AC rag-accuracy-optimizer
Optimize accuracy for RAG (Retrieval-Augmented Generation) systems. Covers: DB schema design, chunking strategies, retrieval optimization, accuracy testing, and anti-hallucination safeguards. Use when: (1) designing or improving a RAG pipeline, (2) choosing the right chunking strategy, (3) optimizing retrieval accuracy (hybrid search, reranking, multi-query), (4) evaluating chunk quality or testing accuracy, (5) setting up monitoring & safeguards for RAG production, (6) choosing SQL vs Vector DB, (7) designing metadata schemas for domain-specific data (insurance, finance, healthcare, e-commerce).
As a process C 57/100 · Has gaps — weak spots: when it triggers, inputs and preconditions, running it twice
How to improve
- The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
- 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 · 1
✓ No critical or high findings
Medium and low: 1
✓ Guard found no suspicious behaviour. 1 matches are attack strings quoted in this security skill's own documentation.
Files scanned: 14. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
body-longSKILL.md body ≈ 5288 tokens (recommended < 5000); move details to references/
Process rating: all ten parameters 57/100
- 0Inputs and preconditions. Does not say what the process needs to start
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 2 mutating operations with no state check
- 50Failures and branches. 0 branches, has a failure section
- 60Tools and files. Uses tools (python) that frontmatter does not declare
- 60Result and completion. Output format stated, no completion criterion
- 70Execution cost. Instruction body is 5288 tokens
- 100Steps. 53 steps
- 100Consistency. Name and required fields are in place
- 100Progress reporting. Reports progress
- low 14 top-level sections: this looks like several domains in one skill
- low The skill ranks results itself: that belongs to the system behind the tool, not the model
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)
- -213 emoji in the instructions: noise for the model
- +1No license
- +2Single-language instructions
- +3Description length 603: enough signal without eating the budget
- +4Structure: 67 headings
- +3Step-by-step instructions: 53 items
- +3Output format is stated explicitly
- +4Has examples (34 code blocks)
- +4Reference files are cited in the instructions (8 of 8)
- +3All 4 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 82.