algernon-sprint
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.
Best use case
algernon-sprint is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
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.
Teams using algernon-sprint should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/algernon-sprint/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How algernon-sprint Compares
| Feature / Agent | algernon-sprint | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
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.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# 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%
```Related Skills
sprint-os
5-minute sprint operating system for AI agents. Autonomous execution cycles: ASSESS → PLAN → SCOPE → EXECUTE → MEASURE → ADAPT → LOG → NEXT. Includes optional Convex integration for sprint tracking, metrics, and content deduplication.
algernon-texto
Block-by-block reading mode for OpenAlgernon materials. Use when the user runs `/algernon texto SLUG`, `/algernon paper SLUG`, says "quero ler [material]", "vamos ler [topic] bloco a bloco", "modo texto", or "leitura guiada". Also activates when the user is mid-session and selects /continue between blocks. Paper mode adds structured reflection between major sections.
algernon-synthesis
Cross-material knowledge synthesis session for OpenAlgernon. Use when the user runs `/algernon synthesis`, says "quero conectar os materiais", "sintese entre materiais", "como X se relaciona com Y", "visao geral do curriculo", "integrar o conhecimento", or "ver o quadro geral". Requires at least 2 materials with reviewed cards. Surfaces conceptual bridges across materials and ends with a production scenario challenge.
algernon-review
FSRS-4.5 flashcard review session for OpenAlgernon. Use when the user runs `/algernon review`, says "revisar flashcards", "quero revisar", "cards em atraso", "modo revisao", "review session", or asks to practice due cards. Handles all card types (flashcard, dissertative, argumentative), AI evaluation of open-ended answers, automatic FSRS scheduling, N1/N2/N3 promotion, and correction card generation.
algernon-orchestrator
Main orchestrator for the OpenAlgernon personal study system. Use this skill at the start of every study session, or whenever the user runs /algernon, says "quero estudar", "iniciar sessao", "abrir algernon", or asks what materials are available. Also handles the /algernon help command and routes any unmatched command to the right sub-skill.
algernon-interview
Mock technical interview mode for OpenAlgernon. Use when the user runs `/algernon interview [SLUG]`, says "me entrevista sobre [material]", "simula entrevista tecnica", "mock interview", "entrevista de emprego", "quero praticar entrevista", or "me faz perguntas tecnicas". Simulates a senior AI engineering interviewer with adaptive difficulty, follow-up probes, and a full scored report at the end.
algernon-feynman
Feynman Technique study session for OpenAlgernon. Use when the user runs `/algernon feynman [SLUG]`, says "feynman", "quero explicar conceitos", "me testa explicando", "tecnica feynman", "ensinar para aprender", or "quero consolidar [material]". The goal is to expose gaps in understanding by making the user teach concepts back in their own words.
algernon-debate
Design trade-off debate mode for OpenAlgernon. Use when the user runs `/algernon debate [SLUG]`, says "quero debater [topic]", "me desafia sobre trade-offs", "debate tecnico", "discutir decisoes de design", "quando usar X vs Y", or "argumento tecnico". Forces the user to defend a position and exposes nuances they may not have considered. Ends with a synthesis that is exactly what you would say in a technical interview.
Sprint Retrospective Facilitator
Run structured sprint retrospectives that produce real action items — not vague "we should communicate better" fluff.
Sprint Planner
Plan, scope, and run agile sprints that actually ship. No ceremony bloat.
---
name: article-factory-wechat
humanizer
Remove signs of AI-generated writing from text. Use when editing or reviewing text to make it sound more natural and human-written. Based on Wikipedia's comprehensive "Signs of AI writing" guide. Detects and fixes patterns including: inflated symbolism, promotional language, superficial -ing analyses, vague attributions, em dash overuse, rule of three, AI vocabulary words, negative parallelisms, and excessive conjunctive phrases.