BC rag-implementation
RAG (Retrieval-Augmented Generation) implementation workflow covering embedding selection, vector database setup, chunking strategies, and retrieval optimization.
The skill outlines a standard RAG pipeline: embeddings selection, vector database setup, chunking, retrieval tuning. Sounds complete in theory, but the entire content is 1076 tokens—roughly one page. Security checks passed at 100, quality scored 84, but process score dropped to 64. No critical errors, but concrete code examples appear absent. Works as a terminology refresher; for production use you'll need specifics: which libraries, chunking parameters, quality metrics.
Install if you want a RAG concept overview. If you need working code, first verify whether the files contain examples or just theory.
RAG (Retrieval-Augmented Generation) implementation workflow covering embedding selection, vector database setup, chunking strategies, and retrieval…
As a process C 64/100 · Has gaps — weak spots: result and completion, when it triggers, failures and branches
The same skill appears in 1 more place: agentic-awesome-skills
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: 1. 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 64/100
- 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
- 30Running it twice. 2 mutating operations with no state check
- 40Result and completion. Does not say what the result is
- 70Inputs and preconditions. Inputs and preconditions are listed
- 100Tools and files. No external tools needed
- 100Steps. 73 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1076 tokens
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)
- +3Output format is not stated: the model decides each time
- +1No license
- +2Single-language instructions
- +3Description length 162: enough signal without eating the budget
- +4Structure: 40 headings
- +3Step-by-step instructions: 73 items
- +4Has examples (10 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.