writing constraints, JSON output format, and normalization rules.
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Design Health — runs 19 deterministic quality checks on HTML output. Use when the user says /reimagine-it audit, "audit this page", "check design quality", or wants to verify craft-floor compliance before shipping. Catches blank plates, missing focus rings, non-compositor motion, off-palette accents, dead motion, fabricated content. No LLM, no API key. CI-ready with --json and exit codes. Part of the reimagine-it Content-Derived Design suite.
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Extract brand identity from a website URL — voice, colors, typography, imagery, values, and target audience — into a structured brand-profile.json. The profile feeds downstream skills (seo-content-writer, email-composer, frontend-design, pro-deck-builder, cross-platform-audit) for brand-consistent output. Use when the user says brand DNA, brand profile, extract brand, analyze brand, brand voice, brand identity, brand colors, or brand style guide.
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Cerebras Inference is a hosted LLM API that runs open models on Cerebras wafer-scale chips at thousands of tokens per second, through an OpenAI-compatible endpoint. Use when a user wants the lowest-latency chat, code completion or agent loop, asks how to call the Cerebras API or SDK, needs streaming, tool calling, strict JSON schema output or reasoning control, or hits a "model not found" error from a retired Cerebras model ID.
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