Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add rules/dasomel/oh-my-cursor/learnergit clone --depth 1 https://github.com/dasomel/oh-my-cursorWhat 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 | $0.00013 | $0.00642 |
| Opus 5 | $0.00006 | $0.00321 |
| Sonnet 5 | $0.00003 | $0.00128 |
| Haiku 4.5 | $0.00001 | $0.00064 |
Grade A, and why
learner 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 yesterday.
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 — 83 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Learner Workflow
The Learner extracts reusable patterns, conventions, and lessons from the current session and saves them as .mdc rule files or updates existing ones.
Triggers
- "learn this", "save this pattern", "remember this", "extract rule"
When to Use
- After discovering a project convention
- After solving a recurring problem
- After the user corrects a pattern multiple times
- When a debugging technique proves effective
- When a coding pattern should be consistently applied
Protocol
Step 1: Identify the Pattern
Ask yourself:
1. What did I learn that applies beyond this specific task?
2. Is this a project convention, a general practice, or a debugging technique?
3. Would this prevent future mistakes if codified?
4. Is it specific enough to be actionable?
Step 2: Classify the Pattern
| Type | Directory | Example |
|---|---|---|
| Coding convention | rules/practices/ |
"Always use named exports" |
| Agent behavior | rules/agents/ |
"Always check for null before accessing nested properties" |
| Workflow improvement | rules/workflows/ |
"Run lint before commit" |
| Project-specific | .cursor/rules/ |
"Use camelCase for API endpoints in this project" |
Step 3: Create or Update Rule
For new rules:
Create a .mdc file with proper frontmatter:
---
description: "Concise description of what this rule enforces"
alwaysApply: false # or true for universal rules
globs: # optional: auto-attach for matching files
- "**/*.ts"
---
For existing rules:
Read the existing rule, then append the new pattern to the appropriate section.
Step 4: Validate
1. Read the created/updated rule file
2. Verify it's well-formed YAML frontmatter + Markdown
3. Check it doesn't conflict with existing rules
4. Confirm with user: "Saved pattern: {description}. Applied to: {scope}"
Output Format
## Learned Pattern
**Pattern**: {what was learned}
**Source**: {what triggered the learning — user correction, repeated issue, etc.}
**Saved to**: {file path}
**Scope**: {when this rule activates}
**Conflicts**: {none | list of potential conflicts with existing rules}
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.
- yesterday First seen · 83 lines · 13 tokens per session scan A 6c5fc5e7cb16
learner is a cursor rule published in the GitHub repository dasomel/oh-my-cursor (2 stars, last pushed 5mo ago), licensed MIT. It adds 13 tokens to every session and 642 once invoked, about $0.0001 per session on Opus 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 2026-08-31.
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