trigger-cost-savings 是做什麼的？
分析 Trigger.dev 任務、排程和執行記錄，找出成本最佳化的機會。適用於被要求減少支出、最佳化成本、審核用量、調整機器規模或檢討任務效率時。需要 Trigger.dev MCP 工具來進行執行分析。
分析 Trigger.dev 任務、排程和執行記錄，找出成本最佳化的機會。適用於被要求減少支出、最佳化成本、審核用量、調整機器規模或檢討任務效率時。需要 Trigger.dev MCP 工具來進行執行分析。
此技能專門分析 Trigger.dev 平台上的任務、排程和執行記錄，以識別並建議成本最佳化措施。它能幫助開發者減少雲端運算支出、審核資源使用效率、調整機器規格，並提升任務效能。此技能支援所有 Trigger.dev 環境，並需搭配 Trigger.dev MCP 工具以取得詳細的執行資料進行分析。其運作方式是先收集任務與排程的執行歷史，然後根據執行時間、資源占用、錯誤率等指標，提出具體的最佳化建議，例如縮減閒置排程、調整並行限制或降級機器類型。
npx skills add https://github.com/triggerdotdev/skills/tree/main/skills/trigger-cost-savingsInstalled? Explore more 研究與資料分析 skills: affaan-m/uspto-database, affaan-m/scholar-evaluation, affaan-m/literature-review · View all 6 →
分析 Trigger.dev 任務、排程和執行記錄，找出成本最佳化的機會。適用於被要求減少支出、最佳化成本、審核用量、調整機器規模或檢討任務效率時。需要 Trigger.dev MCP 工具來進行執行分析。
This repo contains 5 individual skills — each has its own dedicated page.
AI agent patterns with Trigger.dev - orchestration, parallelization, routing, evaluator-optimizer, and human-in-the-loop. Use when building LLM-powered tasks that need parallel workers, approval gates, tool calling, or multi-step agent workflows.
Configure Trigger.dev projects with trigger.config.ts. Use when setting up build extensions for Prisma, Playwright, FFmpeg, Python, or customizing deployment settings.
Subscribe to Trigger.dev task runs in real-time from frontend and backend. Use when building progress indicators, live dashboards, streaming AI/LLM responses, or React components that display task status.
Set up Trigger.dev in your project. Use when adding Trigger.dev for the first time, creating trigger.config.ts, or initializing the trigger directory.
Build AI agents, workflows and durable background tasks with Trigger.dev. Use when creating tasks, triggering jobs, handling retries, scheduling cron jobs, or implementing queues and concurrency control.
USPTO patent and trademark data workflow for official record lookup, PatentSearch queries, TSDR checks, assignment data, and reproducible IP research logs.
Structured scholarly-work evaluation for papers, proposals, literature reviews, methods sections, evidence quality, citation support, and research-writing feedback.
Systematic literature-review workflow for academic, biomedical, technical, and scientific topics, including search planning, source screening, synthesis, citation checks, and evidence logging.
The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.
Agent skill repository discovered by 10x-chat research.
Review backend code for quality, security, maintainability, and best practices based on established checklist rules. Use when the user requests a review, analysis, or improvement of backend files (e.g., `.py`) under the `api/` directory. Do NOT use for frontend files (e.g., `.tsx`, `.ts`, `.js`). Supports pending-change review, code snippets review, and file-focused review.