webflow-data-handling
Implement Webflow data handling — CMS content delivery patterns, PII redaction in form submissions, GDPR/CCPA compliance for ecommerce data, and data retention policies. Trigger with phrases like "webflow data", "webflow PII", "webflow GDPR", "webflow data retention", "webflow privacy", "webflow CCPA", "webflow forms data".
What this skill does
# Webflow Data Handling
## Overview
Handle sensitive data correctly when working with the Webflow Data API v2. Covers
PII in form submissions, ecommerce customer data, CMS content classification,
GDPR/CCPA compliance patterns, and data retention policies.
## Prerequisites
- Understanding of GDPR/CCPA requirements
- Webflow API token with `forms:read`, `ecommerce:read` scopes
- Database for audit logging
- Scheduled job infrastructure for data cleanup
## Webflow Data Classification
| Source | Data Type | PII Risk | Handling |
|--------|-----------|----------|----------|
| Form submissions | Email, name, phone, message | High | Encrypt at rest, redact in logs |
| Ecommerce orders | Name, email, address, payment | High | Never log, minimal retention |
| CMS items | Blog posts, team bios, products | Low-Medium | May contain names/photos |
| Site analytics | Page views, sessions | Low | Aggregate when possible |
| API tokens | Access credentials | Critical | Never log, rotate quarterly |
## Instructions
### Step 1: PII Detection in Form Submissions
```typescript
const PII_PATTERNS = [
{ type: "email", regex: /[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}/g },
{ type: "phone", regex: /\b\d{3}[-.]?\d{3}[-.]?\d{4}\b/g },
{ type: "ssn", regex: /\b\d{3}-\d{2}-\d{4}\b/g },
{ type: "credit_card", regex: /\b\d{4}[- ]?\d{4}[- ]?\d{4}[- ]?\d{4}\b/g },
];
function detectPII(text: string): Array<{ type: string; found: boolean }> {
return PII_PATTERNS.map(p => ({
type: p.type,
found: p.regex.test(text),
})).filter(r => r.found);
}
// Scan form submissions for PII before logging
async function processFormSubmission(formId: string) {
const { formSubmissions } = await webflow.forms.listSubmissions(formId);
for (const sub of formSubmissions || []) {
const rawData = JSON.stringify(sub.formData);
const piiFindings = detectPII(rawData);
if (piiFindings.length > 0) {
console.warn(`PII detected in submission ${sub.id}: ${piiFindings.map(f => f.type).join(", ")}`);
// Log redacted version only
console.log("Form data:", redactPII(sub.formData || {}));
}
}
}
```
### Step 2: PII Redaction
```typescript
function redactPII(data: Record<string, any>): Record<string, any> {
const sensitiveFields = new Set([
"email", "phone", "telephone", "mobile", "ssn",
"password", "credit-card", "card-number", "address",
"full-name", "first-name", "last-name",
]);
const redacted: Record<string, any> = {};
for (const [key, value] of Object.entries(data)) {
const normalizedKey = key.toLowerCase().replace(/[\s_]/g, "-");
if (sensitiveFields.has(normalizedKey)) {
redacted[key] = "[REDACTED]";
} else if (typeof value === "string") {
// Redact inline PII patterns
let cleaned = value;
for (const pattern of PII_PATTERNS) {
cleaned = cleaned.replace(pattern.regex, `[${pattern.type.toUpperCase()}_REDACTED]`);
}
redacted[key] = cleaned;
} else {
redacted[key] = value;
}
}
return redacted;
}
// Usage in logging
async function logFormData(formData: Record<string, any>) {
console.log("Form submission (redacted):", redactPII(formData));
}
```
### Step 3: Ecommerce Data Handling
```typescript
// Order data contains high-sensitivity PII
async function processOrder(siteId: string, orderId: string) {
const order = await webflow.orders.get(siteId, orderId);
// NEVER log full customer info
const safeOrderLog = {
orderId: order.orderId,
status: order.status,
itemCount: order.purchasedItems?.length,
totalCents: order.customerPaid?.value,
// Redact customer info
customer: {
hasEmail: !!order.customerInfo?.email,
hasAddress: !!order.shippingAddress,
// Never: order.customerInfo?.email
// Never: order.shippingAddress?.addressLine1
},
createdAt: order.acceptedOn,
};
console.log("Order processed:", safeOrderLog);
}
```
### Step 4: GDPR — Data Subject Access Request (DSAR)
```typescript
interface DataExport {
source: string;
exportedAt: string;
requestedBy: string;
data: {
formSubmissions: Array<{ formName: string; submittedAt: string; data: Record<string, any> }>;
orders: Array<{ orderId: string; status: string; total: number; items: string[] }>;
};
}
async function exportUserData(siteId: string, userEmail: string): Promise<DataExport> {
const exportData: DataExport = {
source: "Webflow",
exportedAt: new Date().toISOString(),
requestedBy: userEmail,
data: { formSubmissions: [], orders: [] },
};
// 1. Find form submissions by email
const { forms } = await webflow.forms.list(siteId);
for (const form of forms || []) {
const { formSubmissions } = await webflow.forms.listSubmissions(form.id!);
for (const sub of formSubmissions || []) {
const formData = sub.formData || {};
// Check all fields for matching email
const hasEmail = Object.values(formData).some(
v => typeof v === "string" && v.toLowerCase() === userEmail.toLowerCase()
);
if (hasEmail) {
exportData.data.formSubmissions.push({
formName: form.displayName!,
submittedAt: sub.submittedAt!,
data: formData,
});
}
}
}
// 2. Find orders by email
const { orders } = await webflow.orders.list(siteId);
for (const order of orders || []) {
if (order.customerInfo?.email?.toLowerCase() === userEmail.toLowerCase()) {
exportData.data.orders.push({
orderId: order.orderId!,
status: order.status!,
total: (order.customerPaid?.value || 0) / 100,
items: order.purchasedItems?.map(i => i.productName || "Unknown") || [],
});
}
}
return exportData;
}
```
### Step 5: GDPR — Right to Deletion
```typescript
async function deleteUserData(
siteId: string,
userEmail: string
): Promise<{ deleted: string[]; retained: string[] }> {
const result = { deleted: [] as string[], retained: [] as string[] };
// Note: Webflow API does not currently support deleting form submissions
// via API. You must delete them through the Webflow dashboard.
// However, you can delete your local copies:
// 1. Delete local form submission copies
await db.formSubmissions.deleteMany({ email: userEmail, source: "webflow" });
result.deleted.push("Local form submission copies");
// 2. Delete local order copies (keep anonymized for accounting)
await db.orders.updateMany(
{ email: userEmail, source: "webflow" },
{ $set: { email: "[DELETED]", name: "[DELETED]", address: "[DELETED]" } }
);
result.retained.push("Anonymized order records (legal requirement)");
// 3. Audit log (required — never delete audit logs)
await db.auditLog.insertOne({
action: "GDPR_DELETION",
email: userEmail,
service: "webflow",
timestamp: new Date(),
deletedSources: result.deleted,
retainedSources: result.retained,
});
result.retained.push("Audit log entry");
return result;
}
```
### Step 6: Data Retention Policy
| Data Type | Retention | Reason | Auto-Cleanup |
|-----------|-----------|--------|--------------|
| Form submissions | 90 days | Business need | Yes |
| Order records | 7 years | Tax/accounting | No |
| API call logs | 30 days | Debugging | Yes |
| Error logs | 90 days | Root cause analysis | Yes |
| Audit logs | 7 years | Compliance | No |
| Cached CMS content | 24 hours | Performance | Yes (TTL) |
```typescript
async function cleanupExpiredData() {
const now = new Date();
// Delete form submissions older than 90 days
const formCutoff = new Date(now);
formCutoff.setDate(formCutoff.getDate() - 90);
await db.formSubmissions.deleteMany({
source: "webflow",
createdAt: { $lt: formCutoff },
type: { $nin: ["audit", "compliance"] },
});
// Delete API logs older than 30 days
const logCutoff = new Date(now);
logCutoff.setDate(logCutoff.getDate() - 30);
await db.apiLogs.deleteMany({
service: "webfRelated in Writing & Docs
jax-development
IncludedUse this skill when the user is writing, debugging, profiling, refactoring, reviewing, benchmarking, parallelising, exporting, or explaining JAX code, or when they mention JAX, jax.numpy, jit, grad, value_and_grad, vmap, scan, lax, random keys, pytrees, jax.Array, sharding, Mesh, PartitionSpec, NamedSharding, pmap, shard_map, Pallas, XLA, StableHLO, checkify, profiler, or the JAX repo. It helps turn NumPy or PyTorch-style code into pure functional JAX, fix tracer/control-flow/shape/PRNG bugs, remove recompiles and host-device syncs, choose transforms and sharding strategies, inspect jaxpr/lowering/IR, and benchmark compiled code correctly.
nature-article-writer
IncludedDrafts, rewrites, diagnostically critiques, and style-calibrates primary research manuscripts for Nature and Nature Portfolio journals. Use when the user wants a Nature-style title, summary paragraph or abstract, introduction, results, discussion, methods, figure legends, presubmission enquiry, cover letter, reviewer response, or when a scientific draft sounds generic, jargon-heavy, structurally weak, or AI-ish and needs precise, broad-reader-friendly prose without inventing data, analyses, or references. Best for primary research articles and letters rather than reviews or press releases unless explicitly adapting one.
deckrd
IncludedDocument-driven framework that derives requirements, specifications, implementation plans, and executable tasks from goals through structured AI dialogue. Use when user says "write requirements", "create spec", "plan implementation", "derive tasks", "structure this feature", "break down into tasks", or "document this module". Also use for reverse engineering existing code into docs (/deckrd rev). Do NOT use for direct code writing — use /deckrd-coder after tasks are generated. Do NOT use when the user only wants to run or fix existing code without planning.
clinical-decision-support
IncludedGenerate professional clinical decision support (CDS) documents for pharmaceutical and clinical research settings, including patient cohort analyses (biomarker-stratified with outcomes) and treatment recommendation reports (evidence-based guidelines with decision algorithms). Supports GRADE evidence grading, statistical analysis (hazard ratios, survival curves, waterfall plots), biomarker integration, and regulatory compliance. Outputs publication-ready LaTeX/PDF format optimized for drug development, clinical research, and evidence synthesis.
handling-sf-data
IncludedSalesforce data operations with 130-point scoring. Use this skill to create, update, delete, bulk import/export, generate test data, and clean up org records using sf CLI and anonymous Apex. TRIGGER when: user creates test data, performs bulk import/export, uses sf data CLI commands, needs data factory patterns for Apex tests, or needs to seed/clean records in a Salesforce org. DO NOT TRIGGER when: SOQL query writing only (use querying-soql), Apex test execution (use running-apex-tests), or metadata deployment (use deploying-metadata).
accelint-ac-to-playwright
IncludedConvert and validate acceptance criteria for Playwright test automation. Use when user asks to (1) review/evaluate/check if AC are ready for automation, (2) assess if AC can be converted as-is, (3) validate AC quality for Playwright, (4) turn AC into tests, (5) generate tests from acceptance criteria, (6) convert .md bullets or .feature Gherkin files to Playwright specs, (7) create test automation from requirements. Handles both bullet-style markdown and Gherkin syntax with JSON test plan generation and validation.