By The Agent Ledger — AI-native learning system for people who consume more than they retain.
Your agent tracks what you're learning, prompts you to review key concepts, and makes sure knowledge actually sticks — and gets applied.
The Problem
You read 30 articles a week. You listen to podcasts. You take courses. But 3 months later, you can't recall the key insight from that book that "changed everything." The consumption-to-retention ratio for most people is terrible.
This skill turns your AI agent into a learning partner that captures, organizes, reviews, and connects knowledge over time.
Setup
Step 1: Create State File
Create learning/learning-state.md in your workspace:
# Learning State
## Active Learning Tracks
| Track | Type | Source | Started | Progress | Priority |
|-------|------|--------|---------|----------|----------|
| [Topic] | Book | [Title by Author] | YYYY-MM-DD | 35% | High |
| [Topic] | Course | [Course name — platform] | YYYY-MM-DD | 60% | Medium |
| [Topic] | Self-directed | [Description] | YYYY-MM-DD | Ongoing | Low |
## Learning Goals (This Quarter)
1. [Finish X] — deadline: [date]
2. [Understand Y well enough to Z] — no deadline, but priority
3. [Build skill in Z] — measurable: [what would prove it]
## Review Schedule
- Daily: Quick review of yesterday's captures (2 min)
- Weekly: Full review + connect to existing knowledge (15 min)
- Monthly: Retention check on older material
Step 2: Create Captures Directory
Create learning/captures/ for individual learning notes. One file per source:
# [Source Title]
**Type:** Book / Article / Course / Podcast / Video / Experience
**Author/Creator:** [name]
**Date captured:** YYYY-MM-DD
**Status:** Reading / Completed / Abandoned
**Rating:** [1-5] how valuable
## Key Takeaways
1. [Most important insight]
2. [Second most important]
3. [Third]
## Memorable Quotes
> "[Quote]" — [context/page]
## How This Applies to My Work
- [Specific application to your projects/business]
- [Connection to something you already know]
## Questions It Raised
- [Things you want to explore further]
## Review History
| Date | Recall Score | Notes |
|------|-------------|-------|
| YYYY-MM-DD | 4/5 | Remembered core thesis, fuzzy on details |
Step 3: Add to AGENTS.md
## Learning
- When I share an article, book note, or learning, capture it in `learning/captures/`
- During weekly reviews, prompt me on items due for review
- Connect new learnings to existing captures when relevant
- Track my learning goals in `learning/learning-state.md`
- Suggest review when I haven't revisited a high-value capture in 30+ days
Usage Patterns
"I just read [article/book/watched video]"
Agent creates a capture file, asks targeted questions to extract key takeaways:
- "What was the single most important idea?"
- "How does this relate to what you're working on?"
- "What would you do differently based on this?"
"Add [book/course] to my reading list"
Agent updates learning-state.md with new entry, suggests priority based on current goals and active tracks.
"What should I review?"
Agent checks review dates across captures and surfaces:
- Items never reviewed (captured but never revisited)
- Items due for spaced repetition (7 days → 30 days → 90 days)
- High-value captures (rating 4-5) that haven't been reviewed in 30+ days
"Connect this to what I already know"
Agent searches existing captures for related concepts and shows connections:
## Connections Found
- **[New capture]** relates to **[Existing capture]**
- Shared concept: [what they have in common]
- Tension: [where they disagree or offer different perspectives]
- Combined insight: [what you get from both together]
"Learning review"
Weekly format:
## Weekly Learning Review — [Date]
### This Week's Captures
| Source | Type | Rating | Key Insight |
|--------|------|--------|-------------|
| [Title] | Article | 4/5 | [One-liner] |
### Review Prompts (Spaced Repetition)
1. **[Capture from 7 days ago]:** Can you recall the main argument? [Y/N → update recall score]
2. **[Capture from 30 days ago]:** What was the key takeaway? [Y/N]
3. **[Capture from 90 days ago]:** How has this influenced your thinking? [Reflect]
### Learning Track Progress
| Track | Last Week | This Week | Notes |
|-------|-----------|-----------|-------|
| [Topic] | 35% | 42% | Read chapters 8-10 |
### Applications This Week
- Applied [concept from X] to [project/decision]
- [Concept from Y] contradicted my assumption about [Z]
### Next Week Focus
- Priority reading: [what to focus on]
- Review due: [N items need revisiting]
"Quiz me on [topic/capture]"
Agent generates questions from a capture to test retention:
- Recall questions ("What were the 3 types of X?")
- Application questions ("How would you apply X to your current project?")
- Synthesis questions ("How does X relate to Y that you learned last month?")
"What have I learned about [topic]?"
Agent searches all captures for a topic and synthesizes:
## Knowledge Summary: [Topic]
### Sources (chronological)
1. [Source 1] — [date] — Key point: [...]
2. [Source 2] — [date] — Key point: [...]
### Synthesized Understanding
[What you know about this topic, combining all sources]
### Evolution of Thinking
- Initially thought: [earlier captures]
- Now believe: [later captures, updated views]
### Gaps
- Still unclear on: [questions that remain]
- Should read: [suggested next sources]
Spaced Repetition Schedule
The skill uses a simple spaced repetition system:
| Review | After | Purpose |
|---|---|---|
| First | 1 day | Immediate recall check |
| Second | 7 days | Short-term retention |
| Third | 30 days | Medium-term retention |
| Fourth | 90 days | Long-term retention |
| Ongoing | 180 days | Refresh if still relevant |
Each review updates the recall score (1-5):
- 5: Perfect recall, can explain to others
- 4: Good recall, minor details fuzzy
- 3: Partial recall, needed prompting
- 2: Vague recall, couldn't explain it
- 1: No recall, basically learning it fresh
Items scoring 1-2 get moved to a shorter review cycle. Items scoring 5 consistently can be marked "internalized" and removed from active review.
Reading List Management
## Reading List
### Currently Reading
| # | Title | Author | Type | Started | Progress | Priority |
|---|-------|--------|------|---------|----------|----------|
| 1 | [Book] | [Author] | Book | 3/1 | 45% | 🔴 High |
| 2 | [Course] | [Platform] | Course | 2/15 | 80% | 🟡 Medium |
### Up Next (Prioritized)
| # | Title | Author | Type | Why | Added |
|---|-------|--------|------|-----|-------|
| 1 | [Book] | [Author] | Book | [Reason/recommendation] | 2/28 |
| 2 | [Course] | [Platform] | Course | [Reason] | 3/1 |
### Completed (Last 90 Days)
| Title | Type | Rating | Capture? | Key Insight |
|-------|------|--------|----------|-------------|
| [Book] | Book | 4/5 | ✅ | [One-liner] |
### Abandoned
| Title | Type | Why Abandoned | Worth Revisiting? |
|-------|------|--------------|-------------------|
| [Book] | Book | Too basic for current level | No |
Customization
Learning Styles
Adapt capture format to how you learn:
- Visual learner: Include diagram descriptions, spatial relationships
- Practical learner: Emphasize "How to apply" section, include exercises
- Conceptual learner: Focus on frameworks, mental models, principles
- Social learner: Note who recommended it, discussion points, teaching opportunities
Domain-Specific Templates
Create capture templates for common source types:
- Technical books: Code examples section, implementation notes
- Business books: Framework summary, when-to-use guide
- Research papers: Methodology notes, limitations, replication potential
- Podcasts: Timestamped highlights, guest background
Knowledge Areas
Track breadth across learning domains:
## Knowledge Map
| Domain | Captures | Last Active | Depth |
|--------|----------|-------------|-------|
| Business strategy | 12 | 2 days ago | Deep |
| Machine learning | 3 | 45 days ago | Surface |
| Writing | 7 | 10 days ago | Moderate |
| Finance | 15 | 1 day ago | Deep |
Integration with Other Skills
| Skill | Integration |
|---|---|
| goal-tracker | Learning goals as quarterly OKRs; capture progress as KR updates |
| research-assistant | Research findings auto-captured as learning entries |
| content-calendar | Turn high-value captures into content (teach what you learn) |
| writing-assistant | Use captures as source material for articles and posts |
| decision-log | Reference relevant learnings when making decisions |
| habit-tracker | Track daily reading/learning as a habit |
Heartbeat Integration
Add to HEARTBEAT.md:
## Learning Check
- Any captures due for spaced repetition review?
- Has it been >7 days since last capture? (consumption without capture = waste)
- Any learning tracks stalled for >14 days?
Troubleshooting
| Issue | Fix |
|---|---|
| Captures too long | Limit to 3 key takeaways max; depth goes in references |
| Never reviewing | Set up cron for weekly review prompt; start with just 3 items |
| Too many active tracks | Cap at 3 simultaneous; finish or pause before starting new ones |
| Captures feel useless | Focus "How This Applies" section — if you can't fill it, the source may not be valuable |
| Reading list growing forever | Monthly prune: remove anything added 60+ days ago that you haven't started |
| Can't recall anything | Normal for first 30 days; spaced repetition needs 2-3 cycles to work |
Privacy & Safety
- All learning data stays in your local workspace
- No external API calls for tracking (pure file-based)
- Captures may contain copyrighted quotes — keep for personal use only
- Agent never auto-shares your reading list or learning notes
Part of the Agent Skills Collection by The Agent Ledger. Subscribe at theagentledger.com for the premium guide and new skills.