BD greenhelix-agent-loyalty-incentives
Agent Loyalty & Incentives Engineering. Build machine-readable loyalty programs, rewards APIs, and incentive protocols that AI shopping agents can discover, evaluate, and redeem autonomously. Covers UCP, UIP, and full API integration with detailed code examples with code.
As a process D 48/100 · Unfinished process — weak spots: result and completion, inputs and preconditions, execution cost
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- 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
description-no-whendescription does not say WHEN to use the skill (no "use when") - warning
body-longSKILL.md body ≈ 22971 tokens (recommended < 5000); move details to references/ - note
frontmatter-keyunknown frontmatter key "type" - note
frontmatter-keyunknown frontmatter key "price_usd" - note
frontmatter-keyunknown frontmatter key "content_type" - note
frontmatter-keyunknown frontmatter key "executable" - note
frontmatter-keyunknown frontmatter key "install" - note
frontmatter-keyunknown frontmatter key "credentials"
Process rating: all ten parameters 48/100
- 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
- 10Execution cost. Instruction body is 22971 tokens: crowds the task out of the window
- 30Running it twice. 24 mutating operations with no state check
- 50When it triggers. No condition that starts the skill
- 60Tools and files. Uses tools (web, python) that frontmatter does not declare
- 65Failures and branches. 3 branches
- 100Steps. 98 steps
- 100Consistency. Name and required fields are in place
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
- low 11 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 272: enough signal without eating the budget
- +4Structure: 59 headings
- +3Step-by-step instructions: 98 items
- +4Has examples (32 code blocks)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 60.