Learn Skill
SkillAI & modelsAdds a learn skill so your agent can pull key takeaways from a chat and save them to docs/learnings.md.
Use Learn Skill in Claude, ChatGPT or Ahel Desktop
Free. Sign in, add Learn Skill and connect your AI. About a minute.
Also: Claude Code · Cursor · Codex
Then ask your AI: use the Learn Skill skill
Details
Instructions available. Your AI can read the instructions. Execution depends on the setup they require.
Account requirements not reviewed. Check the skill instructions before use; ahel provides instructions and does not run this skill.
No other account needed.
Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.
About this skill
Extract key learnings from the current chat thread and save them to docs/learnings.md. Use this skill when the user types "/learn" or asks to save learnings from the conversation.
What this skill tells your AI
The instructions your AI receives, as published by builderio/agent-native in templates/analytics/.builder/skills/learn/SKILL.md and read by ahel’s review.
When to Use
- User types
/learnin chat - User asks to "save this as a learning", "remember this", or "add this to learnings"
- User corrects you and you want to persist that correction for future sessions
How It Works
- Review the full conversation thread
- Identify corrections, preferences, data mappings, workflow insights, and gotchas
- Save each learning via
POST /api/learn - Confirm what was saved
API Endpoint
POST /api/learn
Appends a structured entry to docs/learnings.md.
Structured entry (preferred for individual learnings):
{
"category": "User Preferences",
"insight": "Always filter out internal team emails when showing customer-specific activity",
"source": "User correction during customer dashboard session"
}
Raw markdown (for complex multi-line entries):
{
"rawMarkdown": "### Customer Data\n\n**Example Corp** org ID: `example-org-id`. Primary contact: jane@example.com."
}
GET /api/learn
Returns the current contents of docs/learnings.md as { content: string }.
Valid Categories
Use one of the existing section headers from docs/learnings.md:
Agent Behavior RulesCustomer DataUser PreferencesUI PatternsDashboard Data Fetching PatternReusable ScriptsCross-Referencing Customers Across Services
Use Other if none fit — the learning will be appended at the end of the file.
What to Extract
When reviewing a thread, focus on:
| Signal | Example |
|---|---|
| User corrections | "No, that metric should use signup not sign_up" |
| Data source mappings | "Example Corp org IDs are X, Y, Z" |
| Query patterns | "Always join on dim_hs_contacts for customer lookups" |
| Preferences | "I prefer stacked bar charts for per-user breakdowns" |
| Gotchas | "The data column is JSON — use JSON_VALUE() to extract" |
| Workflow insights | "Check Grafana before looking at code for incidents" |
Skip obvious or trivial observations. Each learning should be actionable — what to do, what not to do, and why.
Example Flow
User types /learn. Agent responds:
- Scan the thread for corrections and insights
- For each learning found, call
POST /api/learn:POST /api/learn { "category": "Customer Data", "insight": "Example Corp org ID is `example-org-id`", "source": "Thread with Steve" } - Summarize what was saved:
Saved 3 learnings to
docs/learnings.md:- Customer Data: Example Corp org ID is
example-org-id - User Preferences: Use dark theme for all exported charts
- Agent Behavior Rules: Always check Sentry before investigating code for error spikes
- Customer Data: Example Corp org ID is
Gotchas
- Always read
docs/learnings.mdfirst to avoid duplicating existing entries - Keep insights concise — one actionable point per entry
- Use the structured format (category + insight + source) for most entries; raw markdown only for complex multi-line content
- The
sourcefield is optional but helpful for traceability
Signals
- GitHub stars
- 7k
- Forks
- 635
- Last commit
- Oct 2026
Advanced
- Item type
- skill
- Key
learn-builderio- Source
- github.com/builderio/agent-native
github.com/builderio/agent-native
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