
Remember
This is a lightweight memory system that lets Claude store learnings in PostgreSQL with embeddings for later retrieval. You invoke it with `/remember` followed by what you learned, and it auto-categorizes the entry into types like WORKING_SOLUTION, ERROR_FIX, or FAILED_APPROACH based on keywords. The real value is accumulating context across sessions so you're not solving the same problem twice. It extracts tags, assigns confidence levels, and returns an ID for each entry. The auto-detection is simple pattern matching on words like "error" or "didn't work", so explicit types are better when you want precision. Most useful in long-running projects where institutional knowledge tends to evaporate between debugging sessions.
- skills.sh installs
- 523
- GitHub stars
- 3.9K
- last updated
- Aug 21, 2026
Install
$ npx -y skills add parcadei/continuous-claude-v3 --skill remember --agent claude-codeInstalls into .claude/skills of the current project.
Files
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
- Stores the learning in PostgreSQL with BGE embeddings
- Auto-detects learning type if not specified
- Extracts tags from content
- 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:
Sponsors
Details
- Repository
- github.com/parcadei/continuous-claude-v3
- Category
- Productivity & Planning
- First seen
- Jun 3, 2026
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