774건 · 관련도순
Together AI SDK patterns for TypeScript — client setup, chat completions, streaming, structured output, function calling, embeddings, image generation, fine-tuning, and OpenAI-compatible endpoints
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Hugging Face Inference SDK patterns for TypeScript/Node.js — InferenceClient setup, chat completion, text generation, streaming, embeddings, image generation, audio transcription, translation, summarization, and Inference Endpoints
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在宣称工作完成、已修复或测试通过之前使用，在提交或创建 PR 之前——必须运行验证命令并确认输出后才能声称成功；始终用证据支撑断言
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Generates CVE request packages from pentest findings. Reads cve-candidates.json (auto-generated at pentest completion) or findings.json directly, then produces for each qualifying vulnerability: MITRE CVE form data, GitHub Security Advisory draft, full disclosure report, and vendor notification email. Invoke manually after a pentest engagement when you have true-positive findings that warrant CVE IDs.
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Deep API security assessment beyond surface scanning. Covers the full OWASP API Security Top 10 (2023): Broken Object Level Authorization (BOLA / IDOR), Broken Authentication, Broken Object Property Level Authorization (mass assignment + excessive data exposure), Unrestricted Resource Consumption, Broken Function Level Authorization (BFLA / vertical privilege escalation), Unrestricted Access to Sensitive Business Flows, Server-Side Request Forgery via API parameters, Security Misconfiguration, Improper Inventory Management (shadow/zombie/deprecated endpoints, v1/v2 drift), and Unsafe Consumption of third-party APIs. Works across REST, GraphQL, gRPC, SOAP, and MCP servers. Discovers APIs from OpenAPI/Swagger specs, GraphQL introspection, gRPC reflection, .well-known endpoints, JS bundles, and traffic capture. Uses kiterunner, ffuf, schemathesis, restler-fuzzer, openapi-fuzzer, graphql-cop, clairvoyance, batchql, inql, jwt_tool, postman, mitmproxy, and manual http(action="request", ...) payloads. Every technique includes actual payloads, commands, and verification logic. Chains from /pentester or /codebase when API endpoints are discovered, chains into /web-exploit when classic injection points are found in API parameters, chains into /post-exploit when RCE is achieved, and chains into /ai-redteam when an LLM/AI endpoint is discovered (chat APIs, completion endpoints, RAG search, agentic tool-use, MCP servers).
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Check whether the work is actually finished rather than finished-looking — what was promised versus what really runs. Several models compare the task against the code, and every completion claim has to survive a command that was actually executed. Use before calling something done, before a PR, at the end of a session, or on "is this done", "did I finish", "check if done", "what did we skip".
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Execute a project from a folder of raw materials. Use when the user drops messages, screenshots, documents, drafts, and references into a project directory and wants Codex to identify the real goal, build a plan, do the work, drive the project to a submission-ready state, verify completion, and return a clear final summary of what was required, what was completed, and how it was done.
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Land a PR by monitoring conflicts, resolving them, waiting for checks, and squash-merging when green; use when asked to land, merge, or shepherd a PR to completion.
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Use when a task needs the judgment of an Etcher/Engraver — compensating a resist pattern for expected undercut before etching fine detail, calculating etch time from a known etch rate rather than judging completion visually, verifying a functional engraving's line width/depth against its actual accuracy requirement, or re-validating process parameters on a new material batch before production.
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Use when a task needs the judgment of a Cooling/Freezing Equipment Operator — calculating freezing time for a new product thickness via the thickness-squared relationship rather than assuming linear scaling, verifying freezing completion by actual core/thermal-center temperature rather than surface temperature or elapsed time, recognizing a temperature plateau as expected latent-heat removal rather than a malfunction, or matching belt speed/residence time to the calculated freezing time requirement.
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End-of-story completion review. Reads the story file, verifies each acceptance criterion against the implementation, checks for GDD/ADR deviations, prompts code review, updates story status to Complete, and surfaces the next ready story from the sprint.
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OpenUI generative UI with OpenAI SDK backend. Streaming chat completions with gpt-5.5 (or any current OpenAI-compatible model).
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Prove existing work meets acceptance conditions without expanding scope. Use for validation-only tasks, completion checks, focused gate runs, and last-mile proof.
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Use when maintaining vailá File Manager Frame A buttons, previewed local operations, CLI parity, or interactive SSH transfer with status-file completion.
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Connects to and performs inference with Google Cloud Agent Platform GenAI models, including First-Party Gemini models and Third-Party OpenMaaS models (Llama, DeepSeek, Qwen, etc.). Use when asked to perform inference, ask a model a question, run a test prompt, execute chat completions, or generate code for calling Gemini or OpenMaaS models, authenticate with GenAI SDK, OpenAI SDK, or legacy Agent Platform SDK, configure base URLs and global/regional endpoints, or troubleshoot 429 Resource Exhausted (DSQ), 400 User Validation, or 404 Not Found errors. Don't use for deploying models to endpoints or for running model evaluations.
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Generate a pre-launch readiness checklist for feature releases. Use this skill when: - A feature is nearing completion and you need a launch readiness checklist - You want to ensure all pre-launch items are covered (docs, training, support, rollback) - You are planning a phased rollout and need a structured go-to-market plan
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Use this skill when configuring MCAE (Account Engagement / Pardot) lead scoring models, grading profiles, score decay rules, or automation rules that fire on score/grade thresholds. Covers: scoring point values per activity, score decay for inactivity, Profiles for fit grading, combined MQL definitions (Score + Grade), and automation rules vs completion actions. NOT for defining the MQL model before build — use admin/lead-scoring-requirements. NOT for Engagement Studio nurture programs — use admin/lead-nurture-journey-design.
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Configure platform-level global search in a Salesforce org — per-object Search Layouts (Search Results, Lookup Dialogs, Lookup Phone Dialogs, Tab columns), the Setup → Search Settings page (Sidebar Search, Lookup Auto-Completion, Drop-Down List size), and Synonym Groups (Setup → User Interface → Synonyms). NOT for Einstein Search personalization signals or Natural Language Search (use agentforce/einstein-search-personalization), NOT for Experience Cloud site Search Manager (use lwc/experience-cloud-search-customization), NOT for SOSL query authoring (use data/sosl-search-patterns), NOT for Commerce storefront search tuning.
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ออกแบบคอร์สออนไลน์ตั้งแต่ curriculum, lesson plan, assessment, จนถึง sales page พร้อมวางแผน completion rate
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Verify ticket completion criteria — use when finishing a ticket, before marking work done, or checking acceptance criteria. Runs tests, build, lint, scenarios, and dependency drift checks.
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Use when implementation is complete, all tests pass, and you need to decide how to integrate the work - guides completion of development work by presenting structured options for merge, PR, or cleanup
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Building production-quality CLI tools with Cobra command framework and Viper configuration management
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Use when about to claim work is complete, fixed, or passing, before committing or creating PRs - requires running verification commands and confirming output before making any success claims; evidence before assertions always
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Scan project state to determine phase completion, write flow_status.md, and orchestrate the full RTL design flow from architecture to documentation
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