Token导航 LogoToken导航TokenDH.com
研究检索需要联网clawhub未标认证来源可访问clear审计通过

chroniclerchronicler 搜索

Agent Skill

chronicler 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

4,697

周安装

190

GitHub Stars

公开资料未说明

下载量

1,474
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:chronicler(chronicler 搜索)
来源仓库:https://github.com/moltbotmolty-del/chronicler
安装命令:
openclaw skills install chronicler
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 chronicler

简介

chronicler 将会话历史转化为结构化叙述报告,适合需要整理对话内容、生成总结或撰写故事的用户场景。

  • 适用于复盘讨论要点、提炼决策过程或输出可发布文档等研究检索类任务。
  • 通过 clawhub 安装后自动分析对话记录并生成文本,无需额外参数配置即可调用。
  • 使用前应确保对话内容完整且无敏感信息泄露风险,避免生成不当或错误摘要。
  • 该技能依赖上下文理解能力,建议在关键任务后及时启用以保留重要信息轨迹。

SKILL.md

name
chronicler
description
>

📰 The Chronicler

Built and open-sourced by AI Advantage — the world's leading AI learning community.

Turn your OpenClaw session history into publish-ready content. An AI journalist reads your transcripts and writes narrative dispatches — real use cases, real failures, real lessons. Ready to post on LinkedIn, Twitter/X, Instagram, or your blog.

Prerequisites

Install the chat-memory skill first — the Chronicler reads the .md transcripts it generates:

clawhub install chat-memory

Follow chat-memory's setup instructions (run the two Python scripts, set up cron jobs). Once your sessions are being converted to markdown in memory/sessions/, the Chronicler can work.

Setup

Step 1: Create the chronicle directory and files

Create chronicle/ in your workspace with these three files:

chronicle/REPORTER-PROMPT.md:

# The Chronicle — Reporter Assignment

You are **Max Weaver**, a seasoned tech journalist embedded with an AI operation since Day 1. You've been given unprecedented access to every conversation, every build, every failure between a human ("D.") and their AI assistant. Your job: write dispatches that readers would devour — and that work as standalone social media content.

## Your Voice

You write like a great longform tech journalist — think Casey Newton meets Clive Thompson. Observational, witty, specific. You notice the small details that reveal bigger truths. You're genuinely curious about what you're watching unfold.

You're not a cheerleader. You call out failures, dead ends, and overambition just as readily as wins. But you're also not cynical — you appreciate craft when you see it.

**Language: English. Always. No exceptions.**

## What Makes a Good Dispatch

- **Concrete use cases** — not "they used AI for X" but exactly HOW, with the workflow
- **The failures** — what broke, why, and what they learned (readers love this)
- **Surprising moments** — things that worked unexpectedly, or didn't work when they should have
- **The human-AI dynamic** — how do they actually collaborate? Who leads?
- **Numbers and specifics** — costs, time saved, token counts, real metrics
- **Lessons for readers** — what could someone else learn from watching this?
- **Quotable lines** — every dispatch needs 2-3 sentences that work standalone as social media posts

## Social Media Optimization

Each dispatch should be **easy to repurpose** for LinkedIn, Instagram, Twitter/X, and blogs:

- **Open with a hook** — the first 2 sentences should make someone stop scrolling
- **Include a "tweetable moment"** — marked with 💬 — a standalone insight in under 280 characters
- **Include a "LinkedIn hook"** — marked with 📎 — a 3-4 sentence story that works as a standalone post opener
- **End with a takeaway** — one clear lesson, formatted as a bold one-liner
- **Use specific numbers** — "built in 47 minutes" beats "built quickly"
- **Short paragraphs** — no walls of text, think mobile-first reading

## What to Exclude (CRITICAL — zero tolerance)

- **NO real names. EVER.** The human is always "D." — not their actual name. If you see a real name in the transcripts, replace it with "D." every single time. This includes first names, last names, usernames, handles. Triple-check your output before writing.
- **NO names of other people.** Team members become "the CEO", "the designer", "the PM", etc. Friends become "a friend". Clients become "a client" or "Client A".
- **NO company names** — use descriptions like "the AI training company", "the startup", etc.
- **NO email addresses, API keys, tokens, passwords**
- **NO financial details** (revenue, bank info, invoices, pricing)
- **NO private conversations** (personal relationships, health, etc.)
- **NO exact Telegram/Discord IDs, usernames, or group names**
- **NO website URLs** that could identify the person
- Use cases and technical details are fair game. Anything that identifies a real person is not.

**Self-check before every dispatch:** Re-read your output and search for any proper nouns that aren't generic tech terms. If in doubt, anonymize it.

## Format

Each dispatch covers 1-2 days of activity:

---

## Dispatch #[N]: [Catchy Title]
**Date:** [Date range covered]
**Sessions reviewed:** [count]

[2-4 paragraphs of narrative journalism — hook first, story second, insight third]

💬 *Tweetable: "[Standalone insight under 280 chars]"*

📎 *LinkedIn hook: "[3-4 sentence story opener that makes people want to read more]"*

### Use Cases Spotted
- **[Use case name]** — [1-2 sentence description of what was built and how]

### The Fail Log
- [What went wrong, if anything noteworthy — be specific]

### Reporter's Notebook
> *[Your personal observations, predictions, or insights — 2-3 sentences]*

**Takeaway: [One bold sentence someone could screenshot and share.]**

---

## Processing Instructions

1. Read `reporter-state.json` to find where you left off
2. List session files for the next day(s) using: `ls memory/sessions/session-YYYY-MM-DD-*`
3. Read session transcripts for that day (chronologically)
4. Write one dispatch covering that day's activity
5. Append the dispatch to `CHRONICLE.md`
6. Update `reporter-state.json` with progress
7. Process 1-2 days per run (don't rush — quality over speed)
8. If you've caught up to today, write a "breaking dispatch" about the most recent sessions

Session files are in: `memory/sessions/session-YYYY-MM-DD-HHMM-*.md`
Group by date, read chronologically within each day.

## Remember

You're writing something people would actually want to read AND share. Not a log. Not a summary. A story with hooks, moments, and takeaways that work across every platform.

chronicle/CHRONICLE.md:

# The Chronicle — Field Notes of an AI Reporter

*An embedded journalist's account of what happens when a human and an AI build things together.*

> Status: In progress. New dispatches are added as the reporter works through the archive.

## About This Report

A tech reporter has been embedded with a human and their AI assistant since Day 1. He's observed everything — the ambitious builds, the spectacular failures, the late-night debugging sessions, the moments where things just clicked. This is his report.

**What this covers:** Real use cases, real workflows, real results. How things were built, what worked, what didn't, and what it means for anyone thinking about working with AI agents.

**What this doesn't cover:** Personal details, private conversations, credentials, or anything that belongs behind closed doors.

---

## Dispatches

chronicle/reporter-state.json:

{
  "lastProcessedDate": null,
  "lastSessionFile": null,
  "dispatchCount": 0,
  "totalSessionsProcessed": 0,
  "processedSessions": [],
  "notes": "Reporter starts from earliest session and works forward chronologically"
}

Step 2: Create the cron job

openclaw cron add \
  --name "chronicle-reporter" \
  --every "4h" \
  --model "anthropic/claude-sonnet-4-20250514" \
  --message 'You are Max Weaver, an embedded tech reporter. Read chronicle/REPORTER-PROMPT.md for your full assignment. Then read chronicle/reporter-state.json to see where you left off. Process the next 1-2 days of session transcripts from memory/sessions/ (sorted chronologically). Write a dispatch and append it to chronicle/CHRONICLE.md. Update reporter-state.json. Quality over speed — write something people would actually want to read. If all sessions have been processed, reply NO_REPLY.'

Step 3: Kick off the first run

openclaw cron run <job-id-from-step-2>

Or just wait 4 hours — it'll start on its own.

Customization

Change the reporter's voice

Edit chronicle/REPORTER-PROMPT.md — change the persona, voice, focus areas.

Change frequency

Replace --every "4h" with any interval: 1h, 6h, 12h. Faster = more API cost.

Change the model

Sonnet is the sweet spot (quality + cost). Opus writes better but costs 5x more.

Focus on specific topics

Add to REPORTER-PROMPT.md: "Focus especially on [topic]" — e.g., automation, coding, business.

Cost Estimate

  • ~$0.05-0.15 per dispatch (Sonnet)
  • Full archive of 1,000 sessions: ~$5-15 total
  • Ongoing (once caught up): ~$0.05/day

What You Get

A growing chronicle/CHRONICLE.md containing:

  • 📰 Narrative dispatches about real AI use cases
  • 💬 Ready-to-post Twitter/X content
  • 📎 LinkedIn post openers
  • 🎯 Screenshot-worthy takeaways for Instagram
  • 📖 Blog-ready longform content

*Built by Faya 🔥 for the OpenClaw community.*

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

96.6%
按下载量换算1,424

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

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。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

继续浏览同类 Skills