Iván

Decide

AI 智慧體v1.0.1
作者:Iván更新於 2026-05-11共 2 個版本 · 最新版本釋出於 2026-03-09decide@clawhub_ivangdavila/decide
任務自動化上下文管理記憶增強#Learn

據 AlphaMoat 統計,截至 2026-09,Decide 累計下載 4,059 次、獲 5 個星標,在全部 5,078 個 AI 技能中排名第 2,503 位,在「AI 智慧體」分類(969 個)中排名第 487 位。

累計安裝
136
累計下載
4059
星標
5
全站排名
#2,503
全站前 50% · 「AI 智慧體」前 51%
平台累計安裝累計下載星標評論
ClawHub ↗13639865-
SkillHub ↗13240595—

各平台的計數口徑不同,統計時間不一致。

這個技能是做什麼的

該技能針對可能影響架構、工作流、成本、釋出、供應商或長期方向的分支決策。它要求先明確問題,收集關鍵條件,檢查本地決策記憶和相似記錄;只有當問題與決定性條件相符時才參考過去選擇,缺少資訊或存在例外時先詢問使用者。決策記憶儲存在本地 ~/decide/,可能包含敏感專案背景;應限制檔案許可權,且不要存放秘密、憑據或第三方私密資料。

資料釋出於 2026-10-29

可以用來做什麼

  • 處理可能造成長期影響的重大分支選擇

什麼時候用

  • 判斷現有決策記錄是否與當前問題及關鍵條件相匹配

示例提示詞

以下是根據技能文件生成的示例,不代表實際執行結果。

建立決策記憶或複用已確認選擇前,檢查適用範圍並在條件變化時再次詢問

月度資料趨勢

累計下載4059
2121316242032026-03 · 累計下載 22652026-05 · 累計下載 33042026-06 · 累計下載 34632026-07 · 累計下載 37442026-08 · 累計下載 38612026-09 · 累計下載 405926-0326-09
星標5
4552026-03 · 星標 42026-05 · 星標 52026-06 · 星標 52026-07 · 星標 52026-08 · 星標 52026-09 · 星標 526-0326-09

2026-09 起統計方法調整:同名技能按作者分別統計,並覆蓋全部技能。圖中虛線為調整前的月份,與之後的資料不直接比較,也不計算跨越調整的增長。

月份累計下載環比星標
2026-094059—5
2026-083861+3.1%5
2026-073744+8.1%5
2026-063463+4.8%5
2026-053304—5
2026-032265—4

SKILL.md 原文

以下是作者釋出的原始檔案,按純文字顯示,本站未做修改或稽核;其中的連結不可點選,命令請核實後再執行。抓取於 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/"]}}
---
展開全部(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 智慧體」分類下的熱門技能

Iván 的其他技能

資料月份:2026-09 · 下載/星標/安裝數聚合自公開技能註冊庫(ClawHub、SkillHub),按月更新。口徑說明見資料方法。