BC fintech-support-agent
AI-powered customer support agent for fintech and remittance products. Handles transfer status lookups, refund requests, account suspensions, KYC document guidance, and complaint escalation across any messaging channel. Resolves tier-1 tickets autonomously and hands off complex cases to human agents with full context already written up.
As a process C 57/100 · Has gaps — weak spots: result and completion, inputs and preconditions, consistency
What is at stake
Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.
Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.
The instructions or scripts send data out: environment variables, keys, file contents, chat history. You may never notice, because the agent performs the upload as "part of the task".
If the upload is not needed for the task, remove it: catalogs flag such skills and delist them. If it is needed, name the destination explicitly, say what leaves the machine, and give the user a switch.
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- 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 · 2
✓ No critical or high findings
Medium and low: 2
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medium Exfiltration
net-redirectable-api-keyhandlers.py:30Helper sends the API key to a host configured by an environment variable — the key can be redirected to another serverAPI key + configurable base URL from environment
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low Exfiltration
exfil-webhook-urlREADME.md:55Webhook / callback URL commonly used for exfiltration (verify the destination) (placeholder value)"ESCALATION_WEBHOOK_URL": "https://hooks.slack.com/your-webhook"
placeholder
Files scanned: 5. 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")
Process rating: all ten parameters 57/100
- 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. 7 mutating operations with no state check
- 40Consistency. Frontmatter name (fintech-support-agent) differs from the folder (fintech-customer-support)
- 60Tools and files. Uses tools (bash, web, python) that frontmatter does not declare
- 70Failures and branches. 10 branches
- 100Steps. 52 steps
- 100When it triggers. States when to use and when not to
- 100Execution cost. Instruction body is 1498 tokens
- 100Progress reporting. Reports progress
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
- low The response is described with custom markup (4 tags): a typed call is more reliable
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
- +4No input/output examples
- +1No license
- +2Single-language instructions
- +3Description length 338: enough signal without eating the budget
- +4Structure: 11 headings
- +3Step-by-step instructions: 52 items
Quality base 70; lint remarks subtract, signals add up to 100. Result: 68.