AC unformal-api
Create conversational Pulses that replace forms, surveys, intake emails, feedback requests, NPS checks, user interviews, lead qualification, application forms, client onboarding questionnaires, and customer research. Send someone a link — an AI agent has a real conversation with them and returns structured data, transcripts, and aggregate insights. Use whenever the user wants to collect information from people (customers, leads, applicants, employees, respondents, event attendees, workshop participants, beta testers), run a survey, send out a feedback form, interview users at scale, qualify leads, onboard new clients, extract structured answers from free-text responses, or analyze sentiment across many respondents.
As a process C 62/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice
What is at stake
Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.
Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.
The skill asks for more than the task needs: broad tool access, credential environment variables, binaries. Every extra permission widens the damage from a mistake or a compromise.
Narrow allowed-tools and the variable list to the minimum; replace binaries with readable sources or scripts.
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 · 1
✓ No critical or high findings
Medium and low: 1
-
medium Broad scope
meta-broad-allowed-toolsSKILL.md:1Broad tool permissions pre-approved: Bashallowed-tools: Bash
Files scanned: 2. 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 62/100
- 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
- 30Running it twice. 24 mutating operations with no state check
- 70When it triggers. States when to use, but not when not to
- 70Failures and branches. 7 branches
- 70Execution cost. Instruction body is 4820 tokens
- 100Tools and files. Tools declared in frontmatter
- 100Steps. 33 steps
- 100Consistency. Name and required fields are in place
- low 18 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
- +2Single-language instructions
- +3Description length 724: enough signal without eating the budget
- +4Structure: 32 headings
- +3Step-by-step instructions: 33 items
- +4Has examples (24 code blocks)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 85.