SKILLEMALL.ai

BC CardBenefits

Tracks the principal's credit cards, their perks and annual credits, and points and miles balances across bank, airline and hotel programs, then turns them into which-card answers, unused-credit sweeps and points redemptions through transfer partners, all from the principal's own card data files. USE WHEN credit card benefits, card perks, which card should I use, annual credits, unused credits, expiring credits, my points, my miles, airline miles, hotel points, update my points balance, redeem points, transfer partners, plan a trip with points, refresh card perks. NOT FOR spending totals, expense tracking or bookkeeping, revenue or payments data, or moving money.

danielmiessler/Personal_AI_Infrastructure Agent Skills author: danielmiessler 7 files body ≈ 1 257 tokens Open the sourcegithub.com↗ analyzed 28 h ago

Tracks the principal's credit cards, their perks and annual credits, and points and miles balances across bank, airline and hotel programs, then turns them…

As a process C 54/100 · Has gaps — weak spots: result and completion, inputs and preconditions, failures and branches

ProcedureData and analyticsAI and agentsFinancetype and topics are labelled automatically from the skill text
JSON
Technical rating
B
93/100
safety, quality, tests
Safety 60%
100
Quality 40%
82
Run on models
none yet
Process rating
C
54/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
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: 7. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • warning name-format name should be kebab-case (lowercase letters, digits, hyphens)
    • note frontmatter-key unknown frontmatter key "evals"

    Process rating: all ten parameters 54/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 1 mutating operations with no state check
    • 50When it triggers. No condition that starts the skill
    • 100Tools and files. No external tools needed
    • 100Steps. 12 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1257 tokens
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing

    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
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +3Description length 671: enough signal without eating the budget
    • +4Structure: 6 headings
    • +3Step-by-step instructions: 12 items
    • +4Has examples (3 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 82.