Intercom Data Handling
Overview
Handle sensitive contact data in Intercom integrations with GDPR/CCPA compliance:
data export via the Data Export API, contact deletion with an audit trail, PII
redaction in logs, and data retention policies. This skill gives you a lean map of
the five workflows here; the full copy-ready TypeScript lives in
references/implementation.md and worked usage in
references/examples.md.
Prerequisites
- Understanding of GDPR/CCPA requirements
intercom-client SDK installed
- Database for audit logging
- Familiarity with Intercom's contact and conversation data model
Authentication
Every call authenticates with an Intercom access token via a Bearer header. Store
it as INTERCOM_ACCESS_TOKEN in the environment — never hardcode it and never log
it:
import { IntercomClient } from "intercom-client";
const client = new IntercomClient({
token: process.env.INTERCOM_ACCESS_TOKEN!,
});
// Raw REST calls use: Authorization: `Bearer ${process.env.INTERCOM_ACCESS_TOKEN}`
Grant the token the minimum scopes needed (read contacts/conversations for export,
write/delete for erasure). Rotate it if it ever appears in a log or a diff.
Data Classification for Intercom
| Category |
Intercom Fields |
Handling |
| PII |
email, name, phone, location |
Encrypt at rest, redact in logs |
| Identifiers |
id, external_id, user_id |
Use for lookups, no display |
| Conversation content |
body, conversation_parts |
May contain PII, scan before logging |
| Custom attributes |
User-defined |
Depends on content |
| System metadata |
created_at, updated_at, role |
Standard handling |
Instructions
The five workflows below compose into a compliant Intercom data lifecycle. Follow
the summary here, then open references/implementation.md
for the complete function bodies.
- DSAR export —
exportContactData(contactId) gathers the contact profile,
all conversations (with parts), tags, segments, and data events into one bundle.
This is the "give me all my data" request.
- Right to deletion (Article 17) —
deleteContactData(contactId) exports for
the audit trail first, then deletes from Intercom and every local cache, and
records a PII-free audit entry (email is hashed, not stored).
- Bulk data export —
bulkExportMessages(start, end) kicks off the async
/export/messages/data job; checkExportStatus(jobId) polls until a CSV
download_url is returned.
- PII redaction in logs —
redactIntercomData(data) masks a fixed
PII_FIELDS set (including nested custom_attributes.*) before anything is
logged.
- Retention enforcement —
enforceRetention() sweeps cached records past
their RETENTION window on a daily cron, and never touches the 7-year audit log.
Data minimization underpins all five: sync only the fields you need so the erasure
and breach surface stays small (see references/examples.md).
Here is the entry-point skeleton — the export that DSAR and deletion both build on:
const contact = await client.contacts.find({ contactId });
const convList = await client.conversations.search({
query: { field: "contact_ids", operator: "=", value: contactId },
});
// ...gather tags, segments, events → return one bundle
Output
Each workflow returns a structured, PII-aware result:
- DSAR export → an object with
contact, conversations[], tags[],
segments[], and events[] — the full data bundle to hand to the requester.
- Deletion →
{ deleted: true, auditRecord } where auditRecord holds the
action, hashed email, timestamp, purged data sources, and conversation count —
proof of erasure that contains no raw PII.
- Bulk export → a
job_identifier, then a { status, downloadUrl } once the
CSV is ready.
- Redaction → the same object shape with PII fields replaced by
[REDACTED].
- Retention →
{ deleted: { [cacheType]: count } } per swept cache type.
Error Handling
| Issue |
Cause |
Solution |
| Export job stuck in "pending" |
Large dataset |
Poll every 30s, timeout at 1h |
| Deletion returns 404 |
Already deleted |
Log and continue (idempotent) |
| PII in conversation bodies |
User-submitted content |
Scan with regex, redact in logs |
| Audit log gap |
Failed write |
Use write-ahead log or queue |
Examples
Full worked examples — fulfilling a DSAR, honoring a deletion request, polling a
bulk export to completion, and redacting before logging — are in
references/examples.md. The shortest one:
// A user asks for all their data — export the whole bundle to JSON.
const bundle = await exportContactData("5f3c9b2e8a1d4e0012ab34cd");
await fs.writeFile(`dsar/${bundle.contact.id}.json`, JSON.stringify(bundle, null, 2));
Resources
Next Steps
For enterprise access control and permission scoping on top of these data
workflows, see the intercom-enterprise-rbac skill in this pack.
Source: jeremylongshore/claude-code-plugins-plus-skills → plugins/saas-packs/intercom-pack/skills/intercom-data-handling/SKILL.md
1---2name: intercom-data-handling3description: "'Implement Intercom data handling for GDPR, contact export, data retention, and PII. Use when handling sensitive Intercom contact data, fulfilling a data subject access or deletion request, redacting PII in logs, or setting retention policy for cached Intercom records. Trigger with phrases like \"intercom data\", \"intercom PII\", \"intercom GDPR\", \"intercom data retention\", \"intercom privacy\", \"intercom CCPA\", \"intercom data export\", \"intercom delete contact\". '"4allowed-tools: "Read, Write, Edit"5category: backend-and-data6source_repo: jeremylongshore/claude-code-plugins-plus-skills7source_path: "plugins/saas-packs/intercom-pack/skills/intercom-data-handling/SKILL.md"8source_url: https://github.com/jeremylongshore/claude-code-plugins-plus-skills/blob/HEAD/plugins/saas-packs/intercom-pack/skills/intercom-data-handling/SKILL.md9---1011# Intercom Data Handling1213## Overview1415Handle sensitive contact data in Intercom integrations with GDPR/CCPA compliance:16data export via the Data Export API, contact deletion with an audit trail, PII17redaction in logs, and data retention policies. This skill gives you a lean map of18the five workflows here; the full copy-ready TypeScript lives in19[references/implementation.md](references/implementation.md) and worked usage in20[references/examples.md](references/examples.md).2122## Prerequisites2324- Understanding of GDPR/CCPA requirements25- `intercom-client` SDK installed26- Database for audit logging27- Familiarity with Intercom's contact and conversation data model2829## Authentication3031Every call authenticates with an Intercom access token via a Bearer header. Store32it as `INTERCOM_ACCESS_TOKEN` in the environment — never hardcode it and never log33it:3435```typescript36import { IntercomClient } from "intercom-client";3738const client = new IntercomClient({39 token: process.env.INTERCOM_ACCESS_TOKEN!,40});41// Raw REST calls use: Authorization: `Bearer ${process.env.INTERCOM_ACCESS_TOKEN}`42```4344Grant the token the minimum scopes needed (read contacts/conversations for export,45write/delete for erasure). Rotate it if it ever appears in a log or a diff.4647## Data Classification for Intercom4849| Category | Intercom Fields | Handling |50|----------|----------------|----------|51| PII | `email`, `name`, `phone`, `location` | Encrypt at rest, redact in logs |52| Identifiers | `id`, `external_id`, `user_id` | Use for lookups, no display |53| Conversation content | `body`, `conversation_parts` | May contain PII, scan before logging |54| Custom attributes | User-defined | Depends on content |55| System metadata | `created_at`, `updated_at`, `role` | Standard handling |5657## Instructions5859The five workflows below compose into a compliant Intercom data lifecycle. Follow60the summary here, then open [references/implementation.md](references/implementation.md)61for the complete function bodies.62631. **DSAR export** — `exportContactData(contactId)` gathers the contact profile,64 all conversations (with parts), tags, segments, and data events into one bundle.65 This is the "give me all my data" request.662. **Right to deletion (Article 17)** — `deleteContactData(contactId)` exports for67 the audit trail *first*, then deletes from Intercom and every local cache, and68 records a PII-free audit entry (email is hashed, not stored).693. **Bulk data export** — `bulkExportMessages(start, end)` kicks off the async70 `/export/messages/data` job; `checkExportStatus(jobId)` polls until a CSV71 `download_url` is returned.724. **PII redaction in logs** — `redactIntercomData(data)` masks a fixed73 `PII_FIELDS` set (including nested `custom_attributes.*`) before anything is74 logged.755. **Retention enforcement** — `enforceRetention()` sweeps cached records past76 their `RETENTION` window on a daily cron, and never touches the 7-year audit log.7778Data minimization underpins all five: sync only the fields you need so the erasure79and breach surface stays small (see [references/examples.md](references/examples.md)).8081Here is the entry-point skeleton — the export that DSAR and deletion both build on:8283```typescript84const contact = await client.contacts.find({ contactId });85const convList = await client.conversations.search({86 query: { field: "contact_ids", operator: "=", value: contactId },87});88// ...gather tags, segments, events → return one bundle89```9091## Output9293Each workflow returns a structured, PII-aware result:9495- **DSAR export** → an object with `contact`, `conversations[]`, `tags[]`,96 `segments[]`, and `events[]` — the full data bundle to hand to the requester.97- **Deletion** → `{ deleted: true, auditRecord }` where `auditRecord` holds the98 action, hashed email, timestamp, purged data sources, and conversation count —99 proof of erasure that contains no raw PII.100- **Bulk export** → a `job_identifier`, then a `{ status, downloadUrl }` once the101 CSV is ready.102- **Redaction** → the same object shape with PII fields replaced by `[REDACTED]`.103- **Retention** → `{ deleted: { [cacheType]: count } }` per swept cache type.104105## Error Handling106107| Issue | Cause | Solution |108|-------|-------|----------|109| Export job stuck in "pending" | Large dataset | Poll every 30s, timeout at 1h |110| Deletion returns 404 | Already deleted | Log and continue (idempotent) |111| PII in conversation bodies | User-submitted content | Scan with regex, redact in logs |112| Audit log gap | Failed write | Use write-ahead log or queue |113114## Examples115116Full worked examples — fulfilling a DSAR, honoring a deletion request, polling a117bulk export to completion, and redacting before logging — are in118[references/examples.md](references/examples.md). The shortest one:119120```typescript121// A user asks for all their data — export the whole bundle to JSON.122const bundle = await exportContactData("5f3c9b2e8a1d4e0012ab34cd");123await fs.writeFile(`dsar/${bundle.contact.id}.json`, JSON.stringify(bundle, null, 2));124```125126## Resources127128- [Full implementation walkthrough](references/implementation.md) — all five workflows, copy-ready129- [Worked examples](references/examples.md) — end-to-end usage + data minimization130- [Data Export API](https://developers.intercom.com/docs/references/rest-api/api.intercom.io/data-export/data_export)131- [Contacts API](https://developers.intercom.com/docs/references/rest-api/api.intercom.io/contacts)132- [Intercom Privacy](https://www.intercom.com/privacy)133134## Next Steps135136For enterprise access control and permission scoping on top of these data137workflows, see the `intercom-enterprise-rbac` skill in this pack.138139---140141**Source:** [`jeremylongshore/claude-code-plugins-plus-skills`](https://github.com/jeremylongshore/claude-code-plugins-plus-skills) → `plugins/saas-packs/intercom-pack/skills/intercom-data-handling/SKILL.md`