AC doordash-group-orders
Group food ordering through the DoorDash CLI (dd-cli) from a persistent team roster. One request ("lunch for the team") fans out into a single merged cart with every line attributed to its eater via a person-to-cart-item-id ledger, per-person cost split with fee proration, payer rotation history, and a checkout gate that re-derives allergen conflicts against the roster live. Use when ordering for multiple people — team lunch, incident-response food, "collect orders from the thread" — or for /whose-turn payer rotation questions. Handles paste-a-thread intake: paste a Slack/chat thread and it builds the order ledger from it.
Group food ordering through the DoorDash CLI (dd-cli) from a persistent team roster.
As a process C 56/100 · 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: 1. 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 56/100
- 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
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 5 mutating operations with no state check
- 50Failures and branches. 0 branches, has a failure section
- 100Tools and files. Tools declared in frontmatter
- 100Steps. 17 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1125 tokens
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
- +1No license
- +2Single-language instructions
- +5Description quotes 2 example trigger phrases
- +3Description length 630: enough signal without eating the budget
- +4Structure: 8 headings
- +3Step-by-step instructions: 17 items
- +4Has examples (2 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 89.