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srsasrsa 效率

Agent Skill

srsa 用于补充效率相关能力,适合在 OpenClaw 中需要让 Agent 承接效率相关任务时使用。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。

总安装

4,672

周安装

189

GitHub Stars

公开资料未说明

下载量

1,467
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:srsa(srsa 效率)
来源仓库:https://github.com/cheanus/srsa
安装命令:
openclaw skills install srsa
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 srsa

简介

运行 AI 代理间隔重复系统 (SRSA) 复习课程。

  • 支持对卡片进行再次/困难/良好/简单评分。
  • 建议外显记忆和知识巩固策略。
  • 适用于学习和知识管理场景。srsa 属于效率类 Skill,可作为该场景下的辅助能力补充。
  • 需定期维护和更新学习内容。适用宿主包括 OpenClaw,接入前应确认版本、权限和运行环境要求。

SKILL.md

name
srsa-review
description
Use when running Spaced Repetition Systems for AI Agents (SRSA) daily review sessions, grading cards with again/hard/good/easy, and proposing explicit memory add/delete/update actions after each review.

SRSA Review Skill

Purpose

Use SRSA's command-line workflow to drive efficient agent (you) reviews and turn each review result into actionable memory correction tasks.

Concept Boundary

  • SRSA cards: managed only through card and review commands in this skill.
  • Agent memory system: must be updated explicitly by the agent (add/delete/update), based on reflection.

What cards need to be generated?

  • Actions that have been corrected by the user
  • User preferences
  • Decision you have hesitated to make
  • Others that the user explicitly wants you to remember

Command Cheat Sheet

# Print total cards, today's review progress, future due cards and average retrievability
uv run python scripts/main.py status
# Create a new card
uv run python scripts/main.py card new -q "question" -a "answer"
# Override an existing card
uv run python scripts/main.py card override [CARD_ID] -q "question" -a "answer"
# Remove a card
uv run python scripts/main.py card rm [CARD_ID]
# Get a question and its CARD_ID
uv run python scripts/main.py review get-question
# Get the answer and CARD_ID of the current question
uv run python scripts/main.py review get-answer
# Rate the review result, then print historical accuracy, today's review progress and retrievability change
uv run python scripts/main.py review rate [again|hard|good|easy]

Review Loop

Follow this sequence strictly. Do not skip steps:

  1. review get-question
  2. The agent answers from its own memory first (do not view the answer yet).
  3. review get-answer
  4. Compare with the answer, then self-grade with again/hard/good/easy.
  5. review rate [RATING]
  6. Use the output's historical correctness and remaining progress to apply the reflection template.
  7. Continue to the next card until there are no due cards or the user asks to stop.

State Constraints

  • If did not run rate, running get-question again will repeat the previous card.
  • Running get-answer before get-question returns an error.
  • Running rate before get-answer returns an error.

Rating Rules

  • again: You could not recall it, or the core facts in your answer were wrong.
  • hard: You recalled it, but with clear difficulty and noticeable delay.
  • good: You answered correctly with only a brief pause.
  • easy: You answered quickly and accurately with no obvious hesitation.

Reflection Template

After each rating, unless the self-rating is easy, output reflection using this template:

  1. Conclusion for this card
  • Was the answer correct?
  • What were the main errors or hesitation points?
  1. Update your memory system (explicit action required)
  • Add: If missing key information caused a wrong or slow answer.
  • Delete: If interfering memory caused misjudgment.
  • Update: If existing memory is inaccurate and needs correction.
  1. Challenge the card (optional)
  • Is the prompt underspecified or ambiguous?
  • Does the reference answer need revision?
  1. Next step
  • Ask for the next card, or state that the review is finished.

Output Discipline

  • In the get-question stage, focus only on the prompt.
  • In the get-answer stage, focus only on the reference answer.
  • In the rate stage, do scoring and reflection only; do not rewrite the full question.
  • In long review sessions, keep reflections short to control context length.
  • When updating memory, you need to explicitly state the action (add/delete/update) on your own memory system. SRSA tracks and schedules cards only. It does not automatically update it.

End Conditions

End the review when any one condition is met:

  • The command output says "No due cards".
  • The user explicitly asks to pause or stop.

Recovery Rules

  • If a command returns an error, fix the call order first, then continue.
  • If a card is clearly problematic (ambiguous prompt or wrong answer), use the following when needed:

- card override [CARD_ID] ... to revise content - card rm [CARD_ID] to remove an invalid card

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

90.41%
按下载量换算1,326

安全审计

VirusTotal

未展示

ClawScan

通过

Static analysis

通过

权限和风险

执行命令

安装流程涉及命令执行,可能通过 openclaw skills install srsa 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

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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