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initiate-memory启动记忆

Agent Skill

initiate-memory 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

364

周安装

15

GitHub Stars

公开资料未说明

下载量

119
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

2

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:initiate-memory(启动记忆)
来源仓库:https://github.com/qredence/agentic-fleet
仓库路径:skills/initiate-memory
安装命令:
npx skills add qredence/agentic-fleet --skill "initiate-memory"
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。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

AgentSkills.tonpx skills
npx skills add qredence/agentic-fleet --skill "initiate-memory"

简介

启动记忆技能用于会话历史加载与上下文恢复。

  • 适用于需要长期对话记忆的多轮交互应用。
  • 通过 qredence/agentic-fleet 仓库安装,需配置存储后端。
  • 使用前应评估存储容量与隐私保护机制。initiate-memory 属于研究检索类 Skill,可作为该场景下的辅助能力补充。
  • 注意定期归档旧数据,防止内存占用过高影响性能。

SKILL.md

Initializing Memory

The user has requested that you initialize or reorganize your memory state for this project. This skill helps you systematically explore and document a codebase to build effective working memory.

Understanding Your Context

Important: This skill is designed for stateful agents that work with users over extended periods. Your memory is not just a convenience; it's how you get better over time and maintain continuity across sessions.

This command may be run in different scenarios:

  • Fresh project: Starting work on a new codebase
  • Existing project: User wants you to reorganize or significantly update your understanding
  • Deep dive: User wants comprehensive analysis of the project

Before making changes, inspect your current context and understand what already exists.

What to Remember About a Project

1. Procedures (Rules & Workflows)

Explicit rules and workflows that should always be followed:

  • "Never commit directly to main - always use feature branches"
  • "Always run lint before running tests"
  • "Use conventional commits format for all commit messages"
  • "Always check for existing tests before adding new ones"

2. Preferences (Style & Conventions)

User and project coding style preferences:

  • "Never use try/catch for control flow"
  • "Always add JSDoc comments to exported functions"
  • "Prefer functional components over class components"
  • "Use early returns instead of nested conditionals"

3. History & Context

Important historical context that informs current decisions:

  • "We fixed this exact pagination bug two weeks ago - check PR #234"
  • "This monorepo used to have 3 modules before the consolidation"
  • "The auth system was refactored in v2.0 - old patterns are deprecated"

Memory Scope Considerations

Consider whether information is:

Project-scoped:

  • Build commands, test commands, lint configuration
  • Project architecture and key directories
  • Team conventions specific to this codebase
  • Technology stack and framework choices

User-scoped:

  • Personal coding preferences that apply across projects
  • Communication style preferences
  • General workflow habits

Session/Task-scoped:

  • Current branch or ticket being worked on
  • Debugging context for an ongoing investigation
  • Temporary notes about a specific task

Recommended Memory Structure

Core Information Categories

Project Overview: Your behavioral guidelines and project purpose

  • Technology stack and architecture
  • Key directories and their purposes
  • Build/test/lint commands

Conventions: Project-specific rules and patterns

  • Commit message format
  • Code style preferences
  • PR process and review guidelines

User Preferences: User-specific information

  • Communication style preferences
  • Cross-project preferences
  • Working style and habits

Optional Categories (Create as Needed)

Current Task: Scratchpad for current work item context

  • Ticket ID, branch name, PR number
  • Relevant links and context
  • Current focus area

Decisions: Architectural decisions and their rationale

  • Why certain approaches were chosen
  • Trade-offs that were considered

Research Depth

You can ask the user if they want a standard or deep research initialization:

Standard initialization (~5-20 tool calls):

  • Scan README, package.json/config files, AGENTS.md, CLAUDE.md
  • Review git status and recent commits
  • Explore key directories and understand project structure
  • Create/update your memory to contain the essential information

Deep research initialization (~100+ tool calls):

  • Everything in standard initialization, plus:
  • Use TodoWrite to create a systematic research plan
  • Deep dive into git history for patterns, conventions, and context
  • Analyze commit message conventions and branching strategy
  • Explore multiple directories and understand architecture thoroughly
  • Search for and read key source files to understand patterns

What deep research can uncover:

  • Contributors & team dynamics: Who works on what areas? Who are the main contributors?
  • Coding habits: When do people commit? What's the typical commit size?
  • Writing & commit style: How verbose are commit messages? What conventions are followed?
  • Code evolution: How has the architecture changed? What major refactors happened?
  • Pain points: What areas have lots of bug fixes? What code gets touched frequently?

Research Techniques

File-based research:

  • README.md, CONTRIBUTING.md, AGENTS.md, CLAUDE.md
  • Package manifests (package.json, Cargo.toml, pyproject.toml, go.mod)
  • Config files (.eslintrc, tsconfig.json,.prettierrc)
  • CI/CD configs (.github/workflows/,.gitlab-ci.yml)

Git-based research (if in a git repo):

  • git log --oneline -20 - Recent commit history and patterns
  • git branch -a - Branching strategy
  • git log --format="%s" -50 | head -20 - Commit message conventions
  • git shortlog -sn --all | head -10 - Main contributors
  • Recent PRs or merge commits for context on ongoing work

How to Do Thorough Research

Don't just collect data - analyze and cross-reference it.

Shallow research (bad):

  • Run commands, copy output
  • Take everything at face value
  • List facts without understanding

Thorough research (good):

  • Cross-reference findings: If two pieces of data seem inconsistent, dig deeper
  • Resolve ambiguities: Don't leave questions unanswered
  • Read actual content: Don't just list file names - read key files to understand them
  • Look for patterns: What do the commit messages tell you about workflow?
  • Form hypotheses and verify: "I think this team uses feature branches" → check git branch patterns
  • Think like a new team member: What would you want to know on your first day?

Questions to ask yourself during research:

  • Does this make sense?
  • What's missing?
  • What can I infer?
  • Am I just listing facts, or do I understand the project?

The goal isn't to produce a report - it's to genuinely understand the project and how to be an effective collaborator.

Recommended Questions to Ask

Bundle these questions together when starting:

  1. Research depth: "Standard or deep research (comprehensive, as long as needed)?"
  2. Identity: "Which contributor are you?" (You can often infer from git logs)
  3. Related repos: "Are there other repositories I should know about?"
  4. Communication style: "Terse or detailed responses?"
  5. Any specific rules: "Rules I should always follow?"

What NOT to ask:

  • Things you can find by reading files ("What's your test framework?")
  • Permission for obvious actions - just do them
  • Questions one at a time - bundle them

Your Task

  1. Ask upfront questions: Bundle the recommended questions above
  2. Inspect existing context: See what already exists in AGENTS.md, README, etc.
  3. Identify the user: From git logs and their answers
  4. Research the project: Explore based on chosen depth. Use TodoWrite for a systematic plan.
  5. Document findings: Create notes or update project documentation
  6. Reflect and review: Check for completeness and quality
  7. Ask user if done: Check if they're satisfied or want you to continue

Reflection Phase

Before finishing, do a reflection step:

  1. Completeness check: Did you gather all relevant information?
  2. Quality check: Are there gaps or unclear areas?
  3. Structure check: Would this information make sense to your future self?

After reflection, summarize what you learned:

"I've completed the initialization. Here's a brief summary of what I set up: [summary]. Should I continue refining, or is this good to proceed?"

Remember: Good memory management is an investment. The effort you put into organizing your knowledge now will pay dividends as you work with this user over time.

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

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

windsurf

30.85%
按下载量换算37

OpenCode

21.49%
按下载量换算26

Codex

16.88%
按下载量换算20

Claude Code

13.57%
按下载量换算16

Antigravity

8.38%
按下载量换算10

Gemini CLI

3.91%
按下载量换算5

安全审计

暂无安全审计结果可展示。

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。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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