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trucontext-openclawtrucontext OpenClaw 搜索

Agent Skill

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。

总安装

3,810

周安装

162

GitHub Stars

公开资料未说明

下载量

1,335
OpenClaw

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

MIT-0

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: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

简介

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。

SKILL.md

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

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

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

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

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

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。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。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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