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Agent Skill · 研究检索

agentledger-decision-log(Agent 账本决策日志)

agentledger-decision-log 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。

agentledger-decision-log 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为需要联网;许可证为 MIT-0,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 12,078。

总安装 10,584周安装 450GitHub Stars 公开资料未说明下载 3,708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
1.0.2

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:agentledger-decision-log(Agent 账本决策日志)
来源仓库:https://github.com/clawdssen/agentledger-decision-log
安装命令:
openclaw skills install agentledger-decision-log
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 agentledger-decision-log

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。

agentledger-decision-log 是面向个体企业家的 AI 决策日志工具,用于记录和回顾关键决策。它捕捉决策背景、理由与预期结果,支持事后复盘以优化未来判断。适用于需要系统化追踪商业选择并从中学习的管理场景。

安装命令为 openclaw skills install agentledger-decision-log,需验证权限设置。注意该项目可能涉及本地文件操作,建议提前确认存储路径和数据安全策略。

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 agentledger-decision-log;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

    它需要需要联网,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

agentledger-decision-log 怎么安装?

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

agentledger-decision-log 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。

agentledger-decision-log 需要什么权限?

权限级别是需要联网。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。

agentledger-decision-log 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。

agentledger-decision-log 用的人多吗?

公开数据是累计安装 10,584 次,近一周安装 450 次,下载 3,708 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。

agentledger-decision-log 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

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。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 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。
  • 需要许可证清楚、并且有第三方扫描结果的团队项目。

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
agentledger-decision-log

Agent 账本决策日志 · 本页

10,584—需要联网MIT-0通过

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
decision-log
version
1.0.2
description
AI-powered decision journal for solopreneurs — capture decisions with context, rationale, and expected outcomes, then review them later to learn from what you got right and wrong.
tags
[decisions, journal, learning, accountability, retrospective, strategy, solopreneur, judgment, decision-making, review]
platforms
[openclaw, cursor, windsurf, generic]
category
productivity
author
The Agent Ledger
license
CC-BY-NC-4.0
url
https://github.com/theagentledger/agent-skills

Decision Log

Stop making the same expensive mistakes. Start learning from the ones you've already made.

by The Agent Ledger — theagentledger.com


The Problem

Most solopreneurs make dozens of meaningful decisions every week — which products to build, which clients to take, which tools to use, which strategies to pursue. They almost never write them down. Six months later, when a decision goes sideways, the context is gone: what were the alternatives? What did you expect to happen? Why did you choose this over that?

The result: you can't learn systematically from your own experience. You repeat mistakes. You forget what worked and why.

This skill turns your agent into a decision journal. Capture any decision in seconds. Get a structured record with context, alternatives, expected outcomes, and confidence level. Then, on a schedule you set, your agent reviews past decisions against what actually happened and surfaces patterns in your judgment.


Setup

Step 1 — Create Your Decisions Directory

mkdir -p decisions/

Step 2 — Create Your Decision Index

Create decisions/INDEX.md:

# Decision Log

**Started:** [date]
**Total decisions logged:** 0
**Last review:** —

---

## Open Decisions (awaiting outcome)

| ID | Date | Decision | Category | Expected Outcome | Review Date |
|----|------|----------|----------|-----------------|-------------|
| — | — | — | — | — | — |

---

## Reviewed Decisions

| ID | Date | Decision | Category | Outcome | Verdict | Lesson |
|----|------|----------|----------|---------|---------|--------|
| — | — | — | — | — | — | — |

---

## Patterns (updated during reviews)

*Will be filled in by your agent after the first batch of reviews.*

Step 3 — Create Your First Decision Entry

Each decision gets its own file: decisions/YYYY-MM-DD-short-title.md

Template:

# Decision: [Short Title]

**ID:** DEC-[001]
**Date:** [YYYY-MM-DD]
**Category:** [business / product / operations / financial / personal / hiring / strategy]
**Status:** open

---

## The Decision

[What did you decide? Be specific. One sentence if possible.]

---

## Context

[What was happening when you made this decision? What problem were you solving? What constraints existed?]

---

## Alternatives Considered

1. **Option A (chosen):** [description]
   - Pros: [list]
   - Cons: [list]

2. **Option B:** [description]
   - Pros: [list]
   - Cons: [list]

3. **Option C (do nothing):** [description]
   - Why rejected: [reason]

---

## Why I Chose This

[The actual reasoning. Not the polished version — the real one. What tipped the scale?]

---

## Expected Outcome

[What do you expect to happen if this was the right call? Be specific and measurable where possible.]

**Time horizon:** [When will you know if this was right? 30 days? 6 months? 1 year?]

---

## Confidence Level

**Rating:** [1-10]
**Why:** [What would make this more or less confident?]

---

## Risk / Downside

[If this is wrong, what's the worst case? How recoverable is it?]

---

## Review Scheduled

**Review date:** [YYYY-MM-DD based on time horizon above]
**Review prompt:** "Check decision DEC-[001] — was [expected outcome] realized?"

---

## Outcome Review

*Fill in when reviewing:*

**Reviewed:** [date]
**What actually happened:** [description]
**Verdict:** ✅ Right call / ⚠️ Partially right / ❌ Wrong call / 🔄 Too early to tell
**Lesson:** [What would you do differently? What did you learn about your judgment?]

Step 4 — Configure Your Agent

Add to your agent's instructions or AGENTS.md:

## Decision Log

I maintain a decision journal in `decisions/`. When I make a significant decision:

1. Create a new entry: `decisions/YYYY-MM-DD-short-title.md`
2. Use the decision log template
3. Update `decisions/INDEX.md` with the new entry
4. Flag for review at the appropriate horizon

**Log decisions when:**
- Spending more than $100 on a tool, service, or ad campaign
- Starting or stopping a project
- Taking on or declining a client/partnership
- Choosing between two meaningful strategies
- Making a hiring/contractor decision
- Any choice I'll want to revisit in 90+ days

**Do not log:**
- Routine operational choices (which tool to use for a small task)
- Decisions that are immediately reversible with no material cost
- Personal/lifestyle choices (unless I ask you to track them)

**Weekly prompt (optional):** "Check decisions/INDEX.md for any decisions with a review date this week."

Logging a Decision (Quick Capture)

When you're in a hurry, give your agent this prompt:

Log a decision: [what you decided]. Category: [category]. Why: [brief reason]. Expected: [outcome]. Review in: [timeframe].

Your agent will create a properly formatted entry, assign an ID, and update the index.


The Review Protocol

Weekly Review (5 minutes)

Ask your agent:

Check decisions/INDEX.md for any open decisions with a review date this week or earlier. For each one, ask me: what actually happened?

Your agent will pull each due decision, prompt you for the outcome, fill in the verdict and lesson, and move it to the reviewed section.

Monthly Pattern Analysis

Ask your agent:

Review all decisions in decisions/ logged in the last 90 days. Identify:
1. My verdict breakdown (% right / partial / wrong)
2. Which categories I'm most accurate in
3. Which categories I'm least accurate in
4. Any repeated mistakes or blind spots
5. My calibration: am I overconfident or underconfident relative to my confidence ratings?

Summarize findings in decisions/PATTERNS.md.

Quarterly Decision Retrospective

Add to your quarterly review:

Pull decisions/PATTERNS.md and all decisions from this quarter. Write a 1-page decision retrospective:
- My best decision this quarter (and why)
- My worst decision this quarter (and what I missed)
- The pattern I most need to address
- One rule I'm adding to my decision-making process

Decision Categories

Use consistent categories for meaningful pattern analysis:

CategoryUse For
businessBusiness model, positioning, pivots
productWhat to build, features, prioritization
operationsTools, processes, systems
financialSpending, pricing, investment
marketingChannels, campaigns, messaging
partnershipClients, collaborators, vendors
hiringContractors, employees, agencies
strategyLong-term direction, market entry
personalWork style, schedule, habits

Confidence Calibration Guide

Use this to rate decisions consistently:

ScoreMeaning
9-10Near certain. Strong evidence, low complexity, proven pattern.
7-8High confidence. Good evidence, manageable unknowns.
5-6Moderate confidence. Mixed signals or real uncertainty.
3-4Low confidence. Mostly a bet. Limited evidence.
1-2Essentially a guess. High uncertainty, no clear evidence.

Track your calibration over time: if your "7s" only pan out 40% of the time, you're overconfident. If your "4s" pan out 80% of the time, you're underconfident. Good calibration is when your 7s hit ~70% of the time.


Integration With Other Agent Ledger Skills

This skill works well alongside:

  • solopreneur-assistant — Log decisions surfaced in your weekly business review
  • goal-tracker — Tag decisions that affect quarterly goals; review whether decisions moved the needle
  • project-tracker — Log project go/no-go decisions and major pivots
  • research-assistant — Research briefs inform decision context; link briefs to decision entries

Cross-skill pattern: When your goal-tracker flags a missed KR, ask your agent to pull any decisions logged in that goal's category during the quarter. Often the root cause of a missed goal is a bad decision logged months earlier — now you have the receipt.


Customization Options

Track Only High-Stakes Decisions

Raise the threshold in your agent instructions:

Only log decisions involving: >$500 spend, >10 hours of effort commitment, or any choice I explicitly flag as "log this."

Add Stakeholder Field

For decisions that affect others:

**Stakeholders:** [who else is affected or needs to know]
**Communicated:** yes / no / partial

Add Emotional State

For decisions where cognitive bias is a real risk:

**Emotional state when deciding:** [rushed / calm / anxious / excited / frustrated]

Over time, your agent can correlate emotional state with decision quality. Most solopreneurs find their worst decisions happen when rushed or excited.

Public Accountability Mode

Share a sanitized decision log in your newsletter:

Pull my last 5 reviewed decisions from decisions/. Summarize the lesson from each in one sentence, anonymizing any sensitive details. Format for a newsletter section called "What I Got Wrong This Month."

File Structure

decisions/
├── INDEX.md                          # Decision inventory + patterns
├── PATTERNS.md                       # Quarterly pattern analysis (agent-generated)
├── 2026-03-01-launch-channel.md      # Example: "Which channel to launch on first"
├── 2026-03-05-pricing-model.md       # Example: "Subscription vs one-time pricing"
└── 2026-04-02-contractor-hire.md     # Example: "Hire VA or DIY content creation"

Troubleshooting

IssueFix
Decision entries are too vagueAsk agent: "Make me fill in the 'Why I Chose This' field before creating the entry — don't let me skip it"
Review dates pile upAdd a weekly recurring reminder or heartbeat check for due reviews
Index gets out of syncAsk agent: "Scan decisions/ and rebuild INDEX.md from actual files"
Patterns analysis is shallowMake sure you have at least 10 reviewed decisions before running pattern analysis
I'm logging too many decisionsRaise the threshold — only log decisions you'd want to justify to a board

Why This Works

Most "decision journals" fail because they require too much friction at capture time and provide no structured review. This skill solves both problems:

  1. Low-friction capture — Quick-capture prompt gets you a full entry in under 2 minutes
  2. Automatic review prompts — Review dates are set at capture time, not whenever you remember
  3. Pattern analysis — Your agent synthesizes across entries, surfacing insights you'd never see manually
  4. Calibration tracking — Over time you build a data-driven model of your own judgment quality

The goal isn't to second-guess every decision. It's to get 10% smarter per quarter by learning systematically from your own track record.


License

CC-BY-NC-4.0 — Free to use and share with attribution. Not for resale.

by The Agent Ledger | theagentledger.com

Premium skills, playbooks, and the complete Agent Blueprint guide are available at theagentledger.com. Subscribe to the newsletter for release announcements.

*DISCLAIMER: This skill was created by an AI agent. It is provided "as is" for informational and educational purposes only. It does not constitute professional, financial, legal, or technical advice. Review all generated files before use. The Agent Ledger assumes no liability for outcomes resulting from use. Use at your own risk.*

平台分布

OpenClaw

71.01%
按下载量换算2,633

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。目前只有一个公开来源,建议同时核对源仓库。

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