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savesave 搜索

Agent Skill

save 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

474

周安装

19

GitHub Stars

1

下载量

154
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

unknown

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:save(save 搜索)
来源仓库:https://github.com/laststance/skills
仓库路径:skills/save
安装命令:
npx skills add https://github.com/laststance/skills --skill save
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。

skills.shnpx skills
npx skills add https://github.com/laststance/skills --skill save

简介

save 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中快速定位候选结果。

  • 适用于内容保存、知识库构建等信息管理场景。
  • 通过关键词输入触发搜索,返回结构化结果列表供进一步处理。
  • 安装前需确认权限范围和维护状态,注意可能涉及联网和数据读取操作。
  • save 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

Session Save

Persist session context to Serena MCP memory for cross-session continuity.

<essential_principles>

Core Requirements

  • All persistence uses Serena MCP tools exclusively (no agent-specific tools)
  • Always check existing memories before writing to avoid overwriting valuable context
  • Session checkpoint keys must include date: session_YYYY-MM-DD_<description>
  • Pattern and learning memories use: pattern_<topic>
  • Report what was saved as a structured summary to the user

</essential_principles>

Phase 1: Session Analysis

  1. Review what was accomplished this session:

- Files created or modified - Decisions made and their rationale - Problems encountered and solutions found - Tasks completed and tasks remaining

  1. Identify what is worth persisting:

- Session state: Current progress, next steps, blockers - Learnings: Reusable patterns, solutions to problems - Plans: Active plans or updated plans - TODOs: Outstanding work items

Phase 2: Memory Inventory

  1. Call list_memories to see existing memories
  2. Check if project_overview needs updating (significant new understanding gained?)
  3. Identify which existing memories need updates vs. new memories to create

Phase 3: Persist Session Checkpoint

  1. Call write_memory with key session_YYYY-MM-DD_<summary>:
## Session: YYYY-MM-DD — <summary>

### Accomplished
- [what was done]

### Decisions Made
- [decision]: [rationale]

### Files Changed
- [file path]: [what changed]

### Pending / Next Steps
- [what remains to be done]

### Blockers (if any)
- [blocker description]

Phase 4: Persist Learnings (if any)

  1. For each reusable pattern discovered, call write_memory with key pattern_<topic>:
## Pattern: <name>

**Context**: [when this applies]
**Solution**: [the pattern/approach]
**Example**: [concrete example]
**When to Use**: [trigger conditions]
  1. For each persistent TODO, call write_memory with key todo_<description>:
## TODO: <description>

**Priority**: [high/medium/low]
**Context**: [why this matters]
**Acceptance Criteria**: [how to know it's done]

Phase 5: Update Project Overview (if needed)

  1. If significant new project understanding was gained:

- Call read_memory("project_overview") to get current content - Call write_memory("project_overview", updated_content) with additions - Do NOT overwrite existing content — append or update sections

Phase 6: Validation

  1. Verify: session checkpoint created, learnings persisted, no critical context lost

Phase 7: Save Report

Report to the user:

## Session Saved

### Memories Written
| Key | Purpose |
|-----|---------|
| session_YYYY-MM-DD_xxx | Session checkpoint |
| pattern_xxx | [if any] |
| todo_xxx | [if any] |

### Memories Updated
| Key | What Changed |
|-----|-------------|
| project_overview | [if updated] |

### Next Session
Run `/load` to restore this context.

Memory Naming Conventions

See references/memory-conventions.md for the complete naming reference.

Quick summary:

PrefixPurposeExample
project_overviewProject summary (singleton)project_overview
CRITICAL_*Must-read rulesCRITICAL_activation_rule
session_YYYY-MM-DD_*Session checkpointssession_2026-02-09_auth-flow
plan_*Active plansplan_dark-mode
pattern_*Reusable patternspattern_supabase-rls
discovery_*Brainstorming resultsdiscovery_api-options
todo_*Persistent TODOstodo_fix-login

Success Criteria

  • Session accomplishments analyzed
  • Existing memories checked (no accidental overwrites)
  • Session checkpoint memory written with date-stamped key
  • Learnings/patterns persisted (if any discovered)
  • Project overview updated (if significant new understanding)
  • Save report presented to user

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

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

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

33.53%
按下载量换算52

Codex

33.11%
按下载量换算51

Cursor

19.8%
按下载量换算30

Gemini CLI

8.65%
按下载量换算13

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

external-service

该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

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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