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BF qdrant-performance-optimization

Different techniques to optimize the performance of Qdrant, including indexing strategies, query optimization, and hardware considerations. Use when you want to improve the speed and efficiency of your Qdrant deployment.

github/awesome-copilot Agent Skills author: github MIT 1 file body ≈ 380 tokens Open the sourcegithub.com↗ analyzed 14 h ago

Different techniques to optimize the performance of Qdrant, including indexing strategies, query optimization, and hardware considerations.

As a process F 20/100 · Will not run — References files that are not bundled: search-speed-optimization/SKILL.md, indexing-performance-optimization/SKILL.md, memory-usage-optimization/SKILL.md

ReferenceInfrastructureData and analyticsAI and agentstype and topics are labelled automatically from the skill text
Runs in: GitHub Copilot / VS Code Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
Technical rating
B
86/100
safety, quality, tests
Safety 60%
100
Quality 40%
65
Run on models
none yet
Process rating
F
20/100
Will not run
References files that are not bundled: search-speed-optimization/SKILL.md, indexing-performance-optimization/SKILL.md, memory-usage-optimization/SKILL.md
Tools and files w 18
0
Steps w 15
0
Result and completion w 14
0
the three weakest of ten parameters · all ten

The same skill appears in 1 more place: RA-Skills

How to improve

  1. The text references files that are not there: add them or drop the references.
For the model run — optional
  • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
  • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

Guard findings · 0

✓ No critical or high findings

Files scanned: 1. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning missing-ref reference to a missing file: search-speed-optimization/SKILL.md
  • warning missing-ref reference to a missing file: indexing-performance-optimization/SKILL.md
  • warning missing-ref reference to a missing file: memory-usage-optimization/SKILL.md

Process rating: all ten parameters 20/100

Will not run. References files that are not bundled: search-speed-optimization/SKILL.md, indexing-performance-optimization/SKILL.md, memory-usage-optimization/SKILL.md
  • 0Tools and files. 3 referenced file(s) missing: search-speed-optimization/SKILL.md, indexing-performance-optimization/SKILL.md, memory-usage-optimization/SKILL.md
  • 0Steps. Prose only: no discrete steps
  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Failures and branches. Linear process with no failure handling
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 380 tokens
  • 100Running it twice. No mutating operations

Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

Quality signals

  • +5Description has no quoted example phrases that should trigger the skill
  • +4Description does not say when NOT to use the skill (false activations)
  • +3No numbered steps or checklist
  • +3Output format is not stated: the model decides each time
  • +4No input/output examples
  • +1No license
  • +2Single-language instructions
  • +3Description length 220: enough signal without eating the budget
  • +4Structure: 4 headings

Quality base 70; lint remarks subtract, signals add up to 100. Result: 65.