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.
Best use case
algernon-texto is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
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.
Teams using algernon-texto 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-texto/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How algernon-texto Compares
| Feature / Agent | algernon-texto | 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?
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.
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.
Related Guides
AI Agents for Marketing
Discover AI agents for marketing workflows, from SEO and content production to campaign research, outreach, and analytics.
AI Agents for Startups
Explore AI agent skills for startup validation, product research, growth experiments, documentation, and fast execution with small teams.
AI Agents for Coding
Browse AI agent skills for coding, debugging, testing, refactoring, code review, and developer workflows across Claude, Cursor, and Codex.
SKILL.md Source
# algernon-texto
You deliver material content block by block with an interactive tool menu after
each block. The goal is active reading — the user engages with each block before
moving on.
## Constants
```bash
ALGERNON_HOME="${ALGERNON_HOME:-$HOME/.openalgernon}"
DB="${ALGERNON_HOME}/data/study.db"
MATERIALS="${ALGERNON_HOME}/materials"
NOTION_CLI="${NOTION_CLI:-notion-cli}"
```
## Step 1 — Load Material
```bash
sqlite3 "$DB" "SELECT id, name, local_path FROM materials WHERE slug = 'SLUG';"
```
If no result, stop: "Material 'SLUG' not found. Run `list` to see installed materials."
Read `LOCAL_PATH/algernon.yaml` to get:
- `content`: list of content files
- `sections`: section titles mapped to file names
Read all content files and split into blocks of approximately 300 words each.
Preserve section boundaries — never split mid-sentence at a section change.
## Step 2 — Display Session Header
```
================================================
SLUG — mode: texto (or: paper)
N blocks total
================================================
```
## Step 3 — Block Delivery Loop
For each block, display:
```
────────────────────────────────────────────────
Block N/TOTAL · SECTION_TITLE
────────────────────────────────────────────────
[Block content]
────────────────────────────────────────────────
/continue /explain [term] /example
/analogy /summarize /test
/map /deep-dive
────────────────────────────────────────────────
```
Present as an AskUserQuestion with the tool options above.
### Tool Behaviors
| Tool | What to do |
|---------------|----------------------------------------------------------------------------|
| `/continue` | Advance to the next block |
| `/explain X` | Define X at N1 level first. Ask if user wants N2 before going deeper. |
| `/example` | Give a concrete real-world example of the main concept in this block |
| `/analogy` | Create an original analogy that maps the concept to something familiar |
| `/summarize` | Summarize the block in 2-3 sentences; ask user to add anything missed |
| `/test` | Ask 1 quick comprehension question about this block; give feedback |
| `/map` | Show how this concept connects to others already covered in this material |
| `/deep-dive` | Expand the block's core concept to N2/N3 depth; note as focus for cards |
After any tool response, re-display the current block menu so the user can
continue or use another tool.
### Paper Mode Additions
In paper mode, content is structured as:
Abstract → Methodology → Results → Implications
Between sections, before showing the first block of the new section:
> "Summarize what you understood from [previous section] before we continue."
(Free text — acknowledge and move on without grading.)
Track which terms the user used `/explain` or `/deep-dive` on. Pass this list
to card generation at the end as additional focus concepts.
## Step 4 — Session End
When the last block is delivered and the user selects /continue:
```
Material complete: MATERIAL_NAME
Sections covered: N
Key concepts explored: [list of terms where user used /explain or /deep-dive]
```
### Generate Cards
Generate cards for this material. Follow the card generation rules in
`algernon-content`:
- Distribution: 50% flashcard, 30% dissertative, 20% argumentative
- All cards start at N1
- Prioritize concepts from the `/explain` and `/deep-dive` list
### Save to Notion (optional)
If `$NOTION_CLI` is available and `$NOTION_PAGE_ID` is set:
```bash
"$NOTION_CLI" append --page-id "$NOTION_PAGE_ID" --content "MARKDOWN"
```
Content to include: key concepts (N1/N2/N3), concepts the user explored deeply,
flashcards generated. This step is skipped silently if Notion is not configured.
### Save Memory
Append a summary to today's conversation log:
```bash
echo "[HH:MM] texto session -- MATERIAL_NAME | Blocks: N/TOTAL | Cards: N" \
>> "${ALGERNON_HOME}/memory/conversations/YYYY-MM-DD.md"
```Related Skills
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-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.
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.
---
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.
find-skills
Helps users discover and install agent skills when they ask questions like "how do I do X", "find a skill for X", "is there a skill that can...", or express interest in extending capabilities. This skill should be used when the user is looking for functionality that might exist as an installable skill.
tavily-search
Use Tavily API for real-time web search and content extraction. Use when: user needs real-time web search results, research, or current information from the web. Requires Tavily API key.
baidu-search
Search the web using Baidu AI Search Engine (BDSE). Use for live information, documentation, or research topics.