编程

Insight Tracker

试用

为会话中的观察、规律和发现建立带标签、日期与生命周期的可检索记录。

它能做什么

把工作中浮现的观察、模式和心得作为独立条目记录下来,每条附带标签(pattern、learning、decision、risk、opportunity、user-preference、technical、process)、优先级以及生命周期状态(candidate、active、validated、contradicted、archived)。写入前会先排查重复、登记来源与证据等级(从 observed-once 到 user-confirmed),并要求措辞可被证伪。产出包括带日期的 Markdown 记录、按标签筛选的清单、关键词检索结果、跨条目关联报告,以及含趋势聚类的周报。

什么时候用它

  • 记录会话中发现的用户偏好
  • 决策前检索历史观察
  • 清理归档陈旧条目
  • 查看周报趋势聚类

技能文档

Insight Tracker

🚀 First-Success Path (30秒上手)

记录一条洞察:

insight add "用户更喜欢简洁的回复风格,长篇大论反而降低满意度" --tags user-preference,learning --priority high

查询已有洞察:

insight list --tag user-preference

三步即用:添加 → 打标签 → 随时检索。

Sample Prompts(输入→输出示例)

Sample 1: 记录新洞察

用户输入:

记一下:我发现当回复中包含具体代码示例时,用户的满意度明显更高

Expected Output:

✅ 洞察已记录

INS-0042: 包含具体代码示例的回复满意度更高
Tags: learning, pattern
Priority: medium
Status: active

是否需要关联已有洞察或添加更具体的标签?

Sample 2: 按标签检索洞察

用户输入:

查一下所有关于用户偏好的高优先级洞察

Expected Output:

📋 高优先级洞察 (标签: user-preference)

  1. INS-0010 | 用户更倾向中文回复,即使提问是英文 | 2026-04-10
  2. INS-0022 | 用户不喜欢重复确认,偏好直接执行 | 2026-05-03
  3. INS-0035 | 工作日晚上用户响应最快 | 2026-05-28
  4. INS-0042 | 代码示例提升满意度 | 2026-06-01

共 4 条。是否需要导出或进一步分析趋势?

Sample 3: 关联分析

用户输入:

把这些洞察按主题分组,看看有什么规律

Expected Output:

📊 洞察主题聚类

主题:回复风格偏好(4条相关)

  • 用户偏好简洁回复
  • 代码示例提升满意度
  • 分点式比段落式更受欢迎
  • 避免过度礼貌用语

主题:响应时机(3条相关)

  • 工作日晚上响应最快
  • 周末上午适合推送
  • 重复确认降低响应意愿

💡 建议:这几个主题可以整理成一份"用户交互指南"供后续参考。

Sample 4: 归档陈旧洞察

用户输入:

INS-0003 那个洞察已经过时了,归档吧

Expected Output:

✅ INS-0003 已归档 原内容:用户反馈图片加载较慢(2025-12-01创建) 归档原因:已确认该问题已修复,不再相关

Sample 5: 生成洞察摘要

用户输入:

帮我总结一下这周记录的所有洞察

Expected Output:

📅 本周洞察摘要(2026-06-09 ~ 2026-06-16)

新增洞察: 5条

  • 高优先级:2条
  • 中优先级:2条
  • 低优先级:1条

热门标签: pattern(3), learning(2), user-preference(2)

值得关注:

  • INS-0042:代码示例提升满意度(已验证2次)
  • INS-0043:傍晚时分搜索意图最明确(待验证)

是否需要将本周高价值洞察同步到知识库?

Overview

This skill provides structured insight management for OpenClaw workflows. It captures observations, patterns, and learnings that emerge during sessions, making them searchable, referenceable, and actionable.

Quality Upgrade: Evidence Lifecycle

Treat every new insight as a claim with a lifecycle, not as a finished fact.

Status Values

  • candidate: plausible but not yet checked against repeated evidence.
  • active: useful enough to influence current work.
  • validated: supported by repeated observations, direct feedback, or measured outcomes.
  • contradicted: later evidence challenges the claim.
  • archived: no longer useful, stale, or merged into a better insight.

Evidence Grades

  • observed-once: one session, one event, or one user comment.
  • repeated: seen across multiple sessions or examples.
  • measured: backed by logs, counts, or explicit evaluation.
  • user-confirmed: confirmed directly by the user.

Recording Gate

Before adding a new insight:

  1. Search for near-duplicates and possible conflicts.
  2. Capture the source, date, and evidence grade.
  3. Keep the wording falsifiable; avoid broad personality claims from one event.
  4. If the insight affects future agent behavior, mark what behavior should change.
  5. Ask before exporting to a second-brain or other persistent knowledge base.

Preferred output:

Insight: 
Evidence grade: 
Status: 
Related/conflicting insights: 
Behavior change: 

When to Use

Use this skill when:

  • A user mentions "note this down" or "remember this insight"
  • Patterns emerge across multiple sessions that should be tracked
  • Research or analysis reveals findings worth preserving
  • The user asks to "track" or "record" something for future reference
  • Cross-referencing insights with existing knowledge is needed

Core Concepts

Insight

An insight is a discrete observation, pattern, or learning with the following attributes:

  • Content: The actual observation or finding
  • Source: Where it came from (session, research, user input)
  • Tags: Categories for organization
  • Priority: Importance level (high/medium/low)
  • Status: Current state (active/validated/archived)
  • Created: Timestamp
  • References: Links to related insights or knowledge

Tags

Standard tags for categorization:

  • pattern: Recurring behaviors or structures
  • learning: New understanding or skill acquired
  • decision: Choices made and their rationale
  • risk: Potential issues or concerns
  • opportunity: Potential improvements or wins
  • user-preference: User-specific preferences
  • technical: Technical findings or constraints
  • process: Workflow or methodological insights

Input

Accepts insights in various formats:

  • Direct text input
  • Session transcript excerpts
  • Memory file references
  • Research findings

Output

Produces:

  • Dated insight records (Markdown)
  • Tagged insight summaries
  • Cross-reference reports
  • Insight search results

Workflow

Recording an Insight

  1. Capture: Extract the core observation
  2. Tag: Apply relevant category tags
  3. Prioritize: Mark importance level
  4. Link: Connect to related insights
  5. Store: Save to dated record

Retrieving Insights

  1. Search: By tag, keyword, date, or priority
  2. Filter: Narrow by status or category
  3. Present: Show matching insights with context

Analyzing Insights

  1. Cluster: Group related insights
  2. Trend: Identify patterns over time
  3. Validate: Check against new evidence
  4. Archive: Mark outdated insights

Commands

Add Insight

insight add "Content of the insight" --tags pattern,learning --priority high

List Insights

insight list --tag pattern --since 2024-01-01

Search Insights

insight search "keyword" --priority high

Show Insight

insight show 

Archive Insight

insight archive 

Output Format

Dated Insight Record

# Insights - YYYY-MM-DD

## New Insights

### INS-001: Title
- **Content**: The insight content
- **Source**: Session/user/research
- **Tags**: pattern, learning
- **Priority**: high
- **Status**: active
- **Created**: 2024-01-15T10:30:00Z
- **References**: INS-003, knowledge-distillation-2024-01-10

## Summary
- Total insights: 5
- High priority: 2
- New tags: user-preference

Quality Rules

  • Be specific: avoid vague generalizations
  • Include source: always note where insight came from
  • Tag consistently: use standard tags
  • Link related insights: build knowledge networks
  • Review regularly: archive outdated insights
  • Prioritize honestly: not everything is high priority

Good Trigger Examples

  • "Track this insight: users prefer X over Y"
  • "Note down that we discovered a pattern in Z"
  • "Search for insights about deployment issues"
  • "Show me all high priority insights from last week"
  • "Archive insight INS-005, it's no longer relevant"

真实任务示例

场景用户会说Skill 执行
记录发现"记下来,用户更喜欢分步确认"创建洞察 → 打标签 → 设置优先级
回顾分析"之前有没有分析过类似模式"按标签搜索 → 列出相关洞察 → 关联分析
趋势发现"看看最近在哪些方面有重复模式"聚类分析 → 统计标签频率 → 提取趋势
知识清理"帮我把过时的洞察清理一下"列出旧洞察 → 确认归档 → 保持数据库整洁
报告生成"把关于 deployment 的洞察整理成报告"过滤相关条目 → 格式化为报告 → 输出摘要

Resources

references/

  • references/tag-taxonomy.md: Full tag definitions and usage guidelines
  • references/output-examples.md: Sample insight records

常见问题

一条洞察会经历哪些状态?
五种状态:candidate、active、validated、contradicted、archived。每条同时附带一个证据等级,从 observed-once 一直到 user-confirmed。
不同洞察之间可以互相引用吗?
可以。每条记录设有 References 字段,用来关联相关或冲突的洞察(例如 INS-003、knowledge-distillation-2024-01-10)。
会自动导出到外部知识库吗?
不会。录入流程要求在导出到 second-brain 或其他持久知识库前先获得用户确认。

相关技能

在本地磁盘以分类纯 Markdown 文件保存需要长期留存的事实,与智能体内置记忆并存。

作者 Iván559 次安装18 星标

把自然语言描述转为结构化 JSON,并由 mcp-diagram-generator MCP 服务生成 Draw.io、Mermaid 或 Excalidraw 图表文件。

作者 nssa.io1.0k 次安装47 星标

诊断生产力系统反复失效的根因,给出最小干预——容量测算、瓶颈定位、可靠的本地记录。

作者 Iván855 次安装69 星标

从 AdMapix API 拉取广告创意、应用、榜单和收入预估等数据,原样返回结构化 JSON。

作者 fly0pants4.3k 次安装296 星标

以 AI 机器人身份加入视频会议,提供语音、虚拟形象与屏幕共享四种模式。

作者 johnpatternai22 次安装8 星标

harrylabsj 的更多技能

浏览全部技能

淘宝与京东、拼多多、唯品会同款比价,输出可执行的购买建议。

作者 harrylabsj52 次安装2 星标

根据美团可见的配送费、包装费、满减门槛、ETA、评价等公开信息,给出一个明确的下单、换店、凑单、再等等或不点的判断。

作者 harrylabsj44 次安装1 星标

在下单前拆解拼多多低价:百亿补贴、拼团门槛、店铺风险、退货条款一次过。

作者 harrylabsj34 次安装1 星标

定位技能瓶颈,把练习日志转化为可执行的下一次训练方案。

作者 harrylabsj35 次安装

用通俗中文解读常规体检和化验报告,标注异常、分清轻重、给出下一步动作。

作者 harrylabsj27 次安装

为盒马鲜生购物规划清单、评估鲜度、推荐配送时段,并整理手动下单步骤。

作者 harrylabsj23 次安装