SKILL.md
SKILL.mdBrowse 1 file
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Snapshot 0df3e4b
Mem0 Remember
Store a fact or learning directly into mem0.
Execution
Step 1: Extract the content
The user provides the content as an argument: /mem0-remember <text>
If no text was provided, ask: "What should I remember?"
Step 2: Classify the memory
Based on the content, pick the best metadata.type:
| Content signal | Type |
|---|---|
| "we decided...", "always use...", "never..." | decision |
| "X doesn't work because...", "don't try..." | anti_pattern |
| "I prefer...", "use X instead of Y" | user_preference |
| "the convention is...", "we always..." | convention |
| "learned that...", "figured out..." | task_learning |
| setup, env, tooling, config | environmental |
| anything else | task_learning |
Step 3: Store
Call add_memory with:
text="<the user's text>"user_id=<active_user_id>app_id=<active_project_id>metadata={"type": "<classified_type>", "branch": "<active_branch>", "confidence": 1.0, "source": "remember_command"}infer=False
infer=False because the user stated the fact explicitly — no extraction needed.
confidence=1.0 because the user explicitly asked to store this.
Step 4: Confirm
The add_memory response returns event_id (not memory_id) because writes are async.
Call get_event_status(event_id=<event_id>) once.
- If status is
SUCCEEDED: print the memory ID from the result. - If status is
PENDINGorprocessing: print with the event ID as fallback.
Remembered as <type>: "<content, first 80 chars>"
Memory ID: <id from event status>
Append ... only if content was truncated (longer than 80 chars).
Output formatting
IMPORTANT: Do NOT use markdown in your output. OpenCode TUI renders text verbatim — markdown like bold, ## headers, and | table | syntax appears as raw characters. Use plain text with indentation for structure. Use dashes for lists. Use spaces to align columns instead of markdown tables.
Discovery context
Discovered by repository scan. No exact path reference found in the snapshot’s root AGENTS.md.