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Agent Skill · 研究检索

bookforge-learning-practice-auditor(Bookforge 学习实践审核员)

用于辅助安全审计、权限检查、凭据风险、认证流程和常见漏洞排查。它适合让 Agent 梳理敏感配置、检查依赖风险、分析鉴权逻辑或生成安全复核清单。使用时不能把工具输出直接当最终结论,涉及密钥、令牌、用户数据或生产系统时,应先确认最小权限、脱敏方式和操作边界。

bookforge-learning-practice-auditor 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 35,079。

总安装 2,140周安装 91GitHub Stars 公开资料未说明下载 750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-learning-practice-auditor(Bookforge 学习实践审核员)
来源仓库:https://github.com/quochungto/bookforge-learning-practice-auditor
安装命令:
openclaw skills install bookforge-learning-practice-auditor
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-learning-practice-auditor

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。

审核学习习惯与课程设计剔除无效方法。bookforge-learning-practice-auditor 属于研究检索类 Skill,可作为该场景下的辅助能力补充。适合教育研究者、培训师与终身学习者。对比实证研究成果提出替代性高效实践。输出为通用建议,需适配个体认知风格。不涉及具体学科知识,专注元学习方法论。

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-learning-practice-auditor;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-learning-practice-auditor 怎么安装?

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

bookforge-learning-practice-auditor 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-learning-practice-auditor 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-learning-practice-auditor 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-learning-practice-auditor 用的人多吗?

公开数据是累计安装 2,140 次,近一周安装 91 次,下载 750 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-learning-practice-auditor 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-learning-practice-auditor

Bookforge 学习实践审核员 · 本页

2,140—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
learning-practice-auditor
description
|
version
1.0.0
homepage
https://github.com/bookforge-ai/bookforge-skills/tree/main/books/make-it-stick/skills/learning-practice-auditor
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
[1, 2, 3, 5, 6]
tags
depends-on
execution
tier
1
mode
hybrid
inputs
description
Description of current study habits, training design, or learning approach — can be a verbal description, a course outline, a study schedule, or a list of current practices
tools-required
[]
tools-optional
[Read, Write]
mcps-required
[]
environment
Any agent environment. Works from a verbal description or a document. No file access required.
discovery
goal
Identify which ineffective learning practices are present, classify each by name and severity, explain the mechanism that makes each feel effective but produces weak retention, and route to the appropriate corrective skill.
tasks
audience
["students", "teachers", "corporate trainers", "instructional designers", "coaches", "lifelong learners"]
triggers
not_for
quality
completeness_score
accuracy_score
value_delta_score

Learning Practice Auditor

When to Use

Something about how you or your learners study is producing less retention than expected. You may be putting in hours that do not convert to durable learning. You may be designing training that graduates confident-feeling but underprepared learners.

This skill is the diagnostic hub: it identifies which of five named ineffective practices are present, explains the mechanism creating the illusion of learning, rates the severity, and routes to the specific corrective skill.

Typical entry points:

  • Learner: "I study for hours but blank on tests"
  • Learner: "I highlight everything but still can't recall it"
  • Teacher or trainer: "Why aren't my learners retaining this?"
  • Instructional designer: "Is there anything wrong with this training structure?"
  • Anyone asking: "Am I studying the wrong way?"

Mode: Hybrid — The agent runs the audit and produces the report. The human and/or agent then invokes the recommended corrective skills.

This skill diagnoses. It does not redesign. After the audit, each corrective action names the skill to invoke next.


Step 1: Intake — Gather Current Practices

Why this step: Anti-pattern detection requires knowing what the learner or designer is actually doing, not what they think they are doing. Many learners describe their method as "studying" without specifying what that means. The intake questions force specificity.

Ask the following. If the user has already provided answers in their message, extract the answers without asking again.

Required

  1. What are you trying to learn, and why?

(Subject, course, skill, domain — and the performance event: exam, job task, certification, presentation)

  1. Walk me through a typical study session from start to finish.

(What does the learner actually do, in sequence, with the material?)

  1. How do you decide when you know something well enough to move on?

(The mastery signal — feeling of familiarity, re-reading smoothly, immediate practice score, etc.)

  1. How is the learning structured over time?

(One long session vs. spaced sessions; one topic completed before the next; mixed topics within a session)

Helpful if available

  1. Do you have a course outline, training schedule, or study plan I can read?

→ If yes, use Read to scan it for blocked/massed structure signals.

  1. What does assessment look like? (Multiple-choice, essay, performance demo, on-the-job transfer)
  1. Have you already taken any tests or received feedback on your performance?

If context is still insufficient after asking, note the gap in the audit report and flag it as a finding ("Unable to assess [pattern] — insufficient information about [X]").


Step 2: Anti-Pattern Detection

Why this step: Each anti-pattern has specific detection signals that appear in how the learner describes their practice. This step converts free-form description into a structured finding list.

Check each anti-pattern against the intake answers. Mark each as Detected, Absent, or Insufficient information.

AP-1: Rereading Trap

What to look for:

  • "I review my notes / re-read the chapter / highlight the key parts"
  • Primary study method is reading or looking at material rather than testing recall
  • Mastery signal is "it feels familiar" or "I can follow along when I read it"
  • Study sessions described as long reading or annotation sessions

Mechanism (explain this to the user when detected): Each time you reread well-organized text, your processing becomes faster and smoother — a state called *fluency*. That fluency feels like understanding and mastery, but it is really your brain's familiarity with the visual form of the text. It cannot distinguish between "I have encoded this in long-term memory" and "I have seen these words before." On a test, you must retrieve — reconstruct from memory — and fluency does not train that. The 2010 study: students who took a recall test after reading retained 50% more a week later than those who reread. Crammers in a 1978 study forgot 50% of their initial recall within two days; retrieval-practice groups forgot only 13%.

Corrective action: Replace rereading sessions with self-testing sessions → invoke retrieval-practice-study-system


AP-2: Massed Practice Delusion

What to look for:

  • "I do all the problems for chapter 5 before moving to chapter 6"
  • Training sessions run drill repetitions on one skill until it feels solid, then move on
  • Study sessions are concentrated blocks on one topic
  • Cramming before an exam described as the primary retention strategy

Sub-pattern — Blocked Practice Trap: Problems or practice tasks are grouped by type. This removes the critical "what kind of problem is this?" sorting decision — which is exactly what real performance requires. College students practicing geometry volumes: blocked practice averaged 89% correct during training but only 20% on a test one week later. Interleaved practice: 60% during training, 63% on the test — a 215% improvement in transfer. (Ch3)

Sub-pattern — Familiarity Trap: After massed repetition, the material feels familiar (System 1 recognition). But the test requires System 2 retrieval and application. Rapid, blocked practice encodes learning in a cognitively simpler neural representation than spaced, varied practice — less flexible, less transferable.

Mechanism: Rapid repetition draws on short-term working memory. Durable encoding requires a consolidation process that unfolds over hours and days. Massed practice produces fast visible gains that feel like mastery, but the gains are not consolidated. In the surgical microsurgery study: residents who had 4 lessons spaced one week apart outperformed those trained in a single day on *all* measures — elapsed time, movement efficiency, and success rate — one month later. 16% of the massed-practice group damaged tissue beyond repair.

Corrective action: Replace massed and blocked practice with spaced, interleaved, and varied practice → invoke desirable-difficulty-classifier to classify present and absent difficulty strategies and get redesign recommendations.


AP-3: Illusions of Knowing Cluster

What to look for:

  • Mastery signal is "it feels familiar" or "I recognized the answer when I saw it"
  • High confidence before a test, low score after
  • "I studied this — I don't know why I blanked"
  • Learner or trainer expresses surprise at gaps revealed on assessment
  • Anyone relying on "just reading through" as a review method

Note: This anti-pattern overlaps with AP-1 and AP-2. Log it separately when the primary problem is *miscalibrated confidence*, not just the study method used.

Mechanism: You rely on System 1 signals — fluency, familiarity, ease of processing — to judge mastery. These signals are unreliable. The Dunning-Kruger research: students at the 12th percentile in logic ability believed they were at the 68th percentile on average. Fluency illusion: re-reading a clear text increases the sensation that it is simple and already known. Hindsight bias: after learning the answer, you feel you "knew it all along," which makes you underestimate future study needs.

Corrective action: Run a calibration cycle to identify the specific distortion and install reliable mastery signals → invoke learning-calibration-audit


AP-4: Learning Styles Myth

What to look for:

  • "I'm a visual learner, so I only use videos"
  • Training is segmented by assessed learning style
  • Learner or designer says matching style to instruction is important for retention
  • Learner avoids certain modalities because they are "not their style"

The Pashler Validity Test: For a study to prove learning styles theory, it must: (1) classify learners by style, (2) randomly assign them to style-matched vs. style-mismatched instruction on the same content, (3) show that style-matched learners outperform style-mismatched learners. In the 2008 Pashler et al. review: no published study meets this design standard. Those that do flatly contradict the theory. Studies found that matching instruction to the *nature of the content* (visual for geometry, verbal for poetry) benefits *all* learners regardless of their style preference.

Mechanism: Learning preferences are real — but preference does not equal differential effectiveness. A learner's self-reported "style" predicts what they *prefer*, not what produces the best retention. Designing instruction around style matching misses the variance drivers that actually matter: prior knowledge, retrieval practice, spacing, and structure-building ability. More than 70 competing style theories exist with contradictory dimensions and no cross-validation — itself a strong signal the construct lacks scientific coherence.

Corrective action: Drop style-based instructional segmentation; shift to content-nature matching and apply evidence-based strategies to all learners regardless of style preference → invoke desirable-difficulty-classifier


AP-5: Errorless Learning Myth

What to look for:

  • Training shows the answer before asking the learner to attempt it
  • Flash cards are answer-first or answer-alongside
  • Instructor corrects immediately before learner generates a response
  • Stated rationale is "we don't want to practice errors"

Mechanism: Generating an answer — even a wrong one — before seeing the correct answer strengthens the subsequent memory of the correct answer more than passive presentation. This is the *generation effect*: word pairs where learners had to fill in a missing letter (foot → s_ _e) produced better later recall than pairs presented intact (foot → shoe). Errors made in *generative attempts* followed by corrective feedback are part of the encoding process, not its enemy. Preventing the attempt removes the cognitive effort that creates durable encoding.

Key distinction: Uncorrected misconceptions are harmful. The solution is corrective feedback — not preventing the attempt.

Corrective action: Restructure practice to require generation before exposure — pre-tests, cued retrieval, retrieval-first flash cards → invoke retrieval-practice-study-system


Step 3: Classify Severity

Why this step: Not all detected anti-patterns require the same urgency. Severity determines what to fix first and what to route immediately vs. note for later.

For each detected anti-pattern, assign a severity:

SeverityCriteriaAction
CriticalThis is the *primary* study or training method; no evidence-based alternative is in useImmediate redesign — route to corrective skill now
ModerateAnti-pattern coexists with some effective strategies but is undermining themTargeted replacement of the identified component
LowPresent but minor; dominant strategy is already evidence-basedFlag for next redesign cycle

Step 4: Produce the Audit Report

Why this step: A written report makes findings actionable. The learner or designer can share it, return to it, and use the corrective routing as a checklist.

Structure the report as follows:

## Learning Practice Audit — [Subject / Program Name]

### Summary
[1-3 sentence overview: how many anti-patterns found, dominant severity level, top recommendation]

### Findings

#### [Anti-Pattern Name] — Severity: [Critical / Moderate / Low]
**Detected signals:** [What in their description triggered this finding]
**Mechanism:** [Why it feels effective but produces weak retention — in plain language]
**Evidence:** [1-2 specific research findings with numbers]
**Corrective action:** [Named replacement practice] → invoke `[skill-name]`

[Repeat for each detected anti-pattern]

### What to Do Next
[Prioritized list of corrective skills to invoke, in order of severity]
1. [Corrective skill] — addresses [anti-pattern(s)]
2. ...

### What Is Already Working
[Any detected evidence-based practices — note and reinforce these]

Examples

Example 1 — Individual Student (Critical: Rereading + Massed Practice)

User description: "I study organic chemistry by reading the chapter twice, then doing all the practice problems for each reaction type before moving on. I feel pretty solid during my sessions but I've failed two midterms."

Audit findings:

  • AP-1: Rereading Trap — Critical. Primary review method is re-reading. The fluency gained from reading the chapter twice is not producing retrievable memory. The learner is using feeling-solid as the mastery signal, which tracks fluency not recall.
  • AP-2: Massed Practice Delusion (Blocked Practice) — Critical. All practice problems for one reaction type before moving to the next = blocked massed practice. This eliminates the "which type of problem is this?" discrimination skill that the exam actually tests. The 20% vs. 63% result on blocked vs. interleaved geometry test is directly applicable.
  • AP-3: Illusions of Knowing — Moderate. "I feel pretty solid during sessions but failed two midterms" = textbook miscalibration. The mastery signal is unreliable.

Corrective routing:

  1. retrieval-practice-study-system — replace re-reading with self-quizzing and build spacing schedule
  2. desirable-difficulty-classifier — redesign the problem practice to interleave reaction types
  3. learning-calibration-audit — install reliable mastery signals to replace the fluency-feeling

Example 2 — Corporate Trainer (Moderate: Learning Styles Design)

User description: "We have our training team assess employees' learning styles before onboarding. Visual learners get diagrams-first modules; auditory learners get voice-over walkthroughs. We invested in this system two years ago."

Audit findings:

  • AP-4: Learning Styles Myth — Critical. The entire curriculum segmentation is organized around a theory with no empirical validation (Pashler et al., 2008). Investment in assessment tools and differentiated modules is producing no learning benefit relative to matched instruction. The content-nature matching finding is relevant: geometry-style content (spatial workflows, process maps) should be visual for *all* learners, regardless of assessed style.

Corrective routing:

  1. desirable-difficulty-classifier — audit the onboarding modules for evidence-based difficulty strategies that will benefit all learners regardless of style
  2. Redirect the investment in style assessment toward spaced-practice scheduling and retrieval-based knowledge checks

Example 3 — Teacher (Low: Partial Massed Practice)

User description: "I do a mixed review at the end of each unit. I mostly teach one topic per day but sometimes bring in earlier material. Students self-quiz once a week using flashcards."

Audit findings:

  • AP-2: Massed Practice — Low. One topic per day is still somewhat blocked, but the weekly mixed review and flashcard self-quizzing are evidence-based practices that partially offset this. The dominant strategy is already better than pure massed practice.
  • No other anti-patterns detected from this description.

Recommendation: This practice is already above average. The flashcard self-quizzing is the highest-value element — reinforce and expand it. For the next redesign cycle: consider interleaving topics within sessions, not just end-of-unit reviews.


Reference

Full evidence profiles, all research citations, and detailed sub-pattern descriptions for each anti-pattern: → references/anti-pattern-reference-table.md

For mechanism details on illusions of knowing, see also: → learning-calibration-audit references

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

Install related skills from ClawhHub:

  • clawhub install bookforge-retrieval-practice-study-system
  • clawhub install bookforge-desirable-difficulty-classifier
  • clawhub install bookforge-learning-calibration-audit

Or install the full book set from GitHub: bookforge-skills

平台分布

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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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