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 skills add ThinkfleetAI/memmesh --skill memmesh-migrategit clone --depth 1 https://github.com/ThinkfleetAI/memmeshWrote 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/thinkfleetai/memmesh/memmesh-migrate)<a href="https://agentmods.dev/skills/thinkfleetai/memmesh/memmesh-migrate"><img src="https://agentmods.dev/badge/skills/thinkfleetai/memmesh/memmesh-migrate/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/thinkfleetai/memmesh/memmesh-migrate"><img src="https://agentmods.dev/badge/skills/thinkfleetai/memmesh/memmesh-migrate.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Rogue Agent · line 61 Skill establishes unauthorized persistence across sessions via cron jobs, startup scripts, or state files. Session persistence allows an attacker to maintain access beyond the current interaction.Fix: Remove any persistence mechanisms (cron jobs, startup scripts, state files). Skills should not maintain state across sessions without explicit user consent.
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 5.1 | $0.00185 | $0.01094 |
| Opus 5.5 | $0.00074 | $0.00438 |
| Sonnet 5.5 | $0.00037 | $0.00219 |
| Haiku 4.5 | $0.00018 | $0.00109 |
Grade A, and why
memmesh-migrate 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 1mo 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.
How it starts
The opening of the file, as written. The whole thing — 84 lines — stays where its author put it; the contents beside it link to each section on GitHub.
memmesh-migrate
Move an existing memory setup onto MemMesh with a reviewable, reversible plan.
Canonical sources (fetch first)
- Docs / API mapping: https://docs.memmesh.ai/llms.txt
- Delegate call-site code to:
memmesh-sdk(hosted) /memmesh-cli(local)
Step 1 — audit the incumbent
Detect what's in use and record .memmesh-migration/audit.md:
- Vendor & SDK (Mem0
MemoryClient/Memory, Zep, Letta, LangChain memory, bare Qdrant/pgvector, …). - Call sites — every
add/search/get_all/update/delete. - Scoping — how
user_id/agent_id/run_id/sessionmap today. - Data volume — roughly how many memories, and where they live.
Step 2 — API mapping (cite in the plan)
| Incumbent (e.g. Mem0) | MemMesh equivalent | Notes |
|---|---|---|
client.add(text, user_id=…) |
memory.observe({ text, userId }) |
MemMesh's engine decides what to save — you can stop pre-filtering. |
client.search(q, user_id=…) |
memory.search({ query, userId }) |
Same shape; MemMesh adds scope hierarchy + status lifecycle. |
client.get_all(user_id=…) |
memory.list({ userId }) |
|
client.update(id, text) |
memory.observe(new) + memory.supersede(oldId, newId) |
Correction keeps provenance instead of destructive overwrite. |
client.delete(id) |
memory.delete({ id }) (soft) / hard:true (GDPR) |
|
| user / agent / run scoping | userId / agentId / sessionId (+ projectId, platformId) |
6-level hierarchy. |
| (no equivalent) | lattice.predict / predictTarget, behaviors.discover, context.queryGraph |
This is why you're migrating — calibrated prediction the source can't do. |
Step 3 — data migration
- Export from the incumbent (its export API or a
get_alldump to JSONL). - Transform each record to a MemMesh
observe(preferred — lets the engine re-extract and build the graph) OR amemory.savewith an explicit id when you must preserve exact rows. - Import in batches; keep a checkpoint file so a re-run is idempotent.
- Reconcile — count source vs destination, sample-search for known facts,
write
.memmesh-migration/reconcile.md.
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.
- 1mo ago First seen · 84 lines · 185 tokens per session scan A 3cfab322aa6d
memmesh-migrate is a skill published in the GitHub repository ThinkfleetAI/memmesh (405 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 185 tokens to every session and 1,094 once invoked, about $0.0007 per session on Opus 5.5. 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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