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Agent Skill · 开发

sage-decision-journal(明智的决定日记)

sage-decision-journal 用于补充开发相关能力,适合在 OpenClaw 中需要让 Agent 承接开发相关任务时使用。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。

sage-decision-journal 是开发类 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,第三方扫描有提醒项,按安装量在开发类的 21,746 个 Skill 里排第 2,113。

总安装 20,370周安装 875GitHub Stars 1下载 7,140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
0.1.0

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:sage-decision-journal(明智的决定日记)
来源仓库:https://github.com/evanl1/sage-decision-journal
安装命令:
openclaw skills install sage-decision-journal
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 sage-decision-journal

这个 Skill 是做什么的

开发类 Skill 面向写代码、改代码和排查问题,一般会读写仓库文件,部分还会执行构建或测试命令。

sage-decision-journal 记录重要决策背景、推理和替代方案。它帮助用户发现盲点并改进决策质量。安装后可在 OpenClaw 中调用,需存储权限。使用前应定义决策分类标准,便于检索。建议结合回顾周期提升使用效果。

sage-decision-journal 属于开发类 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 sage-decision-journal;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

sage-decision-journal 怎么安装?

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

sage-decision-journal 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。

sage-decision-journal 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。

sage-decision-journal 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。

sage-decision-journal 用的人多吗?

公开数据是累计安装 20,370 次,近一周安装 875 次,下载 7,140 次,GitHub 1 星。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。

sage-decision-journal 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。
  • 需要处理开发类任务、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。

开发类 Skill 对比

6 个同类
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8,779,018986操作浏览器MIT-0通过
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6,636,960576只读MIT-0通过
entra-app-registration

内部应用程序注册 · Codex、Claude

6,631,872764接触敏感数据未明确通过
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3,757,680201接触敏感数据未明确通过
nano-banana-pro

纳米香蕉专业版 · OpenClaw

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sage-decision-journal

明智的决定日记 · 本页

20,3701需要联网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
sage-decision-journal
description
A decision capture and review system that records every significant choice — with context, reasoning, and alternatives — so you can detect your own blind spots and decision patterns over time.
version
0.1.0
metadata
openclaw
emoji
🪞
homepage
https://github.com/nicholasgasior/sage
tags
depends_on

Sage Decision Journal — Because You'll Forget Why You Decided That

This skill builds on the identity and behavioral profile established by sage-cognitive. Where sage-cognitive observes *who you are*, sage-decision-journal tracks *what you chose* — and more importantly, *why*.

The core premise: your biggest blind spot is not making bad decisions. It's forgetting you made a decision at all. Without a record, you can't learn. Without learning, you repeat.


How This Works

CAPTURE → CLASSIFY → STORE → FOLLOW UP → REVIEW → SURFACE PATTERNS
    ↑                                                       |
    └───────────────────── feedback loop ──────────────────┘

The journal runs silently alongside sage-cognitive. You don't need to invoke it explicitly — it listens for decision signals in every conversation and records them automatically.


Decision Capture

Signal Detection

The journal detects two types of decisions:

Explicit decisions — user directly states a choice:

  • "I decided to..."
  • "We're going with..."
  • "I told him we'll..."
  • "I chose X over Y"

Implicit decisions — inferred from behavior and context:

  • User describes an action taken without explaining why → record the decision embedded in the action
  • User dismisses an option someone else proposed → record the rejected alternative
  • User changes direction on a previous plan → record the pivot and what triggered it

Decision Record Format

Every captured decision is stored with five fields:

WHAT        The decision itself. One sentence, action form.
WHY         The stated or inferred reasoning. What made this the right call?
ALTERNATIVES What else was on the table? What was NOT chosen?
CONTEXT     What was the environment? Time pressure, stakeholder dynamics, info available?
CONFIDENCE  How certain was the user? (certain / leaning / uncertain / forced)

Example: Explicit Decision

User says: *"I decided to skip the unit tests for the dashboard feature and ship it Thursday. The demo is more important right now."*

Captured record:

WHAT         Skipped unit tests for dashboard feature; shipped Thursday
WHY          Demo deadline took priority over test coverage
ALTERNATIVES Write tests first and delay Thursday ship; write minimal smoke tests
CONTEXT      Demo coming up, stakeholder expectations set, time pressure
CONFIDENCE   Leaning (acknowledged the trade-off)
TYPE         Technical / Reversible (tests can be written post-ship)

Example: Implicit Decision

User says: *"Prepared the PULSE topology diagram, for Bob."*

Captured record:

WHAT         Took on CTO-facing deliverable directly (topology diagram for Bob)
WHY          Not stated — inferred: strategic visibility, PULSE importance
ALTERNATIVES Delegate to team member; route through Shawn
CONTEXT      PULSE is new project; Bob is CTO; direct delivery bypasses normal chain
CONFIDENCE   Certain (deliberate action, not accidental)
TYPE         Strategic / Reversible

Decision Taxonomy

By Domain

TypeExamplesReview horizon
TechnicalArchitecture choice, tech stack, skip tests2–4 weeks
PeopleWho gets which task, feedback delivered, hire/no-hire signal1–3 months
StrategicProject prioritization, resource allocation, scope changes3–6 months
CommunicationWhat to tell whom, when, how much context to share1–2 weeks

By Reversibility (Amazon's framework, adapted)

Two-way door — reversible, low stakes, decide fast:

Reassigning a task, choosing a library, trying a new process

One-way door — hard to undo, high stakes, slow down:

Architectural rewrites, letting someone go, committing to a roadmap to external stakeholders

When a one-way door decision is captured, add a brief flag: *"This is a one-way door. What would make you reverse it?"*

By Decision Mode

ModeSignalWhat it reveals
DeliberateUser weighs options, asks for inputDecisions made with clarity
ReactiveResponse to external pressure or surpriseDecisions under stress — track carefully
DelegatedUser hands off and doesn't revisitTrust in others, or avoidance?
DefaultNo choice made, status quo maintainedInaction is also a decision

Pattern Detection

After 10+ decisions are logged, begin running pattern analysis. Surface patterns — don't diagnose them.

Decision Tendencies

Look for consistent skews across the decision history:

AxisSignal pattern
Speed vs. DeliberationHow often does the user decide within minutes vs. sleep on it?
Conservative vs. AggressiveDoes the user default to the safer option when uncertain?
People-first vs. Task-firstWhen trade-offs involve team vs. delivery, which wins?
Own judgment vs. ConsensusDoes the user seek input before deciding, or after?
Visible vs. Behind-the-scenesDoes the user prefer credit or quiet impact?

Blind Spot Detection

Flag patterns that suggest recurring information gaps:

  • Missing stakeholder consideration: Decisions that didn't account for a key person's reaction
  • Optimism bias: Timelines or outcomes consistently more optimistic than reality
  • Sunk cost signals: Continuing a course of action because of past investment, not future value
  • Confirmation seeking: User only consults sources likely to agree with them
  • Urgency override: Quality or completeness consistently sacrificed under time pressure

Cognitive Bias Signals

When detected, name the bias gently, once. Don't repeat it:

Confirmation bias:    "You've checked with three people who all agreed. Is there anyone who'd push back?"
Sunk cost:            "You've mentioned how much time went into this twice. Is that affecting what you do next?"
Availability bias:    "The last time this went wrong is fresh. Is this situation actually similar?"
Recency bias:         "The last few decisions went [well/badly]. Does that pattern hold here?"

Review Cadence

Weekly Review (every Friday, or end of work week)

Three questions, answered from the decision log:

  1. What decisions did I make this week? (list the captured records)
  2. Which decision am I least confident about in hindsight? (flag for follow-up)
  3. Is there a pattern I keep seeing? (one observation, not a diagnosis)

Output format — concise, no filler:

📋 Decision Week in Review — [date]

Decisions made: 4
  → Technical (2): [brief labels]
  → People (1): [brief label]
  → Strategic (1): [brief label]

Worth watching: [one decision to revisit]
Pattern signal: [one pattern, if present]

Monthly Review (retrospective on 30-day-old decisions)

Pull decisions from ~30 days ago. For each significant one, ask:

  • What actually happened? Does the outcome match the reasoning at the time?
  • What information did you not have then that you have now?
  • Would you make the same call again?

This is where learning happens. Not from knowing the decision was wrong — but from understanding *why* the reasoning felt right at the time.

Quarterly Review (decision style drift)

Look across the full decision history and ask:

  • Is the speed of decisions changing? (faster? slower? in which domains?)
  • Is the confidence level shifting? (more certain vs. more uncertain)
  • Are new decision domains appearing that weren't there before?
  • What's one assumption that appears in decisions from 6 months ago that you no longer hold?

Follow-up System

For every captured decision, set a follow-up based on type:

Decision typeFollow-up trigger
Technical2–3 weeks after shipping
People4–6 weeks after the conversation or action
StrategicEnd of quarter
Communication1 week after delivery

Follow-up prompt (gentle, not interrogating):

"Three weeks ago you decided [WHAT]. How did that land?"

If the user answers, log the outcome alongside the original record. If they don't, note it silently — non-responses are also data (some outcomes are uncomfortable to revisit).


Anti-Patterns

  • Don't record every micro-choice: "I chose to write the email in English" is not a decision worth logging. Only decisions with real alternatives and real stakes.
  • Don't moralize: A decision isn't "good" or "bad" until outcome is known. The journal is neutral.
  • Don't surface patterns too early: Ten decisions minimum before pattern language. Two data points are not a pattern.
  • Don't repeat bias flags: Name it once. If the user ignores it, drop it. Nagging kills trust.
  • Don't conflate outcome with quality: A decision made with bad reasoning can still turn out fine. A decision made with good reasoning can fail. Track both separately.
  • Don't substitute for real-time input: The journal is for retrospective learning, not live deliberation. For live decisions, defer to sage-cognitive's profile and the user's mental models.
  • Don't expose the machinery: Users should feel like they're being *remembered*, not *monitored*. Surface insights naturally, not as database outputs.

Integration with sage-cognitive

This skill reads from sage-cognitive's behavioral profile and writes back to it:

Reads:

  • Decision tendency profile (from Phase 1: OBSERVE)
  • User's stated decision style (from Phase 0: KNOW)
  • Known cognitive preferences (speed, quality, people-first)

Writes:

  • Confirmed or updated decision tendencies → sage-cognitive archive tier
  • Detected bias signals → sage-cognitive working tier (expires in 14 days if not reinforced)
  • Pattern-level insights → sage-cognitive core tier (only when pattern is strong and consistent)

Coordination rule: If sage-cognitive's Mirror (Phase 2) already reflected a decision pattern this session, sage-decision-journal should not surface the same pattern. One mirror per day is enough.

平台分布

OpenClaw

96.41%
按下载量换算6,884

安全审计

VirusTotal

通过

ClawScan

可疑

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。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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