AC pixeltable-skill
Build multimodal AI applications with Pixeltable — declarative tables replace LangChain + pandas + vector DB with one system. Automates chunking, embedding, retrieval, tool-calling agents, and 25+ AI provider integrations (OpenAI, Anthropic, Gemini, etc.) via computed columns that run on insert. Use when building RAG pipelines, processing images/video/audio/documents, orchestrating LLM inference, or deploying agents with persistent memory. Covers incremental computation, version control, similarity search, FastAPI serving, and production patterns. Do NOT use for general Python or direct PostgreSQL administration.
As a process C 53/100 · Has gaps — weak spots: result and completion, inputs and preconditions, consistency
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 · 0
✓ No critical or high findings
Files scanned: 10. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
body-longSKILL.md body ≈ 6240 tokens (recommended < 5000); move details to references/
Process rating: all ten parameters 53/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Progress reporting. Says nothing while it works
- 40Consistency. Frontmatter name (pixeltable-skill) differs from the folder (pixeltable)
- 50When it triggers. No condition that starts the skill
- 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
- 70Execution cost. Instruction body is 6240 tokens
- 100Steps. 25 steps
- 100Failures and branches. 3 branches, has a failure section
- 100Running it twice. Mutating operations check current state
- low 15 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
- +3Output format is not stated: the model decides each time
- +2Single-language instructions
- +4Description says when NOT to use the skill
- +3Description length 620: enough signal without eating the budget
- +4Structure: 25 headings
- +3Step-by-step instructions: 25 items
- +4Has examples (16 code blocks)
- +4Reference files are cited in the instructions (8 of 8)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 83.