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Agent Skill · 研究检索

bookforge-retrieval-practice-study-system(Bookforge 检索练习学习系统)

用于搭建或维护带检索增强的 RAG 工作流,适合让 Agent 处理知识库问答、向量检索、来源引用和事实核查。它可以辅助整理数据接入、Embedding、向量库、召回参数和回答生成流程。使用时需要确认数据来源、更新频率、召回阈值和引用展示方式,避免把未命中的资料或过期内容包装成确定事实。

bookforge-retrieval-practice-study-system 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为调用外部服务;许可证为 MIT-0,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 24,850。

总安装 3,911周安装 168GitHub Stars 公开资料未说明下载 1,371核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT-0
版本
1.0.0

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:bookforge-retrieval-practice-study-system(Bookforge 检索练习学习系统)
来源仓库:https://github.com/quochungto/bookforge-retrieval-practice-study-system
安装命令:
openclaw skills install bookforge-retrieval-practice-study-system
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 bookforge-retrieval-practice-study-system

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

用于搭建或维护带检索增强的 RAG 工作流,适合处理知识库问答、向量检索、来源引用和事实核查。适用于需要接入数据源、配置 Embedding 模型、设置向量库参数及生成可信回答的场景。使用时需确认数据来源、更新频率、召回阈值和引用展示方式,避免将未命中内容包装成确定事实。

通过 clawhub 安装,需确认权限范围和维护状态,注意是否涉及联网或文件读写操作。建议结合原始 README 核验具体用法,确保检索结果准确性和引用规范性。

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 OpenClaw;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 openclaw skills install bookforge-retrieval-practice-study-system;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要调用外部服务,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

bookforge-retrieval-practice-study-system 怎么安装?

在终端执行 openclaw skills install bookforge-retrieval-practice-study-system。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

bookforge-retrieval-practice-study-system 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

bookforge-retrieval-practice-study-system 需要什么权限?

权限级别是调用外部服务。该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

bookforge-retrieval-practice-study-system 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

bookforge-retrieval-practice-study-system 用的人多吗?

公开数据是累计安装 3,911 次,近一周安装 168 次,下载 1,371 次。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

bookforge-retrieval-practice-study-system 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 OpenClaw,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。
  • 需要许可证清楚、并且有第三方扫描结果的团队项目。

不太适合

  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
bookforge-retrieval-practice-study-system

Bookforge 检索练习学习系统 · 本页

3,911—调用外部服务MIT-0通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

name
retrieval-practice-study-system
description
Design a complete self-quizzing study system for any subject, course, or learning goal. Use this skill whenever the user wants to study more effectively, stop wasting time rereading notes, build a study schedule from learning material, prepare for exams, create flashcard decks with a spacing system, design a practice-quiz regimen, or turn any document into a retrieval-based learning plan — even if they don't mention "retrieval practice" or "spaced repetition." Works for students at any level, professionals upskilling, lifelong learners, and coaches designing training programs. Do NOT use this skill to evaluate whether a textbook or course is good (that is a different task), or to build automated quiz software (that requires a coding skill).
version
1.0.0
homepage
https://github.com/bookforge-ai/bookforge-skills/tree/main/books/make-it-stick/skills/retrieval-practice-study-system
metadata
{"openclaw":{"emoji":"📚","homepage":"https://github.com/bookforge-ai/bookforge-skills"}}
status
draft
source-books
title
Make It Stick: The Science of Successful Learning
authors
["Peter C. Brown", "Henry L. Roediger III", "Mark A. McDaniel"]
chapters
[2, 8]
tags
["learning-science", "cognitive-psychology", "evidence-based-learning", "study-skills", "self-testing", "active-recall"]
depends-on
[]
execution
tier
1
mode
hybrid
inputs
description
Study material, course outline, textbook chapters, lecture notes, or learning objectives
tools-required
[Read, Write]
tools-optional
[]
mcps-required
[]
environment
Any agent environment with file read/write access.
discovery
goal
Design a complete retrieval-based study system — quiz questions, spacing schedule, mastery signals, and anti-pattern guide — so the learner can replace passive rereading with active self-testing.
tasks
audience
["students", "lifelong learners", "teachers", "trainers", "coaches"]
triggers
not_for
environment
Document-based: study guides, lecture notes, course outlines, PDF chapters, learning objectives
quality
completeness_score
accuracy_score
value_delta_score

Retrieval Practice Study System

When to Use

You have a body of material to learn and want to build a structured, science-backed study system. Typical situations:

  • The user has a course, textbook, or document they need to master by a deadline
  • The user is frustrated that rereading does not produce real retention
  • The user wants flashcards but does not know how to space them or signal mastery
  • The user needs a study schedule that goes beyond "review the night before"
  • A teacher or trainer wants to design a low-stakes quiz regimen for their students

Before starting, verify:

  • Is the study material available to read? (If not, ask the user to share it or describe it)
  • What is the learning goal and timeline? (Exam date, job start date, presentation deadline)

Mode: Hybrid — The agent designs all study materials, question sets, and the schedule. The human executes the daily practice sessions.

Context & Input Gathering

Required Context (must have — ask if missing)

  • Study material: What must be learned? This is the source for generating questions.

→ Check for: uploaded files, linked documents, pasted notes, chapter summaries in the prompt → If missing, ask: "Please share the material you want to study — lecture notes, textbook chapters, or a course outline."

  • Learning goal: What does mastery look like? This determines question depth.

→ Check for: exam format (multiple-choice, essay, practical), job competency requirements, certification criteria → If missing, ask: "What will you be tested on or need to do with this knowledge?"

  • Timeline: When is the deadline or exam? This sets the spacing schedule.

→ Check for: dates mentioned in prompt, course syllabi, exam announcements → If missing, ask: "How much time do you have before you need to know this material?"

Observable Context (gather from environment)

  • Existing notes or highlights: Prior study attempts that reveal what the user already knows

→ Look for: annotated files, highlighted PDFs, previous flashcard decks → If unavailable: treat all material as new

  • Subject domain: Affects question type (factual recall vs. concept-application vs. procedure)

→ Look for: course name, subject tags, discipline cues in the material

Default Assumptions

  • If no exam format specified → generate a mix: 60% short-answer, 40% concept-connection
  • If no timeline specified → design a 4-week schedule with daily 20-minute sessions
  • If no prior knowledge indicated → assume starting from zero

Sufficiency Threshold

SUFFICIENT when ALL of these are true:
  ✓ Study material is available (or described in enough detail to generate questions)
  ✓ Learning goal is clear (what the learner must be able to do)
  ✓ Timeline is known (or default 4-week schedule is acceptable)
BLOCK if: no material and no description — cannot generate meaningful questions without content

Process

Step 1 — Analyze the Material and Extract Learning Targets

Read the study material and identify:

  • Key concepts: Core ideas the learner must understand (not just recognize)
  • Critical terms: Vocabulary with precise meanings that affect application
  • Relationships: How concepts connect, cause each other, or contrast
  • Procedures: Step-by-step processes or decision rules

WHY: Retrieval practice is most effective when questions target the deep structure of the material — the underlying principles — not just surface facts. Identifying learning targets first ensures the question set is prioritized, not exhaustive.

Output: A numbered list of 10-20 learning targets, ranked by importance to the learning goal.


Step 2 — Generate the Self-Quiz Question Set

For each learning target, write 1-3 questions. Prefer:

  • Short-answer questions that require the learner to produce the answer (not recognize it): "What happens to memory retention when retrieval is delayed by one week?"
  • Concept-connection questions that require relating ideas: "How does spacing interact with retrieval practice to strengthen long-term memory?"
  • Application questions that transfer learning to a new scenario: "A colleague is preparing a presentation. Which study strategy would you recommend and why?"

Avoid:

  • True/false questions (recognition is weaker than recall)
  • Questions answerable by a single memorized word

WHY: Research demonstrates that questions requiring the learner to produce an answer (short-answer, essay) yield significantly stronger long-term retention than recognition-based formats (multiple choice, true/false). The cognitive effort of generating an answer strengthens the neural pathway to that memory. When multiple-choice is necessary (e.g., matching a certification exam format), write questions with plausible distractors that require discrimination, not guessing.

IF the material is primarily procedural (e.g., a clinical protocol, a coding pattern): → Write sequence-recall questions: "List the steps of X in order" and error-identification questions: "What is wrong with this approach?"

IF the material is primarily conceptual (e.g., economic theory, learning science): → Weight toward explanation questions: "Explain why X happens" and comparison questions: "How does X differ from Y?"

Output: A question bank file (quiz-questions.md) with questions grouped by learning target.


Step 3 — Build the Spacing Schedule

Construct a tiered review schedule based on the timeline and confidence level:

Tier schedule (adjust for your timeline):

  • Session 0 (Day 1): Initial study of material + immediate self-quiz (all questions, no peeking)
  • Session 1 (Day 2-3): Review all missed questions from Session 0 + skim correct ones
  • Session 2 (Day 5-7): Quiz all questions again; retire cards answered correctly twice in a row to the "monthly" pile
  • Session 3 (Day 10-14): Quiz remaining active questions; add any new material
  • Monthly review: Pull the "retired" pile once a month and re-quiz — anything missed re-enters the active deck

For short timelines (exam in under 2 weeks):

  • Compress to every-other-day quizzing
  • Prioritize the highest-weight learning targets
  • Do not retire cards until after the exam

WHY: Spacing practice — leaving time between retrieval sessions — forces the brain to reconstruct the memory from long-term storage rather than working memory. This reconstruction process, which feels effortful and even frustrating, is precisely what strengthens long-term retention. Research shows cramming produces 50% forgetting within two days; spaced practice reduces forgetting to 10% over the same period.

Output: A study calendar file (study-schedule.md) with specific dates, session content, and time estimates.


Step 4 — Set Up the Leitner Box Progression

Organize flashcards (physical or digital) into 3-5 boxes with escalating review intervals:

BoxReview frequencyEntry ruleExit rule
Box 1 (Active)Every sessionAll new cards start hereAnswered correctly once → Box 2
Box 2Every other sessionFrom Box 1Correct again → Box 3
Box 3Once a weekFrom Box 2Correct again → Box 4
Box 4Once a monthFrom Box 3Correct again → Box 5
Box 5 (Mastered)Once a semester / before high-stakes eventsFrom Box 4Stays here unless missed → back to Box 1

Critical rule: If a card is answered incorrectly at any box level, it returns immediately to Box 1.

WHY: The Leitner system (a physical implementation of spaced repetition) ensures that difficult material receives more practice and easy material is not wasted on. The "any miss → Box 1" rule prevents the learner from self-deceiving about mastery — the moment a card is missed, it is treated as unlearned.

Output: Instructions for setting up the Leitner system in study-schedule.md, including the starting box assignment for all cards.


Step 5 — Define Mastery Signals

Mastery for a given concept is declared when ALL of these are true:

  1. The learner answers the question correctly without hesitation on 3 consecutive sessions
  2. The learner can explain the concept in their own words (not by reciting the source text)
  3. The learner can connect the concept to at least one other concept in the course
  4. The concept has been in Box 4 or Box 5 for at least one review cycle

Warning signals (study is not working — change approach):

  • Answering correctly immediately after reading but unable to recall 24 hours later → increase spacing interval
  • Feeling confident but scoring below 70% on a practice test → the fluency illusion is active; reduce rereading and increase self-quizzing
  • Unable to answer any question after a session → material is too complex; break learning targets into smaller sub-concepts

WHY: Without explicit mastery criteria, learners commonly experience the "fluency illusion" — the feeling of knowing that arises from familiarity with the text, not from actual command of the material. Familiarity is not retrievability. Defining mastery signals forces the learner to test their knowledge against objective criteria rather than subjective feeling.

Output: A mastery checklist section at the bottom of study-schedule.md.


Step 6 — Produce the Anti-Pattern Comparison

Write a one-page summary contrasting retrieval practice with rereading, specific to the learner's material:

Rereading (what feels productive but is not):

  • Highlights and color-coded notes create visual familiarity
  • Fluency with the text mimics the feeling of understanding
  • Results in 50-70% forgetting within one week
  • Produces overconfidence: learners believe they know material they cannot recall

Retrieval practice (what feels harder but works):

  • Self-quizzing feels awkward and slow — this discomfort is a signal it is working
  • Effort during recall strengthens the memory pathway
  • A single retrieval session boosts one-week retention by ~11%; three sessions "immunize" against forgetting
  • Correcting wrong answers after a retrieval attempt produces better learning than never having tried

WHY: Learners who understand the mechanism are more likely to tolerate the discomfort of self-quizzing. The awkward feeling of struggling to recall is cognitively identical to the process that makes memories durable. Naming this feeling in advance reduces the temptation to abandon the system.

Output: Anti-pattern guide appended to quiz-questions.md.


Inputs

InputRequiredDescription
Study materialYesText, notes, chapters, or course outline to learn
Learning goalYesWhat the learner must be able to do with this knowledge
TimelineYes (or accept default 4-week)Days until exam or competency is needed
Existing flashcards / notesNoPrior study artifacts that can seed the question bank
Exam formatNoAffects question type weighting

Outputs

OutputFormatDescription
quiz-questions.mdMarkdownFull question bank grouped by learning target, with anti-pattern guide
study-schedule.mdMarkdownDay-by-day schedule, Leitner box setup, mastery checklist

Output Template: study-schedule.md

# Study Schedule: [Subject]

**Learning goal:** [What you will be able to do]
**Exam / deadline:** [Date]
**Total sessions:** [N]

## Leitner Box Setup
- Box 1 (Active): [N] cards — review every session
- Box 2: [N] cards — review every other session
- Box 3: [N] cards — review weekly
- Box 4: [N] cards — review monthly
- Box 5 (Mastered): empty at start

## Session Calendar
| Date | Box(es) to quiz | Time estimate | Notes |
|------|----------------|---------------|-------|
| [Day 1] | All (Box 1) | 30 min | First pass — expect to miss most |
| [Day 2-3] | Box 1 (misses only) | 20 min | Focus on gaps |
| ... | ... | ... | ... |

## Mastery Checklist
For each learning target, check off when:
- [ ] Correct 3 consecutive sessions without hesitation
- [ ] Can explain in own words
- [ ] Can connect to at least one other concept
- [ ] In Box 4 or Box 5 for one full cycle

Key Principles

1. Retrieval, not review, is the learning event Reading creates familiarity; retrieval creates memory. After the first reading, every additional hour spent rereading yields far less retention than the same hour spent self-quizzing. The act of pulling a memory from storage — not the act of encoding it — is what makes it durable.

2. Desirable difficulty: effortful = effective The discomfort of struggling to recall something is not a sign of failure; it is the mechanism of learning. Research consistently shows that more effortful retrieval produces stronger retention than easy retrieval. If self-quizzing feels smooth and easy, the spacing interval is probably too short.

3. Spacing beats massing Distributing practice across days produces dramatically better long-term retention than the same amount of practice compressed into one session. The reason: spaced sessions require the brain to reconstruct the memory from long-term storage, reinforcing the neural pathway each time. Cramming draws from short-term memory and fades quickly.

4. Errors are learning events, not failures Getting a question wrong, then checking the correct answer and trying again, produces better learning than never having made the error. Wrong answers followed by corrective feedback are more effective than rereading alone. Do not avoid questions you expect to miss — seek them out.

5. Calibration beats confidence The fluency illusion (feeling like you know material because you can read it fluently) is one of the most reliable predictors of exam failure. Self-quizzing provides an objective measure of what you actually know, not what you feel you know. Use quiz scores, not reading speed or highlighting volume, to guide study decisions.

6. Interleaving deepens discrimination Mixing study of different topics or problem types — rather than blocking one topic at a time — helps the brain learn to identify which approach applies to which situation. This is harder and slower than blocked practice but produces superior transfer to new problems.

Examples

Example 1: Medical Student Preparing for a Physiology Exam

Scenario: A first-year medical student has four weeks before a comprehensive physiology exam. The course covers twelve organ systems. After two weeks of rereading notes and highlighting textbooks, they scored 65 on a practice exam. They need to change their approach.

Trigger: "I have a physiology exam in four weeks. I've been rereading my notes but not retaining anything. Help me study."

Process:

  1. Agent reads the student's notes and identifies 60 learning targets across the twelve systems
  2. Agent generates a 90-question bank weighted toward concept-connection and mechanism questions (e.g., "Trace the pathway by which decreased blood pressure triggers aldosterone release")
  3. Agent builds a 4-week spaced schedule: daily 25-minute sessions in weeks 1-2, 30-minute sessions in weeks 3-4 with backward-reaching review
  4. Agent sets up a 4-box Leitner system and designates mastery criteria for each organ system
  5. Agent writes an anti-pattern guide specific to physiology: why reading the textbook description of the cardiac cycle is not the same as being able to describe it

Output: quiz-questions.md (90 questions, grouped by system) and study-schedule.md (28-day calendar, Leitner assignments, mastery checklist)


Example 2: Professional Upskilling in Data Systems

Scenario: An engineer wants to deeply learn distributed systems concepts from a technical book. They have 6 weeks, no fixed exam, but a system design interview in 42 days. They've been reading chapters but feel like concepts slip away within a day.

Trigger: "I'm reading a technical book on distributed systems for a system design interview. Nothing is sticking. Can you build me a study system?"

Process:

  1. Agent reads the chapter summaries and identifies 35 learning targets (replication strategies, consensus protocols, storage engines, etc.)
  2. Agent generates a 50-question bank mixing definition recall ("What is the difference between strong and eventual consistency?") and application questions ("You are designing a leaderboard for a global gaming platform. Which consistency model would you choose and why?")
  3. Agent builds a 6-week schedule with interleaving across topic areas (storage + replication + consistency alternated, not blocked)
  4. Agent sets up a 3-box Leitner system (simpler, given the timeline) and defines mastery as "able to explain the concept whiteboard-style without notes"

Output: quiz-questions.md and study-schedule.md optimized for interview preparation


Example 3: Teacher Designing a Classroom Retrieval System

Scenario: A middle school social studies teacher wants to integrate low-stakes quizzing into their unit on ancient civilizations. They have 8 weeks of unit material and want a system that does not feel punitive to students.

Trigger: "I'm a teacher. Help me design a quiz system for my ancient civilizations unit that helps students retain information without stressing them out."

Process:

  1. Agent reads the unit outline and identifies 40 learning targets across Egypt, Mesopotamia, India, and China
  2. Agent generates a bank of 80 short-answer questions suitable for classroom use, with 3 quiz sets of approximately 5 questions each (pre-lesson, post-lesson, pre-exam)
  3. Agent builds an 8-week schedule with three quiz points per unit chapter: beginning of class (prior reading), end of class (today's lesson), and 24 hours before the chapter test
  4. Agent writes teacher notes explaining the 79%-vs-92% research result (Columbia Middle School science study) and how to frame quizzes to students as "practice, not a grade"
  5. Agent produces a student-facing anti-pattern card explaining why rereading notes feels productive but is not

Output: quiz-questions.md (80 questions in three quiz sets) and study-schedule.md (8-week calendar with classroom timing)


References

  • references/research-evidence.md — Key empirical studies: testing effect research (1917, 1939, 1978, 2005 Columbia Middle School, 2007 eighth-grade science), forgetting curve data, cramming vs. spacing retention comparisons
  • references/leitner-system-guide.md — Full Leitner box implementation guide including physical card setup and digital app equivalents
  • references/mastery-signals.md — Extended mastery criteria for different subject types (procedural, conceptual, declarative)
  • references/anti-patterns.md — Full comparison of 6 ineffective study strategies vs. retrieval practice alternatives

License

This skill is licensed under CC-BY-SA-4.0. Source: BookForge — Make It Stick: The Science of Successful Learning by Peter C. Brown, Henry L. Roediger III, Mark A. McDaniel.

Related BookForge Skills

This skill is standalone. Browse more BookForge skills: bookforge-skills

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权限和风险

调用外部服务

该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

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。目前只有一个公开来源,建议同时核对源仓库。

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