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/qdrant-scalingnpx skills add SecondLifes/code-intel --skill qdrant-scalinggit 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 | $0.00066 | $0.00513 |
| Opus 5 | $0.00033 | $0.00257 |
| Sonnet 5 | $0.00013 | $0.00103 |
| Haiku 4.5 | $0.00007 | $0.00051 |
Grade A, and why
qdrant-scaling 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 yesterday.
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 — 59 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Qdrant Scaling
Usage
| You say | What happens |
|---|---|
| "CodeIntel's index is getting huge / search is slow at scale" | Determine first whether it's data-volume, QPS, or tenant-count growth (below) — CodeIntel today is single-node/single-operator, so most scaling questions here start from a from-scratch decision, not an existing cluster. |
| Ambiguous/no specific scaling question | Ask what's actually growing (data volume vs. query throughput vs. tenant count) before recommending vertical vs. horizontal scaling. |
First determine what you're scaling for:
- data volume
- query throughput (QPS)
- query latency
- query volume
After determining the scaling goal, we can choose scaling strategy based on tradeoffs and assumptions. Each pulls toward different strategies. Scaling for throughput and latency are opposite tuning directions.
Scaling Data Volume
This becomes relevant when volume of the dataset exceeds the capacity of a single node. Read more about scaling for data volume in Scaling Data Volume
Scaling for Query Throughput
If your system needs to handle more parallel queries than a single node can handle, then you need to scale for query throughput.
Read more about scaling for query throughput in Scaling for Query Throughput
Scaling for Query Latency
Latency of a single query is determined by the slowest component in the query execution path. It is in sometimes correlated with throughput, but not always. It might require different strategies for scaling.
Read more about scaling for query latency in Scaling for Query Latency
Scaling for Query Volume
By query volume we understand the amount of results that a single query returns. If the query volume is too high, it can cause performance issues and increase latency.
Tuning for query volume is opposite might require special strategies.
Read more about scaling for query volume in Scaling for Query Volume
What ships with it
8 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- minimize-latency/SKILL.md 2.7 KB
- scaling-data-volume/horizontal-scaling/SKILL.md 2.9 KB
- scaling-data-volume/SKILL.md 1.9 KB
- scaling-data-volume/sliding-time-window/SKILL.md 4.5 KB
- scaling-data-volume/tenant-scaling/SKILL.md 2.7 KB
- scaling-data-volume/vertical-scaling/SKILL.md 4.5 KB
- scaling-qps/SKILL.md 3.6 KB
- scaling-query-volume/SKILL.md 1.4 KB
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.
- yesterday First seen · 59 lines · 66 tokens per session scan A a76667dab71e
qdrant-scaling is a skill published in the GitHub repository SecondLifes/code-intel (2 stars, last pushed 21d ago), licensed Apache-2.0. It adds 66 tokens to every session and 513 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 2026-08-31.
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