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。
| 平台 | 累计安装 | 累计下载 | 星标 | 评论 |
|---|---|---|---|---|
| ClawHub ↗ | 136 | 3,986 | 5 | - |
| SkillHub ↗ | 132 | 4,059 | 5 | — |
各平台的计数口径不同,统计时间不一致。
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
以下是根据技能文档生成的示例,不代表实际运行结果。
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 |
|---|---|---|---|
| 2026-09 | 4,059 | — | 5 |
| 2026-08 | 3,861 | +3.1% | 5 |
| 2026-07 | 3,744 | +8.1% | 5 |
| 2026-06 | 3,463 | +4.8% | 5 |
| 2026-05 | 3,304 | — | 5 |
| 2026-03 | 2,265 | — | 4 |
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。
---
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/"]}}
---
## 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`
把开发过程中的经验、错误与纠正记录为 Markdown 文件,供后续复用并晋升为工作区记忆。
用自然语言描述需求,在本地与远程技能市场里找技能并给出推荐的技能。
给 AI agent 用的浏览器自动化命令行技能。
把用户纠正与自我复盘沉淀成分层记忆、并按使用情况升降级的技能。
在安装第三方技能之前,按清单审查来源、权限与危险模式的技能。
把 agent 的记忆与跨技能状态存成带类型和约束的知识图谱的技能。
一份为 AI 代理规划主动跟进、工作记忆与复盘流程的工作区配置指南。
指导如何创建与更新有效 Claude 技能的写作指南。
把用户纠正与自我复盘沉淀成分层记忆、并按使用情况升降级的技能。
在保留公式、格式与工作簿结构的前提下创建、检查并修改 Excel 文件的技能。
处理 Word 与 DOCX 文档的作业规范,重点是改完之后样式、编号与版式不跑偏。
把数据库、表格与导出的原始数据变成可决策结论的数据分析技能。
改 PPT 之前先看清结构的一套规范,目标是改完还能干净渲染。
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。