安全扫描（ClawHub）

• VirusTotal ：可疑
• OpenClaw ：可疑

OpenClaw 评估

The skill's runtime instructions reference and operate on a hard-coded, user-specific local SQLite database path and an undeclared DB environment variable, which is inconsistent with the published metadata and could access private local data without clear user configuration.

安装（复制给龙虾 AI）

将下方整段复制到龙虾中文库对话中，由龙虾按 SKILL.md 完成安装。

请把本段交给龙虾中文库（龙虾 AI）执行：为本机安装 OpenClaw 技能「Algernon Sprint」。简介：Timed interleaved study sprint for OpenAlgernon. Use when the user runs `/alger…。
请 fetch 以下地址读取 SKILL.md 并按文档完成安装：https://raw.githubusercontent.com/openclaw/skills/refs/heads/main/skills/antoniovfranco/algernon-sprint/SKILL.md
（来源：yingzhi8.cn 技能库）

SKILL.md

打开原始 SKILL.md（GitHub raw）

---
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:

```bash
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%
```