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/thinkfleetai/memmesh/importnpx skills add ThinkfleetAI/memmesh --skill importgit clone --depth 1 https://github.com/ThinkfleetAI/memmeshWhat 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 | $0.00055 | $0.00372 |
| Opus 5 | $0.00028 | $0.00186 |
| Sonnet 5 | $0.00011 | $0.00074 |
| Haiku 4.5 | $0.00006 | $0.00037 |
Grade A, and why
import 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 2d 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
import
Load durable knowledge into MemMesh.
1. Read the source
Accept: a MemMesh export (Markdown or JSONL), a native MEMORY.md /
CLAUDE.md, an ADR folder, or a mem0 export dump.
2. Feed it in — prefer observe
For prose / notes / decisions, memory_observe each item so the engine
re-extracts and builds the knowledge graph:
{ "name": "memory_observe", "arguments": { "text": "<one fact/decision>", "projectId": "<repo>" } }
For a MemMesh JSONL backup where you must preserve exact ids/timestamps, use
memory_save per row instead.
3. Scope it
Project rules/decisions → scope: "project" with projectId. Personal
preferences → scope: "user" with userId. Batch by scope so it's consistent.
4. Idempotency & verify
memory_observe dedupes re-observed text, so a re-run is safe. After import,
run stats to confirm the count, and peek a couple of known facts to confirm
they're retrievable.
Importing a full mem0/Zep setup (not just a file)? Use
memmesh-migrate— it audits call sites, maps the API, and reconciles counts.
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.
- 2d ago First seen · 44 lines · 55 tokens per session scan A 3393a215c13b
import is a skill published in the GitHub repository ThinkfleetAI/memmesh (440 stars, last pushed 7d ago), licensed Apache-2.0. It adds 55 tokens to every session and 372 once invoked, about $0.0003 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.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
agent-host-chat-contributions
Build and review cross-cutting agent-host chat behavior through lifecycle contributions. Use when adding turn lifecycle side effects, prompt or context injection, restored-history transformation, protocol-action observation, or when reviewing changes that add code to AgentSideEffects or AgentService.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.