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AB doordash-spend-guard

Hard spending policy for agent-driven DoorDash ordering through the DoorDash CLI (dd-cli). Per-order ceiling, daily/weekly/monthly caps, cooldown between orders, and blocked hours — enforced deterministically by routing every cart-mutation and checkout through the dd-guard wrapper script, which prices the cart, checks the policy against a persistent spend ledger, and refuses out-of-policy checkouts with exit code 2. Use when the user wants budget limits on agent food ordering, asks "how much have I spent on DoorDash", wants to set spending caps, or whenever building carts / checking out with dd-cli while this skill is installed. Pairs with a PreToolUse hook that denies raw dd-cli checkout calls that bypass the wrapper.

davila7/claude-code-templates Agent Skills author: davila7 MIT 2 files · 1 script body ≈ 1 039 tokens Open the sourcegithub.com↗ analyzed 6 d ago

Hard spending policy for agent-driven DoorDash ordering through the DoorDash CLI (dd-cli).

As a process B 69/100 · Nearly there — weak spots: result and completion, inputs and preconditions, running it twice

IntegrationAI and agentstype and topics are labelled automatically from the skill text
Runs in: Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
Technical rating
A
95/100
safety, quality, tests
Safety 60%
100
Quality 40%
87
Run on models
none yet
Process rating
B
69/100
Nearly there
Result and completion w 14
0
Inputs and preconditions w 11
0
Running it twice w 4
30
the three weakest of ten parameters · all ten

How to improve

    For the model run — optional
    • 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

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 69/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 30Running it twice. 3 mutating operations with no state check
    • 70When it triggers. States when to use, but not when not to
    • 100Tools and files. Tools declared in frontmatter
    • 100Steps. 12 steps
    • 100Failures and branches. 1 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1039 tokens
    • 100Progress reporting. Reports progress

    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 728: enough signal without eating the budget
    • +4Structure: 6 headings
    • +3Step-by-step instructions: 12 items
    • +4Has examples (2 code blocks)
    • +3All 1 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 87.