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/behaviorsnpx skills add ThinkfleetAI/memmesh --skill behaviorsgit 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/behaviors)<a href="https://agentmods.dev/skills/thinkfleetai/memmesh/behaviors"><img src="https://agentmods.dev/badge/skills/thinkfleetai/memmesh/behaviors.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 | $0.00062 | $0.00485 |
| Opus 5 | $0.00031 | $0.00243 |
| Sonnet 5 | $0.00012 | $0.00097 |
| Haiku 4.5 | $0.00006 | $0.00049 |
Grade A, and why
behaviors 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 5d 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
behaviors
⚙️ Requires MemMesh hosted mode. Calibrated prediction and behavior discovery run on the hosted engine — set your
mm-API key. On a local / open-source install these tools (memory_predict,memory_build_context) are not registered; if a call returns "unknown tool", tell the user this is a hosted capability and fall back tosearch/recallfor what's already known.
Show the patterns MemMesh discovered on its own. These behavior_pattern
memories are what predict projects forward — inspecting them explains the
forecasts.
List mined patterns (local MCP)
{ "name": "memory_search",
"arguments": { "type": "behavior_pattern", "projectId": "<repo>", "limit": 50 } }
Or scope to one subject and read them out of the context bundle:
{ "name": "memory_build_context",
"arguments": { "subjectKind": "user", "subjectId": "<id>", "include": ["patterns"] } }
Discover new patterns (hosted / SDK)
The discovery pass that finds patterns nobody predefined runs on the SDK:
const behaviors = await memory.behaviors.discover({ projectId: "myapp" });
// each: { pattern, prevalence, stability, evidenceMemoryIds }
Present them
For each pattern show: the behavior, how often it holds (prevalence), how stable it is over time (stability), and a couple of evidence memories. Rank by stability × prevalence — the strongest, most reliable habits first.
Why it matters
A vector-recall memory layer can only return facts you already stated. MemMesh
derives structure — "books gym classes on Mondays", "reorders ~every 6 weeks" —
from raw observations. That derived structure is the input to predict and the
reason the predictions have provenance.
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.
- 5d ago First seen · 50 lines · 62 tokens per session scan A 04590297146c
behaviors is a skill published in the GitHub repository ThinkfleetAI/memmesh (441 stars, last pushed 10d ago), licensed Apache-2.0. It adds 62 tokens to every session and 485 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.
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