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Agent Skill · 研究检索

trucontext-openclaw(trucontext OpenClaw 搜索)

trucontext-openclaw 用于查找、检索和筛选相关信息,适合在 OpenClaw 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。

trucontext-openclaw 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为调用外部服务;许可证为 MIT-0,第三方扫描有提醒项,按安装量在研究检索类的 73,675 个 Skill 里排第 25,257。

总安装 3,810周安装 162GitHub Stars 公开资料未说明下载 1,335核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT-0
版本
0.1.9

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:trucontext-openclaw(trucontext OpenClaw 搜索)
来源仓库:https://github.com/alphacollectivellc/trucontext-openclaw
安装命令:
openclaw skills install trucontext-openclaw
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 OpenClaw 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。

ClawHubOpenClaw
openclaw skills install trucontext-openclaw

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

管理 OpenClaw 代理的持久化上下文记忆,支持跨会话知识留存。当需要回顾历史对话或检索先前存储的关键信息时启用。采用结构化存储方式,可按标签或关键词快速定位内容。需注意数据隐私边界,避免泄露用户敏感上下文片段。建议定期清理过期条目以优化内存占用与响应效率。

trucontext-openclaw 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 OpenClaw;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 openclaw skills install trucontext-openclaw;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要调用外部服务,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

trucontext-openclaw 怎么安装?

在终端执行 openclaw skills install trucontext-openclaw。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

trucontext-openclaw 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

trucontext-openclaw 需要什么权限?

权限级别是调用外部服务。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。

trucontext-openclaw 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

trucontext-openclaw 用的人多吗?

公开数据是累计安装 3,810 次,近一周安装 162 次,下载 1,335 次。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

trucontext-openclaw 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。

先想清楚的地方

  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 OpenClaw,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
trucontext-openclaw

trucontext OpenClaw 搜索 · 本页

3,810—调用外部服务MIT-0有提醒

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

name
trucontext-openclaw
description
TruContext persistent memory for OpenClaw agents. Use when you need to remember something significant across sessions, recall prior context, query the knowledge graph, check what TC is curious about, or declare entity nodes. Triggers on: 'remember this', 'recall what we know about', 'check TC', 'what has TC flagged', 'create a node for', 'find the node for'.
homepage
https://trucontext.ai
metadata
{"openclaw": {"emoji": "🧠", "homepage": "https://trucontext.ai", "requires": {"bins": ["trucontext", "python3"]}, "install": [{"id": "npm-trucontext-openclaw", "kind": "node", "package": "trucontext-openclaw", "bins": ["trucontext-openclaw"], "label": "Install trucontext-openclaw (npm) — includes TC CLI setup"}]}}

trucontext-openclaw

Your persistent memory layer. All TC operations go through this skill. Never call the trucontext CLI directly — use the tc-memory verbs below.

If tc-memory is not found, run: trucontext-openclaw install

What this skill reads

  • ~/.trucontext/openclaw-state.json — agent config written by trucontext-openclaw install. Contains your root node ID, user root node ID, recipe, and workspace path. No secrets.
  • TruContext CLI auth (~/.trucontext/credentials.json) — the trucontext CLI manages its own auth tokens. This skill calls the CLI; it does not read or store credentials directly. To authenticate, run: npx trucontext login.

Verbs

# Remember something significant (narrative, not summary)
tc-memory ingest "<narrative text>" [--permanent]

# Retrieve relevant context before a decision or conversation
tc-memory recall "<query>" [--limit N]

# Ask the graph a natural language question
tc-memory query "<question>" [--limit N]

# What gaps has TC identified in your graph?
tc-memory gaps

# What is TC's intelligence layer reporting about your recipe alignment?
tc-memory health

# Find an existing node before creating a new one
tc-memory node find "<name>"

# Create a new entity node (only after find returns no match)
tc-memory node create --type <type> --id <slug> --name "<display name>" [--permanent]

# Look up a node by ID
tc-memory node get <id>

# Create an explicit edge between two nodes
tc-memory node link <id> --rel <RELATIONSHIP> --to <id2>

Node integrity rule

Always call node find before node create. If a match is returned with confidence > 0.8, use the existing node ID. Only create if no match found. This prevents duplicate nodes across sessions.

Session startup

At the start of every session, call:

tc-memory recall "active projects and entities relevant to my current work"

This gives you node IDs to anchor ingests during the session.

Ingest protocol — testify, don't summarize

TC's intelligence layer pattern-matches across ingests. Pre-digested conclusions starve it.

Before ingesting, ask: *If TC's intelligence layer read only this, could it learn something the entity didn't explicitly say?*

If yes — it's signal. Submit it. If no — rewrite it. Find the friction. Find the turn. Find the moment before you knew the answer.

The three layers:

  1. What happened (facts, outcome)
  2. How it happened (process, friction, pivots) ← most signal lives here
  3. What it revealed (character, pattern, relationship dynamic) ← what TC is hungry for

Write in first person, past tense, with friction.

Examples of good vs. bad ingests:

❌ Bad: tc-memory ingest "Fixed the MCP server issue. Used low-level SDK."

✅ Good: tc-memory ingest "The higher-level SDK was injecting taskSupport:forbidden into tool schemas — Claude Desktop was silently filtering the tools out because of it. No error. Just absence. Three hours of looking in the wrong places before pulling the raw protocol response and finding it. The fix was ten minutes. The three hours were spent not knowing what question to ask."

Temporal vs. permanent

  • --permanent for facts: events that happened, decisions made, entities created
  • Default (temporal) for: behavioral observations, inferences, preferences, patterns

Config (resolved from ~/.trucontext/openclaw-state.json)

Your root node, user root, recipe, and primary_about are pre-configured by trucontext-openclaw install. You do not need to pass them on every call.

Source

  • Homepage: https://trucontext.ai
  • Source: https://github.com/AlphaCollectiveLLC/trucontext-openclaw
  • npm: https://www.npmjs.com/package/trucontext-openclaw

平台分布

OpenClaw

78.07%
按下载量换算1,042

安全审计

VirusTotal

可疑

ClawScan

通过

Static analysis

通过

权限和风险

调用外部服务

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。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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