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constraints-scheduling约束调度

Agent Skill

constraints-scheduling 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

1,261

周安装

51

GitHub Stars

93

下载量

396
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

3

许可证

MIT

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:constraints-scheduling(约束调度)
来源仓库:https://github.com/letta-ai/skills
仓库路径:skills/constraints-scheduling
安装命令:
npx skills add https://github.com/letta-ai/skills --skill constraints-scheduling
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/letta-ai/skills --skill constraints-scheduling

简介

约束调度提供系统化方法处理多方时间冲突,找到满足参与者可用性、偏好与日历事件的共同时段。

  • 适用于会议安排、课程表编排和资源预约等涉及多重约束的时间管理场景。
  • 通过 GitHub 安装并使用 npx skills add 命令添加,强调输入验证与程序化确认流程。
  • 输出结果需经人工复核,特别是在跨时区或存在隐性依赖的情况下需额外小心验证。
  • constraints-scheduling 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

Constraints Scheduling

Overview

This skill provides guidance for solving constraint-based scheduling problems where the goal is to find time slots that satisfy multiple constraints from different sources (participant availability, existing calendars, preferences, etc.). It emphasizes systematic data extraction, programmatic verification, and proper handling of time granularity requirements.

Workflow

Phase 1: Input Verification and Data Extraction

Before any analysis, establish complete and verified data:

  1. Verify input file integrity first - Check that input files have not been modified and contain expected data before processing
  2. Extract complete data - Ensure all file contents are fully read (watch for truncation in tool outputs)
  3. Use appropriate parsers - For structured formats like ICS (iCalendar), use proper parsing libraries rather than manual text extraction
  4. Document extracted data - Create a clear summary of all extracted events, constraints, and preferences

Phase 2: Constraint Classification

Organize constraints into clear categories before searching for solutions:

  1. Hard constraints (must be satisfied):

- Participant availability windows - Existing calendar conflicts - Required meeting duration - Non-negotiable time restrictions

  1. Soft constraints (preferences for tie-breaking):

- Preferred days of week - Preferred times of day - Buffer time preferences - Other stated preferences

  1. Edge case constraints:

- Boundary conditions (e.g., meeting ending exactly when lunch begins) - Day-specific rules (e.g., early departure on certain days) - Buffer requirements (e.g., no meetings after a certain time)

Phase 3: Systematic Solution Search

Implement a comprehensive search approach:

  1. Respect stated granularity - If the task specifies minute-level granularity, check all possible start times at that granularity (not just hour boundaries)
  2. Use programmatic verification - Write a script that encodes all constraints and systematically checks every possible time slot
  3. Single comprehensive script - Avoid duplicating work between manual analysis and code; trust programmatic verification
  4. Check earliest valid slot - When finding the "earliest" slot, ensure the search starts from the correct baseline and respects granularity

Phase 4: Verification

Before finalizing the solution:

  1. Verify against all hard constraints - Explicitly check each constraint against the selected time
  2. Document boundary conditions - For edge cases (meeting ending exactly at a constraint boundary), explicitly confirm validity
  3. Cross-reference with source data - Ensure the selected slot doesn't conflict with any extracted calendar events
  4. Verify output format - Ensure the output matches any specified format requirements

Common Pitfalls

Data Extraction Issues

  • Truncated file reads - Tool outputs may truncate long files; always verify complete data extraction or use programmatic parsing
  • Manual parsing errors - Avoid manually extracting structured data (like ICS events) when libraries exist
  • Missing events - Incomplete calendar parsing can lead to scheduling conflicts

Granularity Mistakes

  • Ignoring stated granularity - If minute-level granularity is required, checking only hourly slots may miss valid earlier times
  • Boundary assumptions - A meeting at 10:00-11:00 and a constraint starting at 11:00 may or may not conflict depending on requirements

Analysis Disorganization

  • Redundant manual analysis - Performing the same constraint checks manually multiple times wastes effort and introduces inconsistency
  • Stream-of-consciousness reasoning - Jumping between days and constraints without structure increases error risk
  • Late verification - Checking input integrity after analysis is complete, rather than before

Constraint Handling

  • Treating soft constraints as hard - Preferences should be tie-breakers, not elimination criteria
  • Missing edge cases - Day-specific rules, buffer requirements, and boundary conditions are easily overlooked
  • Incomplete constraint enumeration - Failing to extract all constraints from input data

Verification Checklist

Before submitting a scheduling solution, verify:

  • All input files were read completely without truncation
  • Input file integrity was verified (not modified)
  • All hard constraints are satisfied by the selected time
  • No conflicts exist with any extracted calendar events
  • Stated granularity requirements were respected in the search
  • Edge cases and boundary conditions were explicitly checked
  • Soft constraints were used appropriately for selection among valid options
  • Output format matches requirements

Best Practices

  1. Start with programmatic approach - Immediately write a comprehensive script to parse inputs and check constraints rather than manual analysis
  2. Use proper libraries - For ICS files, use icalendar or similar libraries; for other structured formats, use appropriate parsers
  3. Structure analysis hierarchically - Organize by constraint category rather than jumping between different days or time slots
  4. Single source of truth - Trust programmatic verification; avoid duplicating analysis manually
  5. Explicit boundary handling - Document how boundary conditions are handled (inclusive vs exclusive endpoints)
  6. Complete search - When finding "earliest" or "best" slots, search all possibilities at the required granularity

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

04

需要参考平台分布和安装热度时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Claude Code

30.09%
按下载量换算119

Gemini CLI

24.02%
按下载量换算95

Codex

17.69%
按下载量换算70

Antigravity

13.87%
按下载量换算55

OpenCode

8.2%
按下载量换算32

windsurf

3.64%
按下载量换算14

安全审计

Gen Agent Trust Hub

未通过

Socket

通过

Snyk

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。

来源信息

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