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-search-qualitynpx skills add SecondLifes/code-intel --skill qdrant-search-qualitygit 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 | $0.00122 | $0.00543 |
| Opus 5 | $0.00061 | $0.00271 |
| Sonnet 5 | $0.00024 | $0.00109 |
| Haiku 4.5 | $0.00012 | $0.00054 |
Grade A, and why
qdrant-search-quality 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.
What it actually says
Qdrant Search Quality
Usage
| You say | What happens |
|---|---|
| "Search results are bad/irrelevant" / "which embedding model" / "recall@k" | Check chunk quality first — CodeIntel's own chunker (src/chunker.py) already splits at statement boundaries specifically to avoid mid-sentence splits (this skill's own #1 quality killer), so a real regression usually means a chunker bug, not a Qdrant tuning issue. |
| "Should we add reranking / change hybrid fusion?" | This repo already does hybrid dense+sparse with its own weighted RRF fusion in src/retrieval.py — not Qdrant's built-in RRF query feature this skill assumes. Use its guidance to inform tuning decisions on top of that custom implementation, not as a drop-in Qdrant-side config change. |
| Ambiguous/no specific quality question | Test with exact search first to isolate whether the issue is retrieval or ranking. |
First determine whether the problem is the embedding model, Qdrant configuration, or the query strategy. Most quality issues come from the model or data, not from Qdrant itself. If search quality is low, inspect how chunks are being passed to Qdrant before tuning any parameters. Splitting mid-sentence can drop quality 30-40%.
- Start by testing with exact search to isolate the problem Search API
Diagnosis and Tuning
Isolate the source of quality issues, establish labeled baselines to measure recall and relevance, tune HNSW parameters, and choose the right embedding model. Diagnosis and Tuning
Search Strategies
Hybrid search, reranking, relevance feedback, and exploration APIs for improving result quality. Search Strategies
What ships with it
6 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.
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 · 33 lines · 122 tokens per session scan A e4b6baaf3de9
qdrant-search-quality is a skill published in the GitHub repository SecondLifes/code-intel (2 stars, last pushed 22d ago), licensed Apache-2.0. It adds 122 tokens to every session and 543 once invoked, about $0.0006 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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