BB opensearch-vector-search
Amazon OpenSearch vector search expert knowledge base. Comprehensive guidance on vector search configuration, cluster tuning, quantization, cost optimization, instance sizing, and pricing estimation. **Use this Skill when**: (1) User asks about OpenSearch vector search (k-NN) configuration, HNSW parameter tuning, disk mode (2) User needs vector search cluster sizing, capacity planning, instance recommendations (3) User asks about quantization techniques (Binary/Byte/FP16/Product Quantization) (4) User needs to estimate OpenSearch vector search costs or query pricing (5) User asks about OpenSearch indexing strategies, shard planning, query optimization (6) User mentions "vector database", "vector search", "k-NN", "knn_vector", "embedding search", "HNSW" (7) User mentions data scale (e.g. "100M vectors", "1 billion vectors") and needs cluster configuration advice (8) User asks about OpenSearch cluster JVM, memory, or thread pool configuration (9) Involves Amazon OpenSearch Service pricing, cost calculation, or instance comparison (10) User provides an OpenSearch cluster URL/credentials and wants vector configuration analysis or health check (11) For executing reproducible VectorDBBench performance tests, route to the bundled opensearch-vector-benchmark skill
As a process B 70/100 · Nearly there — weak spots: running it twice, progress reporting
How to improve
- Shorten the description to 1024 characters.
- 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: 15. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- error
description-longdescription is 1278 chars, limit 1024 - note
frontmatter-keyunknown frontmatter key "repository" - note
frontmatter-keyunknown frontmatter key "requirements"
Process rating: all ten parameters 70/100
- 0Progress reporting. Says nothing while it works
- 30Running it twice. 6 mutating operations with no state check
- 60Tools and files. Uses tools (node) that frontmatter does not declare
- 60Result and completion. Output format stated, no completion criterion
- 65Failures and branches. 3 branches
- 70When it triggers. States when to use, but not when not to
- 70Inputs and preconditions. Inputs and preconditions are listed
- 85Steps. 55 steps, 1 vague phrases
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2907 tokens
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
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
- +4Description does not say when NOT to use the skill (false activations)
- +3Description length 1277: 120–800 characters recommended
- +2Single-language instructions
- +5Description quotes 6 example trigger phrases
- +4Structure: 12 headings
- +3Step-by-step instructions: 55 items
- +3Output format is stated explicitly
- +4Has examples (11 code blocks)
- +4Reference files are cited in the instructions (9 of 9)
- +3All 2 scripts are documented
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 70.