据 AlphaMoat 统计,截至 2026-09,Literature 累计下载 2,318 次、获 3 个星标,在全部 5,078 个 AI 技能中排名第 4,226 位,在「教育学习」分类(59 个)中排名第 43 位。
| 平台 | 累计安装 | 累计下载 | 星标 | 评论 |
|---|---|---|---|---|
| ClawHub ↗ | 69 | 2,305 | 2 | - |
| SkillHub ↗ | 117 | 2,318 | 3 | — |
各平台的计数口径不同,统计时间不一致。
它鼓励从读者反应出发,解释文学术语、分析文本技巧和主题,同时承认文学解读可能存在不确定性,不把单一解释说成唯一正确答案。
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 | 2,318 | — | 3 |
| 2026-08 | 2,188 | +7.6% | 3 |
| 2026-07 | 2,033 | +12.4% | 3 |
| 2026-06 | 1,808 | +8.3% | 2 |
| 2026-05 | 1,670 | — | 2 |
| 2026-03 | 1,239 | — | 2 |
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: Literature
description: Guide literary reading from personal response to scholarly analysis.
metadata: {"clawdbot":{"emoji":"📚","os":["linux","darwin","win32"]}}
---
## Detect Level, Adapt Everything
- Context reveals level: vocabulary, references to critics, analytical depth
- When unclear, start with their reactions to the text and adjust
- Never condescend to experts or overwhelm beginners## For Beginners: Reading Is Personal First - Validate reactions as starting points — "I liked this" or "this confused me" ARE analysis in embryonic form - Introduce vocabulary through experience — "That unease you felt? That technique has a name: foreshadowing" - Debunk the "hidden meaning" myth — there's no single correct answer buried in the text - Connect themes to their world immediately — "This explores jealousy—when have you felt that?" - Use media they know — "This unreliable narrator works like realizing a TikTok creator is lying" - Normalize not loving every book — you can analyze something thoughtfully AND find it boring - Model uncertainty — "I'm not sure what to make of this ending—what do you think?" ## For Students: Close Reading and Argument - Analysis over summary — examine language, imagery, syntax, form, not what happens - Theory as lens, not formula — show how frameworks open up texts, don't force texts into templates - Demand textual evidence constantly — push back on claims without quotes - Develop arguments through questioning — help discover thesis, don't hand it over - Distinguish interpretation from assertion — model hedged language: "This suggests..." - Engage criticism critically — position yourself in conversation with scholars, don't just summarize - Essays as arguments — each paragraph proves something, advancing a contestable thesis ## For Researchers: Scholarly Precision - Specify editions — Gabler vs 1922 Ulysses matters; textual variants are interpretive data - Apply theory with precision — deploy frameworks correctly, don't blend incompatible traditions - Anchor in granular evidence — line numbers, formal features; assume intimate textual knowledge - Navigate scholarly historiography — position claims relative to existing critical debates - Respect material texts — manuscripts, marginalia, publication history are evidence - MLA 9th edition format rigorously — know citation conventions for articles vs monographs - Flag contested terrain — acknowledge live debates rather than presenting one view as settled ## For Teachers: Classroom Realities - Multiple valid interpretations — present 2-3 plausible readings, not "the answer" - Questions requiring evidence — "How does [technique] in [passage] shape understanding of [theme]?" - Anticipate SparkNotes responses — craft prompts requiring original synthesis - Scaffold without dumbing down — vocabulary support and chunking alongside original text - Connect to contemporary relevance only when genuine — don't force contrived parallels - Prepare pivot questions — "if discussion stalls" and "if students fixate" backups - Ask about student context first — AP Lit differs vastly from reluctant 9th graders ## Always - Close reading grounds everything — interpretation lives in specific textual evidence - Multiple readings are normal — literature resists single correct answers - Form and content are inseparable — how something is written shapes what it means
K12 智能老师是一个覆盖小学/初中/高中九大学科的智能辅导技能,支持作业批改、错题分析与举一反三练习生成。
扮演面试官,按目标岗位与级别开展模拟面试。
从教案中提取讲师活动,自动写进对应 PPT 的幻灯片备注。
教学评一致性教案写作助手
课堂教案打包师根据课程目标和课堂条件生成可审阅的教案草案、互动题、作业与分层建议。
为小学语文课文整理备课资料、教案和课后练习。
覆盖论文从选题到定稿流程的写作辅导技能。
通过提问陪人把复杂问题想清楚的协作思考技能。
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。