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Algernon Sprint

面向 OpenAlgernon 的定时交错学习冲刺:按 15/25/45 分钟从本地 SQLite 题库抽取到期卡片并跨材料乱序复习,论述题由 AI 评分,复习后经 FSRS 排程;结束时随机抽 5 张做检索测试并输出保留率与增益,同时把会话记录追加到当日日志。

antoniovfranco
antoniovfranco
热度 83.15
最近更新 2026-05-17 14:59:43
分类:知识依赖:代码执行 / 读写文件 / 内部调用大模型应用场景:测验 / 教学辅导

安装技能

复制下面的话术发给你的 Agent,它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill: npx secure-skills-cli add algernon-sprint
Metadata
name
algernon-sprint
description
Timed interleaved study sprint for OpenAlgernon. Use when the user runs `/algernon sprint [15|25|45]`, says "sprint de estudo", "sessao cronometrada", "25 minutos de revisao", "modo pomodoro", "quero fazer um sprint", or "revisar varios materiais de uma vez". Cards from all installed materials are shuffled and interleaved. Ends with a post-sprint retrieval test to measure retention gain.

algernon-sprint

You run a timed interleaved study sprint. Cards from all installed materials are shuffled together — interleaving different topics is the point, because it forces retrieval across contexts and strengthens long-term retention.

Constants

DB=/home/antonio/Documents/huyawo/estudos/vestibular/data/vestibular.db

Card Limits by Duration

Duration Max Cards
15 min 20 cards
25 min 35 cards
45 min 60 cards

Step 1 — Plan the Sprint

Fetch due cards across all materials:

sqlite3 $DB \
  "SELECT c.id, c.type, c.front, c.back, m.name as material
   FROM cards c
   JOIN card_state cs ON cs.card_id = c.id
   JOIN decks d ON d.id = c.deck_id
   JOIN materials m ON m.id = d.material_id
   WHERE cs.due_date <= date('now')
   ORDER BY RANDOM()
   LIMIT CARD_LIMIT;"

Interleave: shuffle so no two consecutive cards come from the same material. If there aren't enough due cards to fill the limit, use cards from the same material twice rather than having fewer than ~15 cards for a 25-min sprint.

Step 2 — Sprint Start

Display:

Sprint: [DURATION] minutes
Materials: [list of materials with at least one card]
Cards: [count]

AskUserQuestion: ["Start sprint"] Record start time.

Step 3 — Sprint Loop

Run the same card review flow as algernon-review:

  • Flashcards: show front → reveal back → Again/Good
  • Dissertative/Argumentative: show front → free-text answer → AI evaluate → Again/Good
  • After each grade, run FSRS scheduling (see algernon-review for FSRS formulas)

After every 10 cards, display:

Cards remaining: N  |  Estimated time: X min

Step 4 — Post-Sprint Break

After all cards reviewed:

Sprint complete. Take a 5-minute break.
Cards reviewed: N  |  Session retention: X%

AskUserQuestion: ["Start post-sprint test"]

Step 5 — Post-Sprint Retrieval Test

Select 5 random cards from the cards reviewed in this sprint. For each card:

  1. Show only the front.
  2. AskUserQuestion: ["Show answer"] — then show the back.
  3. AskUserQuestion options: ["Again", "Good"]
  4. Run FSRS update with the new grade.

Display:

Post-sprint test complete.
Sprint retention:      X%
Post-sprint retention: Y%
Session gain:          +Z%

The gain metric shows whether the sprint improved retention above what FSRS predicted — a positive gain means the interleaved practice worked.

Step 6 — Save Memory

Append to today's conversation log:

[HH:MM] sprint [DURATION]min
Cards: N | Sprint retention: X% | Post-sprint: Y% | Gain: +Z%