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 skills/mem0ai/mem0/pinnpx skills add mem0ai/mem0 --skill pingit clone --depth 1 https://github.com/mem0ai/mem0Wrote 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/skills/mem0ai/mem0/pin)<a href="https://agentmods.dev/skills/mem0ai/mem0/pin"><img src="https://agentmods.dev/badge/skills/mem0ai/mem0/pin.svg" alt="Measured on agentmods" 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 | $0.00042 | $0.00565 |
| Opus 5 | $0.00021 | $0.00282 |
| Sonnet 5 | $0.00008 | $0.00113 |
| Haiku 4.5 | $0.00004 | $0.00056 |
Grade A, and why
pin 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 4d 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.
What it actually says
Mem0 Pin
Pin a memory to mark it as high-priority and protect from pruning.
Execution
Step 1: Find the memory
The user provides either a search query or memory ID.
If memory ID:
- Call
get_memorywith the ID.
If search query:
- Call
search_memorieswith the query,filters={"AND": [{"user_id": "<id>"}, {"app_id": "<pid>"}]},top_k=5. - Show numbered list with content previews.
- Ask: "Which memory to pin? Enter a number."
Step 2: Read current content
Call get_memory with the selected memory ID. Store:
original_text— the memory's text contentoriginal_metadata— the existingmetadatadict
Step 3: Pin it
The MCP update_memory tool only accepts memory_id, text, and source — it
does not accept a metadata parameter. To pin, append a pin marker to the text:
pinned_text = "[PINNED] " + original_text if not original_text.startswith("[PINNED]") else original_text
update_memory(memory_id=<selected_id>, text=pinned_text)
For new memories (user wants to pin text that isn't stored yet):
- Call
add_memorywith:text="[PINNED] <the user's text>"user_id=<active_user_id>app_id=<active_project_id>metadata={"pinned": true, "type": "decision", "confidence": 1.0}infer=False
- The response contains
event_id. Callget_event_status(event_id=<event_id>)once to retrieve the memory ID, then confirm.
Step 4: Confirm
Pinned: "<memory content, first 80 chars>"
Memory ID: <id>
Append ... only if content exceeds 80 characters.
Unpin
If the user says "unpin":
- Call
get_memoryto read current content. - Remove the pin marker from the text:
unpinned_text = original_text.removeprefix("[PINNED] ") update_memory(memory_id=<id>, text=unpinned_text) - Print:
Unpinned: "<content>..."
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.
- 4d ago First seen · 68 lines · 42 tokens per session scan A 7f957673a179
pin is a skill published in the GitHub repository mem0ai/mem0 (64,662 stars, last pushed today), licensed Apache-2.0. It adds 42 tokens to every session and 565 once invoked, about $0.0002 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 2026-08-30.
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