Borrowing it
Nothing to install: this file belongs to Smart-AI-Memory/empathy-framework. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/Smart-AI-Memory/empathy-framework/main/.claude/commands/learning.mdgit clone --depth 1 https://github.com/Smart-AI-Memory/empathy-frameworkWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/commands/smart-ai-memory/empathy-framework/learning)<a href="https://agentmods.dev/commands/smart-ai-memory/empathy-framework/learning"><img src="https://agentmods.dev/badge/commands/smart-ai-memory/empathy-framework/learning/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/commands/smart-ai-memory/empathy-framework/learning"><img src="https://agentmods.dev/badge/commands/smart-ai-memory/empathy-framework/learning.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00013 | $0.00963 |
| Opus 5.5 | $0.00005 | $0.00385 |
| Sonnet 5.5 | $0.00003 | $0.00193 |
| Haiku 4.5 | $0.00001 | $0.00096 |
Grade A, and why
learning scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 156 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Learning Management
Aliases: /learn-hub
IMPORTANT: This command operates on the CURRENT conversation. Do NOT start a new conversation or clear context. You have full access to the conversation history above this point.
Manage continuous learning, pattern extraction, and preferences.
Quick Examples
/learning # Interactive menu
/learning "evaluate session" # Analyze for patterns
/learning "show patterns" # View learned patterns
Discovery
Question:
header: "Action"
question: "What would you like to do with learning?"
options:
- label: "Evaluate session"
description: "Analyze current session for learning opportunities"
- label: "View patterns"
description: "See patterns learned from previous sessions"
- label: "Teach something"
description: "Manually teach a preference or pattern"
Evaluate Session
Analyze the CURRENT session for learning opportunities.
Context: You have access to ALL messages in this conversation above this command. Use that history to identify patterns.
I will:
- Review the conversation history IN THIS SESSION (all messages above)
- Identify:
- Corrections you made to my responses
- Preferences you expressed (coding style, verbosity, etc.)
- Effective approaches that worked well
- Workarounds we discovered together
- Extract patterns with confidence scores
- Store valuable patterns to patterns/ or .claude/rules/
- Report what was learned
Best after:
- Productive debugging sessions
- When you corrected my approach
- Discovering project conventions
- Finding effective solutions
Note: If invoked at the start of a session with no history, will report "no patterns to evaluate".
View Patterns
See patterns learned from previous sessions.
I will:
- Load pattern storage
- Display patterns by category:
- Preferences
- Corrections
- Workarounds
- Project conventions
- Error solutions
- Show confidence and usage stats
- Allow filtering and search
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2mo ago First seen · 156 lines · 13 tokens per session scan A 12b9bbe8a38f
learning is a command published in the GitHub repository Smart-AI-Memory/empathy-framework (13 stars, last pushed 6mo ago), licensed Apache-2.0. It adds 13 tokens to every session and 963 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other commands, from other repositories
context-save
Save project context, decisions, and progress for a later session.
export-closedloop-learnings
Exports pending ClosedLoop learnings to global location with deduplication.
recall-save
Generate / overwrite .recall/context.md with Recall's local offline summarizer.
recall
Recall what MemoRizz remembers about a topic, or about this project.
import-memory
Import a portable MemoRizz memory archive.
pack
Pack target repo with repomix. Snapshot for AI context. Cached per featureid.