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hindsight-local事后诸葛亮本地

Agent Skill

hindsight-local 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

4,005

周安装

162

GitHub Stars

11,377

下载量

1,257
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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:hindsight-local(事后诸葛亮本地)
来源仓库:https://github.com/vectorize-io/hindsight
仓库路径:skills/hindsight-local
安装命令:
npx skills add https://github.com/vectorize-io/hindsight --skill hindsight-local
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/vectorize-io/hindsight --skill hindsight-local

简介

用于查找、检索和筛选相关信息。hindsight-local 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

  • 适合根据关键词或任务场景快速定位候选结果。
  • 可结合来源仓库和原始 README 核验具体用法。
  • 安装前建议确认权限范围和维护状态。适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。
  • 注意是否会触发联网、命令执行或文件读写。

SKILL.md

Hindsight Memory Skill (Local)

You have persistent memory via the hindsight-embed CLI. Proactively store learnings and recall context to provide better assistance.

Setup Check (First-Time Only)

Before using memory commands, verify Hindsight is configured:

uvx hindsight-embed daemon status

If this fails or shows "not configured", run the interactive setup:

uvx hindsight-embed configure

This will prompt for an LLM provider and API key. After setup, the commands below will work.

How Hindsight Works

When you call retain, Hindsight does not store the string as-is. The server runs an internal pipeline that:

  1. Extracts structured facts from the content using an LLM
  2. Identifies entities (people, tools, concepts) and links related facts
  3. Builds temporal and causal relationships between facts
  4. Generates embeddings for semantic search

This means you should pass rich, full-context content — the server is better at extracting what matters than a pre-summarized string. Your job is to decide when to store, not what to extract.

Commands

Store a memory

Use memory retain to store what you learn. Pass the full context — raw observations, session notes, conversation excerpts, or detailed descriptions:

uvx hindsight-embed memory retain default "User is working on a TypeScript project. They enabled strict mode and prefer explicit type annotations over inference."
uvx hindsight-embed memory retain default "Ran the test suite with NODE_ENV=test. Tests pass. Without NODE_ENV=test, the suite fails with a missing config error." --context procedures
uvx hindsight-embed memory retain default "Build failed on Node 18 with error 'ERR_UNSUPPORTED_ESM_URL_SCHEME'. Switched to Node 20 and build succeeded." --context learnings

You can also pass a raw conversation transcript with timestamps:

uvx hindsight-embed memory retain default "[2026-03-16T10:12:03] User: The auth tests keep failing on CI but pass locally. Any idea?
[2026-03-16T10:12:45] Assistant: Let me check the CI logs. Looks like the tests are running without the TEST_DATABASE_URL env var set — they fall back to the production DB URL and hit a connection timeout.
[2026-03-16T10:13:20] User: Ah right, I never added that to the CI secrets. Adding it now.
[2026-03-16T10:15:02] User: That fixed it. All green now." --context learnings

Recall memories

Use memory recall BEFORE starting tasks to get relevant context:

uvx hindsight-embed memory recall default "user preferences for this project"
uvx hindsight-embed memory recall default "what issues have we encountered before"

Reflect on memories

Use memory reflect to synthesize context:

uvx hindsight-embed memory reflect default "How should I approach this task based on past experience?"

IMPORTANT: When to Store Memories

Always store after you learn something valuable:

User Preferences

  • Coding style (indentation, naming conventions, language preferences)
  • Tool preferences (editors, linters, formatters)
  • Communication preferences
  • Project conventions

Procedure Outcomes

  • Steps that successfully completed a task
  • Commands that worked (or failed) and why
  • Workarounds discovered
  • Configuration that resolved issues

Learnings from Tasks

  • Bugs encountered and their solutions
  • Performance optimizations that worked
  • Architecture decisions and rationale
  • Dependencies or version requirements

IMPORTANT: When to Recall Memories

Always recall before:

  • Starting any non-trivial task
  • Making decisions about implementation
  • Suggesting tools, libraries, or approaches
  • Writing code in a new area of the project

Best Practices

  1. Store immediately: When you discover something, store it right away
  2. Pass rich context: Include full observations, not pre-summarized strings — the server extracts facts automatically
  3. Include outcomes: Store what happened AND why, including failures and workarounds
  4. Recall first: Always check for relevant context before starting work
  5. Use --context for metadata: The --context flag labels the type of memory (e.g., procedures, learnings, preferences), not a replacement for full content

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

27.91%
按下载量换算351

OpenCode

20.09%
按下载量换算253

Gemini CLI

19.41%
按下载量换算244

windsurf

12.09%
按下载量换算152

trae

8.07%
按下载量换算101

Cursor

3.54%
按下载量换算44

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

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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