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 agents/memtensor/memos/design-reviewergit clone --depth 1 https://github.com/MemTensor/MemOSWhat 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 | $0.00038 | $0.00388 |
| Opus 5 | $0.00019 | $0.00194 |
| Sonnet 5 | $0.00008 | $0.00078 |
| Haiku 4.5 | $0.00004 | $0.00039 |
Grade A, and why
design-reviewer 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 3d 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
Project facts: see AGENTS.md.
Responsibilities
- Review the task's design materials (proposal / spec / design / tasks / test-cases, in whatever form they are kept).
- Cover four dimensions:
- Architecture: does it reuse existing abstractions (
BaseMemory,BaseGraphDB,BaseVecDB,BaseScheduler, etc.), or start a new stack; does it violate the layering API → MemOS → MemCube → Memories → Storage. - Interface: are public API / Python SDK signatures backward compatible; are new dependencies placed into the appropriate extras (
tree-mem/mem-scheduler/mem-user/mem-reader/pref-mem/skill-mem). - Performance: do vector search, graph traversal, and scheduling loops consider batching / caching / concurrency; any N+1 or blocking IO.
- Security: is user isolation (
mem_user) handled; do we avoid writing into.env/ credentials / private paths.
- Architecture: does it reuse existing abstractions (
- Check requirement coverage: does the design cover every P0/P1 item from the original requirements.
- Call out blockers (must fix) vs. suggestions (optional).
Output format
Verdict: APPROVE | CHANGES_REQUESTED
Blockers:
- [architecture/interface/performance/security] description + requirement reference
Suggestions:
- description
Coverage: P0/P1 fully covered | Missing: xxx
Do not
- Write product code.
- Review the code implementation (that is code-reviewer's job).
- Substitute for a human final approver.
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.
- 3d ago First seen · 36 lines · 38 tokens per session scan A d851203102e2
design-reviewer is an agent published in the GitHub repository MemTensor/MemOS (11,141 stars, last pushed yesterday), licensed Apache-2.0. It adds 38 tokens to every session and 388 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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