AC quizme
Adaptive tech quiz skill for OpenClaw. Asks one question at a time on any coding or CS topic: Python, JavaScript, SQL, system design, algorithms, networking, Git, Docker, APIs, and more. Includes code snippets where relevant. Difficulty adapts dynamically — harder after correct answers, easier after wrong ones. Uses a rolling question bank generated in batches (via GPT-4o-mini) so sessions are fast and token-efficient. Automatically graduates you to the next difficulty level when you hit 80% accuracy. Tracks your topics and progress across sessions. Trigger: /quizme, "quiz me on X", "teach me X", "test my knowledge of X", "I want to learn X", "start a quiz".
As a process C 53/100 · Has gaps — 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: 5. 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 53/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 20When it triggers. No condition that starts the skill
- 60Tools and files. Uses tools (python) that frontmatter does not declare
- 70Failures and branches. 4 branches
- 85Steps. 34 steps, 1 vague phrases
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1929 tokens
- 100Running it twice. Mutating operations check current state
- 100Progress reporting. Reports progress
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
- high The skill tells the model to perform an irreversible action with no human approval
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
- -31 of 1 scripts are never mentioned in SKILL.md
- +1No license
- +2Single-language instructions
- +5Description quotes 5 example trigger phrases
- +3Description length 666: enough signal without eating the budget
- +4Structure: 19 headings
- +3Step-by-step instructions: 34 items
- +4Has examples (7 code blocks)
- +4Reference files are cited in the instructions (1 of 1)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 90.