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/search-speed-optimizationnpx skills add SecondLifes/code-intel --skill search-speed-optimizationgit 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 | $0.00078 | $0.01102 |
| Opus 5 | $0.00039 | $0.00551 |
| Sonnet 5 | $0.00016 | $0.00220 |
| Haiku 4.5 | $0.00008 | $0.00110 |
Grade A, and why
qdrant-search-speed-optimization 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.
This is a copy
100% identical to qdrant-search-speed-optimization — 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 — 78 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Diagnose a problem
There the multiple possible reasons for search performance degradation. The most common ones are:
- Memory pressure: if the working set exceeds available RAM
- Complex requests (e.g. high
hnsw_ef, complex filters without payload index) - Competing background processes (e.g. optimizer still running after bulk upload)
- Problem with the cluster (e.g. network issues, hardware degradation)
Single Query Too Slow (Latency)
Use when: individual queries take too long regardless of load.
Diagnostic steps:
- Check if second run of the same request is significantly faster (indicates memory pressure)
- Try the same query with
with_payload: falseandwith_vectors: falseto see if payload retrieval is the bottleneck - If request uses filters, try to remove them one by one to identify if a specific filter condition is the bottleneck
Common fixes:
- Tune HNSW parameters: Fine-tuning search
- Enable in-memory quantization: Scalar quantization
- Reduce Vector Dimensionality with Matryoshka Models: Matryoshka Models
- Use oversampling + rescore for high-dimensional vectors Search with quantization
- Enable io_uring for disk-heavy workloads on Linux io_uring
Can't Handle Enough QPS (Throughput)
Use when: system can't serve enough queries per second under load.
- Reduce segment count (
default_segment_numberto 2) Maximizing throughput - Use batch search API instead of single queries Batch search
- Enable quantization to reduce CPU cost Scalar quantization
- Add replicas to distribute read load Replication
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 · 78 lines · 78 tokens per session scan A c98c969f7fef
qdrant-search-speed-optimization is a skill published in the GitHub repository SecondLifes/code-intel (2 stars, last pushed 21d ago), licensed Apache-2.0. It adds 78 tokens to every session and 1,102 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to qdrant-search-speed-optimization, differing in 0 lines, and is treated as a copy.
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