remember

Store a learning, pattern, or decision in the memory system for future recall.

422 stars

Best use case

remember is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Store a learning, pattern, or decision in the memory system for future recall.

Teams using remember 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

$curl -o ~/.claude/skills/remember/SKILL.md --create-dirs "https://raw.githubusercontent.com/vibeeval/vibecosystem/main/skills/remember/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/remember/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How remember Compares

Feature / AgentrememberStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Store a learning, pattern, or decision in the memory system for future recall.

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.

SKILL.md Source

# Remember - Store Learning in Memory

Store a learning, pattern, or decision in the memory system for future recall.

## Usage

```
/remember <what you learned>
```

Or with explicit type:

```
/remember --type WORKING_SOLUTION <what you learned>
```

## Examples

```
/remember TypeScript hooks require npm install before they work
/remember --type ARCHITECTURAL_DECISION Session affinity uses terminal PID
/remember --type FAILED_APPROACH Don't use subshell for store_learning command
```

## What It Does

1. Stores the learning in PostgreSQL with BGE embeddings
2. Auto-detects learning type if not specified
3. Extracts tags from content
4. Returns confirmation with ID

## Learning Types

| Type | Use For |
|------|---------|
| `WORKING_SOLUTION` | Fixes, solutions that worked (default) |
| `ARCHITECTURAL_DECISION` | Design choices, system structure |
| `CODEBASE_PATTERN` | Patterns discovered in code |
| `FAILED_APPROACH` | What didn't work |
| `ERROR_FIX` | Specific error resolutions |

## Execution

When this skill is invoked, run:

```bash
cd $CLAUDE_OPC_DIR && PYTHONPATH=. uv run python scripts/core/store_learning.py \
  --session-id "manual-$(date +%Y%m%d-%H%M)" \
  --type <TYPE or WORKING_SOLUTION> \
  --content "<ARGS>" \
  --context "manual entry via /remember" \
  --confidence medium
```

## Auto-Type Detection

If no `--type` specified, infer from content:
- Contains "error", "fix", "bug" → ERROR_FIX
- Contains "decided", "chose", "architecture" → ARCHITECTURAL_DECISION
- Contains "pattern", "always", "convention" → CODEBASE_PATTERN
- Contains "failed", "didn't work", "don't" → FAILED_APPROACH
- Default → WORKING_SOLUTION

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