BB snaplii-cli
Agent-to-Merchant (A2M) payments — AI agents complete transactions without checkout, using Snaplii's pre-funded gift cards as a payment rail across 500+ brands. Use when the user wants to browse, buy, or manage gift cards, check their Snaplii Cash balance, pay bills (utilities, telecom), or send money to another person's phone number (P2P transfer).
Agent-to-Merchant (A2M) payments — AI agents complete transactions without checkout, using Snaplii's pre-funded gift cards as a payment rail across 500+ brands.
As a process B 65/100 · Nearly there — weak spots: result and completion, consistency, execution cost
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: 2. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
body-longSKILL.md body ≈ 8675 tokens (recommended < 5000); move details to references/ - note
edit-residuethe text marks something as outdated (lines 136, 158, 449): check that old rules are not kept next to new ones — the full check reads the text for contradictions
Process rating: all ten parameters 65/100
- 0Result and completion. Does not say what the result is
- 40Consistency. Frontmatter name (snaplii-cli) differs from the folder (snaplii-a2m-payment)
- 40Execution cost. Instruction body is 8675 tokens: crowds the task out of the window
- 60Tools and files. Uses tools (bash, web, python) that frontmatter does not declare
- 70When it triggers. States when to use, but not when not to
- 85Steps. 83 steps, 1 vague phrases
- 100Inputs and preconditions. Inputs and preconditions are listed
- 100Failures and branches. 10 branches, has a failure section
- 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
- +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 351: enough signal without eating the budget
- +4Structure: 26 headings
- +3Step-by-step instructions: 83 items
- +4Has examples (11 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 74.