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rememberremember 搜索

Agent Skill

remember 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

840

周安装

34

GitHub Stars

152

下载量

264
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:remember(remember 搜索)
来源仓库:https://github.com/yonatangross/skillforge-claude-plugin
仓库路径:skills/remember
安装命令:
npx skills add https://github.com/yonatangross/skillforge-claude-plugin --skill remember
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/yonatangross/skillforge-claude-plugin --skill remember

简介

remember 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。

  • 适用于根据关键词、任务场景或来源线索进行信息搜集与整理的研究检索场景。
  • 通过 npx skills add 命令从 GitHub 仓库安装并使用。
  • 安装前需确认权限范围、维护状态,注意是否触发联网、命令执行或文件读写操作。
  • 建议结合原始 README 和仓库内容进一步核验具体用法和功能边界。

SKILL.md

Remember - Store Decisions and Patterns

Store important decisions, patterns, or context in the knowledge graph for future sessions. Supports tracking success/failure outcomes for building a Best Practice Library.

Argument Resolution

TEXT = "$ARGUMENTS"        # Full argument string, e.g., "We use cursor pagination"
FLAG = "$ARGUMENTS[0]"     # First token — check for --success, --failed, --category, --agent
# Parse flags from $ARGUMENTS[0], $ARGUMENTS[1] etc. (CC 2.1.59 indexed access)
# Remaining tokens after flags = the text to remember

Architecture

The remember skill uses knowledge graph as storage:

  1. Knowledge Graph: Entity and relationship storage via mcp__memory__create_entities and mcp__memory__create_relations - FREE, zero-config, always works

Benefits:

  • Zero configuration required - works out of the box
  • Explicit relationship queries (e.g., "what does X use?")
  • Cross-referencing between entities
  • No cloud dependency

Automatic Entity Extraction:

  • Extracts capitalized terms as potential entities (PostgreSQL, React, pgvector)
  • Detects agent names (database-engineer, backend-system-architect)
  • Identifies pattern names (cursor-pagination, connection-pooling)
  • Recognizes "X uses Y", "X recommends Y", "X requires Y" relationship patterns

Usage

Store Decisions (Default)

/ork:remember <text>
/ork:remember --category <category> <text>
/ork:remember --success <text>     # Mark as successful pattern
/ork:remember --failed <text>      # Mark as anti-pattern
/ork:remember --success --category <category> <text>

# Agent-scoped memory
/ork:remember --agent <agent-id> <text>         # Store in agent-specific scope
/ork:remember --global <text>                   # Store as cross-project best practice

Flags

FlagBehavior
(default)Write to graph
--successMark as successful pattern
--failedMark as anti-pattern
--category <cat>Set category
--agent <agent-id>Scope memory to a specific agent
--globalStore as cross-project best practice

Categories

  • decision - Why we chose X over Y (default)
  • architecture - System design and patterns
  • pattern - Code conventions and standards
  • blocker - Known issues and workarounds
  • constraint - Limitations and requirements
  • preference - User/team preferences
  • pagination - Pagination strategies
  • database - Database patterns
  • authentication - Auth approaches
  • api - API design patterns
  • frontend - Frontend patterns
  • performance - Performance optimizations

Outcome Flags

  • --success - Pattern that worked well (positive outcome)
  • --failed - Pattern that caused problems (anti-pattern)

If neither flag is provided, the memory is stored as neutral (informational).

Workflow

1. Parse Input

Check for --success flag → outcome: success
Check for --failed flag → outcome: failed
Check for --category <category> flag
Check for --agent <agent-id> flag → agent_id: "ork:{agent-id}"
Check for --global flag → use global user_id
Extract the text to remember
If no category specified, auto-detect from content

2. Auto-Detect Category

KeywordsCategory
chose, decided, selecteddecision
architecture, design, systemarchitecture
pattern, convention, stylepattern
blocked, issue, bug, workaroundblocker
must, cannot, required, constraintconstraint
pagination, cursor, offset, pagepagination
database, sql, postgres, querydatabase
auth, jwt, oauth, token, sessionauthentication
api, endpoint, rest, graphqlapi
react, component, frontend, uifrontend
performance, slow, fast, cacheperformance

3. Extract Lesson (for anti-patterns)

If outcome is "failed", look for:

  • "should have", "instead use", "better to"
  • If not found, prompt user: "What should be done instead?"

4-6. Extract Entities and Create Graph

Extract entities (Technology, Agent, Pattern, Project, AntiPattern) from the text, detect relationship patterns ("X uses Y", "chose X over Y", etc.), then create entities and relations in the knowledge graph.

Load entity extraction rules, type assignment, relationship patterns, and graph creation examples: Read("${CLAUDE_SKILL_DIR}/references/graph-operations.md")

7. Confirm Storage

Display confirmation using the appropriate template (success, anti-pattern, or neutral) showing created entities, relations, and graph stats.

Load output templates and examples: Read("${CLAUDE_SKILL_DIR}/references/confirmation-templates.md")

File-Based Memory Updates

When updating .claude/memory/MEMORY.md or project memory files:

  • PREFER Edit over Write to preserve existing content and avoid overwriting
  • Use stable anchor lines: ## Recent Decisions, ## Patterns, ## Preferences
  • See the memory skill's "Permission-Free File Operations" section for the full Edit pattern
  • This applies to the calling agent's file operations, not to the knowledge graph operations above

References

Load on demand with Read("${CLAUDE_SKILL_DIR}/references/<file>"):

FileContent
category-detection.mdAuto-detection rules for categorizing memories (priority order)
graph-operations.mdEntity extraction, type assignment, relationship patterns, graph creation
confirmation-templates.mdOutput templates (success, anti-pattern, neutral) and usage examples

Related Skills

  • ork:memory - Search, load, sync, visualize (read-side operations)

Error Handling

  • Knowledge graph unavailable → show configuration instructions
  • Empty text → ask user for content; text >2000 chars → truncate with notice
  • Both --success and --failed → ask user to clarify
  • Entity extraction fails → create generic Decision entity; relation fails → create entities first, retry

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

26.63%
按下载量换算70

OpenCode

24.15%
按下载量换算64

Antigravity

17.19%
按下载量换算45

Gemini CLI

13.18%
按下载量换算35

windsurf

8.24%
按下载量换算22

trae

3.07%
按下载量换算8

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。

来源信息

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