AC onboarding-activator
Guides an AI assistant to redesign SaaS onboarding flows using the Activation Path Method — helping users define their aha moment, map time-to-value, eliminate friction, and produce a concrete minimum viable activation path. Use when a SaaS team wants to improve user activation, reduce time-to-value, or redesign their onboarding experience from first signup to first win.
As a process C 62/100 · Has gaps — weak spots: result and completion, when it triggers, 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 · 0
✓ No critical or high findings
Files scanned: 3. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
body-longSKILL.md body ≈ 5233 tokens (recommended < 5000); move details to references/
Process rating: all ten parameters 62/100
- 0Result and completion. Does not say what the result is
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 28 mutating operations with no state check
- 60Tools and files. Uses tools (web) that frontmatter does not declare
- 70Failures and branches. 9 branches
- 70Execution cost. Instruction body is 5233 tokens
- 100Steps. 105 steps
- 100Inputs and preconditions. Inputs and preconditions are listed
- 100Consistency. Name and required fields are in place
- 100Progress reporting. Reports progress
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
- low 10 top-level sections: this looks like several domains in one skill
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 373: enough signal without eating the budget
- +4Structure: 24 headings
- +3Step-by-step instructions: 105 items
- +4Has examples (1 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 74.