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smarty-skill聪明的技巧

Agent Skill

smarty-skill 用于记录任务执行中的错误、用户纠正、经验和能力缺口,适合在 OpenClaw 中希望让 Agent 持续沉淀问题、修正和最佳实践时使用。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。

总安装

6,614

周安装

265

GitHub Stars

公开资料未说明

下载量

2,141
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

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:smarty-skill(聪明的技巧)
来源仓库:https://github.com/ckpxgfnksd-max/smarty-skill
安装命令:
openclaw skills install smarty-skill
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 smarty-skill

简介

持续记录任务执行中的错误与用户反馈,用于优化 Agent 行为模型。

  • 在每个会话中主动学习偏好设定,提取公理作为后续交互默认值。
  • 适用于需要长期记忆修正经验以提升准确性的复杂协作场景。
  • 数据存储于本地会话缓存,重启后不会持久化,需配合外部知识库使用。
  • 注意其不主动修改核心逻辑,仅影响提示词生成策略与响应倾向。

SKILL.md

name
smarty-skills-infra
description
Always active in every session. Learns user preferences from corrections and stated preferences, distills axioms, applies them as defaults. Makes every other skill better over time.

Smarty Skills-Infra

You maintain a lightweight memory of this user's preferences, judgments, and working style. Memory operations never interrupt the user's workflow.

At Session Start

Do this before addressing the user's request.

  1. Read memory/context-infra/context-profile.md if it exists. Treat axioms as your own defaults — adapt when the situation differs. If missing, skip.
  1. Check memory/context-infra/observations.log. If it has 15+ entries since the last ## Reflected marker, reflect *before* starting the user's task. Say exactly: *"Consolidating patterns from recent work."* Then follow When Reflecting below. Never interrupt a task to reflect.

On first session (no files exist), skip both steps and start observing.

During Every Task

Record ONLY when a trigger fires:

  • Correction: the user changes, rewrites, or redirects your output
  • Stated preference: the user explicitly says they prefer, want, or dislike something
  • Retraction: the user asks to forget, stop applying, or undo a remembered preference

Most tasks produce zero observations.

Append one line to memory/context-infra/observations.log:

YYYY-MM-DD | domain | signal | "Preference in ≤15 words."
  • domain: organic label (e.g. code-style, architecture, communication, tooling, testing, workflow)
  • signal: correction | stated-preference | retraction

One observation per preference per session.

Bootstrap mode (first 2 sessions) — cast a wider net: also note what the user accepts without comment and consistent choices.

Do not record: routine completions, project-specific facts, or one-time decisions.

When Reflecting

Four steps:

  1. Group: Read observations and profile. Cluster by domain, merging near-duplicates.
  2. Promote: Promote when a pattern appears across 3+ distinct contexts (different days or projects), has no contradictions, and is a preference not a fact. Each axiom must be specific enough to change behavior, yet general enough to apply across projects. See references/profile-format.md for format.
  3. Maintain: Increment strength for reinforced axioms. Mark contradictions as contested. Remove axioms targeted by a retraction immediately — no threshold needed. Merge related axioms. Move unconfirmed (30+ days) to Dormant. Cap at 25 — if at cap, merge related axioms or demote lowest-strength to Dormant before promoting.
  4. Clean up: Rewrite the profile. Rewrite observations.log: keep only un-promoted entries, prepend ## Reflected YYYY-MM-DD.

Create missing files on first write. Never fail silently.

Example

Observations:

2026-01-15 | code-style | correction | "User shortened verbose function name."
2026-01-18 | code-style | correction | "User rejected descriptive name, asked for abbreviation."
2026-02-01 | code-style | stated-preference | "User uses 2-3 word function names in new project."

3 distinct contexts, 0 contradictions — promoted:

- I prefer short, concise names — abbreviate rather than spell out.
  strength: 3 | domain: code-style | last-confirmed: 2026-02-01

NOT promoted if all observations were same-session — same-session repeats count as one context.

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

OpenClaw

75.1%
按下载量换算1,608

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

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。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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