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/secondlifes/code-intel/scaling-qpsnpx skills add SecondLifes/code-intel --skill scaling-qpsgit clone --depth 1 https://github.com/SecondLifes/code-intelWhat 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 | $0.00061 | $0.00843 |
| Opus 5 | $0.00030 | $0.00421 |
| Sonnet 5 | $0.00012 | $0.00169 |
| Haiku 4.5 | $0.00006 | $0.00084 |
Grade A, and why
qdrant-scaling-qps 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.
This is a copy
100% identical to qdrant-scaling-qps — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 57 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Scaling for Query Throughput (QPS)
Throughput scaling means handling more parallel queries per second. This is different from latency - throughput and latency are opposite tuning directions and cannot be optimized simultaneously on the same node.
High throughput favors fewer, larger segments so each query touches less overhead.
Performance Tuning for Higher RPS
- Use fewer, larger segments (
default_segment_number: 2) Maximizing throughput - Enable quantization pinned in RAM to reduce disk IO:
memory: pinnedon Qdrant 1.19 or newer,always_ram: trueon 1.18 or older Quantization - Use batch search API to amortize overhead Batch search
Minimize impact of Update Workloads
- Configure update throughput control (v1.17+) to prevent unoptimized searches degrading reads Low latency search
- Set
optimizer_cpu_budgetto limit indexing CPUs (e.g.2on an 8-CPU node reserves 6 for queries) - Configure delayed read fan-out (v1.17+) for tail latency Delayed fan-outs
Horizontal Scaling for Throughput
If a single node is saturated on CPU after applying the tuning above, scale horizontally with read replicas.
- Shard replicas serve queries from replicated shards, distributing read load across nodes
- Each replica adds independent query capacity without re-sharding
- Use
replication_factor: 2+and route reads to replicas Distributed deployment
See also Horizontal Scaling for general horizontal scaling guidance.
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 · 57 lines · 61 tokens per session scan A 9db744ddacd4
qdrant-scaling-qps is a skill published in the GitHub repository SecondLifes/code-intel (2 stars, last pushed 22d ago), licensed Apache-2.0. It adds 61 tokens to every session and 843 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to qdrant-scaling-qps, differing in 0 lines, and is treated as a copy.
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