Iván

Decide

AI 智能体v1.0.1
作者:Iván更新于 2026-05-11共 2 个版本 · 最新版本发布于 2026-03-09decide@clawhub_ivangdavila/decide
任务自动化上下文管理记忆增强#Learn

据 AlphaMoat 统计,截至 2026-09,Decide 累计下载 4,059 次、获 5 个星标,在全部 5,078 个 AI 技能中排名第 2,503 位,在「AI 智能体」分类(969 个)中排名第 487 位。

累计安装
136
累计下载
4,059
星标
5
全站排名
#2,503
全站前 50% · 「AI 智能体」前 51%
平台累计安装累计下载星标评论
ClawHub ↗1363,9865-
SkillHub ↗1324,0595—

各平台的计数口径不同,统计时间不一致。

这个技能是做什么的

该技能针对可能影响架构、工作流、成本、发布、供应商或长期方向的分支决策。它要求先明确问题,收集关键条件,检查本地决策记忆和相似记录;只有当问题与决定性条件相符时才参考过去选择,缺少信息或存在例外时先询问用户。决策记忆保存在本地 ~/decide/,可能包含敏感项目背景;应限制文件权限,且不要存放秘密、凭据或第三方私密资料。

资料发布于 2026-10-29

可以用来做什么

  • 处理可能造成长期影响的重大分支选择

什么时候用

  • 判断现有决策记录是否与当前问题及关键条件相匹配

示例提示词

以下是根据技能文档生成的示例,不代表实际运行结果。

建立决策记忆或复用已确认选择前,检查适用范围并在条件变化时再次询问

月度数据趋势

累计下载4,059
2,1213,1624,2032026-03 · 累计下载 2,2652026-05 · 累计下载 3,3042026-06 · 累计下载 3,4632026-07 · 累计下载 3,7442026-08 · 累计下载 3,8612026-09 · 累计下载 4,05926-0326-09
星标5
4552026-03 · 星标 42026-05 · 星标 52026-06 · 星标 52026-07 · 星标 52026-08 · 星标 52026-09 · 星标 526-0326-09

2026-09 起统计方法调整:同名技能按作者分别统计,并覆盖全部技能。图中虚线为调整前的月份,与之后的数据不直接比较,也不计算跨越调整的增长。

月份累计下载环比星标
2026-094,059—5
2026-083,861+3.1%5
2026-073,744+8.1%5
2026-063,463+4.8%5
2026-053,304—5
2026-032,265—4

SKILL.md 原文

以下是作者发布的原始文件,按纯文本显示,本站未做修改或审核;其中的链接不可点击,命令请核实后再运行。抓取于 2026-10-23。

7.1 KB · ClawHub v1.0.1
---
name: Decide
slug: decide
version: 1.0.1
homepage: https://clawic.com/skills/decide
description: Self-learn your decision patterns to safely build its own decision-making over time.
changelog: "Adds structured decision logging, safer setup, and stricter context matching before autonomous choices."
metadata: {"clawdbot":{"emoji":"⚖️","requires":{"bins":[]},"os":["linux","darwin","win32"],"configPaths":["~/decide/"]}}
---
展开全部(共 129 行)
## Architecture

Decision state lives in `~/decide/`. If that folder is missing or empty, run `setup.md`.

```text
~/decide/
├── memory.md        # Durable decision rules, approval boundaries, and confirmed defaults
├── decisions.md     # Major decisions with question, components, chosen option, and outcome
└── domains/         # Domain-specific decision components, overrides, and exceptions
```

## When to Use

Use when the agent faces a consequential choice that can change architecture, workflow, cost, publish behavior, vendor selection, or long-term project direction.

This skill is for branching decisions, not for generic preferences or execution lessons. It should stay compatible with `self-improving`: `self-improving` learns how to work better, while `decide` learns how to choose safely when the choice has lasting consequences.

## Quick Reference

| Topic | File |
|-------|------|
| Setup guide | `setup.md` |
| Memory template | `memory-template.md` |
| Migration guide | `migration.md` |
| Decision components | `components.md` |
| Confidence calibration | `confidence.md` |
| Exceptions and always-ask cases | `exceptions.md` |

Use those files as a decision safety stack: first know the structure, then calibrate confidence, then verify exceptions before reusing any past choice.

## Decision Workflow

1. Frame the decision as a real question, not as a vague feeling.
2. Gather the components that materially affect the answer.
3. Read `~/decide/memory.md`, then the smallest relevant file in `~/decide/domains/`, then check `~/decide/decisions.md` for a materially similar record.
4. Reuse a past choice only if the question, the key components, and the exception boundaries still line up.
5. If anything important is missing or changed, ask first and log the answer once the human decides.

## Core Rules

### 1. Default Conservative on Consequential Choices
- Frameworks, architectures, migrations, vendors, publish paths, spending, and irreversible branches should default to asking the human.
- Safer failure means asking one question too many, not silently picking the wrong branch.
- A recommendation is good; an unvalidated autonomous choice is not.

### 2. A Decision Requires Components, Not Vibes
- Every major decision should be framed as a question plus the components that materially affect the answer.
- Components can include product surface, client type, reversibility, budget, timeline, team size, project constraints, and long-term maintenance cost.
- If the needed components are missing, the decision is not ready for autonomy.

### 3. Reuse Only When the Context Materially Matches
- A stored rule is reusable only when the question and the key components match closely enough to make the same choice still rational.
- Matching one signal is not enough. "Same framework choice" is weak if client, surface, constraints, or risk changed.
- If there is any serious mismatch, ask first.
- Exceptions beat defaults. A confirmed default is still invalid when a domain override or high-stakes exception changes the branch.

### 4. Promote Patterns Only After Human Confirmation
- Repeatedly seeing the same decision in the same context makes a candidate rule, not an autonomous permission.
- After enough similar decisions, ask: "When this is true plus this plus this, should I default to X?"
- Only promote the pattern after explicit confirmation.

### 5. Log the Full Decision, Not Just the Outcome
- Store the question, components, chosen option, rationale, confidence, and outcome.
- A naked note like "use React Native" is too weak; it must say when and why.
- Good logs prevent false autonomy later.

### 6. Keep Workspace Routing Non-Destructive
- Use setup to add small AGENTS and SOUL snippets that force decision retrieval before major choices.
- Show the exact snippet and wait for explicit approval before writing any workspace file.
- The routing must make it hard to skip the decision log when a consequential branch appears.

### 7. Never Let Decision Memory Shadow Other Skills
- Use `self-improving` for execution quality, corrections, and reusable work habits.
- Use `escalate` for ask-vs-act boundaries across actions broadly.
- Use `decide` only for major branching choices where the structure of the context determines the answer.

## Common Traps

These failures usually come from pattern-matching too early or from collapsing a major decision into a shallow preference.

| Trap | Why It Fails | Better Move |
|------|--------------|-------------|
| Reusing a rule because the question sounds similar | Important components may have changed | Compare question plus key components before reusing |
| Treating one-off emergency choices as defaults | Stress decisions rarely generalize well | Log them, but keep them unconfirmed unless repeated |
| Autodeciding after reading only memory.md | Exceptions and domain overrides get missed | List domains, read the smallest relevant override, then check decisions |
| Turning execution preferences into decision rules | Blurs compatibility with `self-improving` | Keep major branching choices in `decide`, workflow lessons elsewhere |
| Applying a framework or vendor rule across clients blindly | Client and surface often change the optimal answer | Ask again when client, platform, scope, or constraints differ |

## Data Storage

Local state lives in `~/decide/`:

- durable decision rules, approval boundaries, and confirmed defaults in `~/decide/memory.md`
- major decision records in `~/decide/decisions.md`
- domain-specific component models, overrides, and exceptions in `~/decide/domains/`

The packaged guides `components.md`, `confidence.md`, and `exceptions.md` stay in the skill itself and act as references, not as the user's live memory.

## Security & Privacy

- This skill stores local decision notes in `~/decide/`.
- It may read workspace steering files such as the AGENTS file and SOUL file so that decision retrieval happens before major choices.
- It may suggest small non-destructive edits to those files during setup, but it must show the snippet and wait for explicit approval before any write.
- It should prefer asking to guessing whenever a decision can affect money, production, publishing, deletion, contracts, or long-term architecture.
- It never modifies its own `SKILL.md`.

## Related Skills
Install with `clawhub install <slug>` if user confirms:

- `escalate` - Control broad ask-vs-act boundaries around risky actions
- `self-improving` - Learn execution lessons without conflating them with decision rules
- `memory` - Keep broader long-term context and user continuity
- `proactivity` - Push the next step while respecting confirmed decision defaults

## Feedback

- If useful: `clawhub star decide`
- Stay updated: `clawhub sync`

「AI 智能体」分类下的热门技能

Iván 的其他技能

数据月份:2026-09 · 下载/星标/安装数聚合自公开技能注册库(ClawHub、SkillHub),按月更新。口径说明见数据方法。