AD turboquant-memory
Compress and accelerate vector search in memory/RAG systems using TurboQuant (ICLR 2026) — near-optimal vector quantization with 5-8x compression and 98%+ search accuracy. Uses blockwise Hadamard rotation + Lloyd-Max scalar quantization. Use when: (1) optimizing embedding storage size, (2) speeding up semantic search, (3) user mentions "compress embeddings", "quantize vectors", "memory optimization", "faster search", "TurboQuant", "vector compression", or "embedding compression", (4) reducing memory footprint of RAG systems. Works with any embedding model (Gemini, OpenAI, Cohere, local) and any dimension ≥ 128. No GPU required. numpy only.
As a process D 46/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions
How to improve
- 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: 7. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
✓ No remarks against the Agent Skills spec
Process rating: all ten parameters 46/100
- 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
- 60Tools and files. Uses tools (python) that frontmatter does not declare
- 100Steps. 15 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 907 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
- +4Description does not say when NOT to use the skill (false activations)
- +3Output format is not stated: the model decides each time
- +1No license
- +2Single-language instructions
- +5Description quotes 7 example trigger phrases
- +3Description length 647: enough signal without eating the budget
- +4Structure: 12 headings
- +3Step-by-step instructions: 15 items
- +4Has examples (4 code blocks)
- +4Reference files are cited in the instructions (1 of 1)
- +3All 3 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 96.