diagnostics
Skill: Diagnostics
Verify setup and troubleshoot the Personize stack. Two modes:
- VERIFY — Proactive. Run after setting up memory, governance, pipelines, or workspaces to confirm they work.
- FIX — Reactive. Jump to the action that matches the symptom when something breaks.
When to Use This Skill
Verify mode:
- Just finished memorizing data and want to confirm it's stored and recallable
- Just set up guidelines and want to confirm agents can see them
- Just wired a pipeline and want to run one record end-to-end
- Just set up a shared workspace and want to confirm agents can read/write
- Want to run a periodic health check
Fix mode:
- Recall returns irrelevant, empty, or noisy results
- Memorized data isn't being extracted correctly
- Guidelines aren't reaching agents or return wrong content
- Getting 429 rate limit errors or partial batch syncs
- Trigger.dev or n8n workflows are failing
- Shared workspace is going stale or agents aren't contributing
When NOT to Use This Skill
- Need to store data → use entity-memory
- Need to create guidelines → use governance
- Need to build a pipeline → use code-pipelines or no-code-pipelines
- Need to understand the architecture → use solution-architect
Works With Both SDK and MCP
| Interface | How it works | Best for |
|---|---|---|
SDK (@personize/sdk) |
Run verification scripts locally | Developers, CI/CD |
| MCP (Model Context Protocol) | Use memory_recall_pro, ai_smart_guidelines tools interactively |
Claude Desktop, ChatGPT, Cursor |
Quick Smoke Test
If the developer isn't sure what's wrong or just wants to verify everything works, run this:
import { Personize } from '@personize/sdk';
const client = new Personize({ secretKey: process.env.PERSONIZE_SECRET_KEY! });
// 1. Auth — can we connect?
try {
const me = await client.me();
console.log(`✅ Auth: ${me.data.organization.name} (${me.data.plan.name})`);
} catch (e) {
console.log('❌ Auth failed:', e.message);
console.log(' → Check PERSONIZE_SECRET_KEY');
}
// 2. Memory — can we store and retrieve?
try {
await client.memory.memorize({
email: 'diagnostics-test@example.com',
content: 'Test record for diagnostics. This person works at Acme Corp as a VP of Engineering.',
enhanced: true,
});
const recall = await client.memory.recall({
query: 'Who works at Acme Corp?',
email: 'diagnostics-test@example.com',
});
console.log(`✅ Memory: ${recall.data.memories.length > 0 ? 'stored and recallable' : '⚠️ stored but not yet recallable (indexing may take 1-2 min)'}`);
} catch (e) {
console.log('❌ Memory failed:', e.message);
if (e.message.includes('429')) console.log(' → Rate limited. See FIX: RATE-LIMITS.');
}
// 3. Governance — can we fetch guidelines?
try {
const guidelines = await client.ai.smartGuidelines({ message: 'test verification' });
console.log(`✅ Governance: ${guidelines.data.compiledContext ? 'responding' : '⚠️ no guidelines set up yet'}`);
} catch (e) {
console.log('❌ Guidelines failed:', e.message);
}
// 4. Digest — can we compile context?
try {
const digest = await client.memory.smartDigest({
email: 'diagnostics-test@example.com',
include_properties: true,
include_memories: true,
});
console.log(`✅ Digest: ${digest.data.compiledContext ? 'compiling context' : '⚠️ empty'}`);
} catch (e) {
console.log('❌ Digest failed:', e.message);
}
MCP equivalent
1. Call memory_store_pro with content "Test record. Works at Acme Corp as VP Engineering." and email "diagnostics-test@example.com"
2. Call memory_recall_pro with query "Who works at Acme Corp?" and email "diagnostics-test@example.com"
3. Call ai_smart_guidelines with message "test verification"
4. Confirm each returns data
Based on results, jump to the appropriate VERIFY or FIX action below.
VERIFY Actions
Use after setting up a capability to prove it works.
| Action | When to Use | Reference |
|---|---|---|
| VERIFY-MEMORY | After memorizing data — confirm it's stored and recallable | reference/verify-memory.md |
| VERIFY-GOVERNANCE | After setting up guidelines — confirm agents can see them | reference/verify-governance.md |
| VERIFY-PIPELINE | After wiring a pipeline — run one record end-to-end | reference/verify-pipeline.md |
| VERIFY-WORKSPACE | After setting up a shared workspace — confirm agents can read/write | reference/verify-workspace.md |
| HEALTH-CHECK | Ongoing — diagnose quality, performance, and coverage issues | reference/health-check.md |
FIX Actions
Use when something is broken. Jump to the action that matches the symptom.
| Action | When to Use | Reference |
|---|---|---|
| BAD-RECALL | Recall returns irrelevant, empty, or noisy results | reference/bad-recall.md |
| BAD-EXTRACTION | Memorized data isn't being extracted correctly | reference/bad-extraction.md |
| GOVERNANCE-MISS | Guidelines aren't reaching agents or return wrong content | reference/governance-miss.md |
| RATE-LIMITS | Getting 429 errors or partial batch syncs | reference/rate-limits.md |
| PIPELINE-FAILURE | Trigger.dev/n8n workflows failing or producing bad output | reference/pipeline-failure.md |
| WORKSPACE-STALE | Agents not contributing or workspace going stale | reference/workspace-stale.md |
Constraints
Keywords follow RFC 2119: MUST = non-negotiable, SHOULD = strong default (override with stated reasoning), MAY = agent discretion.
- MUST run the smoke test or appropriate verify action after every setup — because untested setups create false confidence and delayed failures.
- MUST run diagnostic steps before suggesting fixes — because guessing wastes time and can introduce new problems.
- MUST use real queries that match the developer's actual use case, not generic test strings — because verification with irrelevant queries can pass while real queries fail.
- SHOULD show the developer the actual API response, not just pass/fail — because seeing the data builds understanding and catches quality issues.
- SHOULD rank root causes by likelihood and check the most likely first — because systematic diagnosis is faster than shotgun debugging.
- SHOULD verify the fix worked by re-running the failing operation — because a fix that doesn't resolve the symptom isn't a fix.
- SHOULD clean up test data after verification — because leftover test records pollute memory and confuse downstream agents.
More from personizeai/personize-skills
personize-memory
Stores and retrieves persistent memory about records — contacts, companies, employees, members, and more. Handles memorization (single and batch with per-property AI extraction), semantic recall, entity digests, and data export. Use this skill whenever the user wants to store data, sync records from a CRM or database, query or search memory, recall what's known about a person or company, assemble context for personalization, import CSV or spreadsheet data, or do anything involving the Personize SDK's memory methods (memorize, recall, smartRecall, smartDigest, search, memorizeBatch). Also use when the user mentions contacts, leads, accounts, customer data, or entity properties.
21entity-memory
Stores and retrieves persistent memory about records — contacts, companies, employees, members, and more. Handles memorization (single and batch with per-property AI extraction), semantic recall, entity digests, and data export. Use this skill whenever the user wants to store data, sync records from a CRM or database, query or search memory, recall what's known about a person or company, assemble context for personalization, import CSV or spreadsheet data, or do anything involving the Personize SDK's memory methods (memorize, recall, smartRecall, smartDigest, search, memorizeBatch). Also use when the user mentions contacts, leads, accounts, customer data, or entity properties.
12no-code-pipelines
Generates importable n8n workflow JSON files that sync data between Personize and 400+ apps. Produces ready-to-import workflows for batch sync, webhook ingestion, per-record AI enrichment, and data export — no code required. Use this skill whenever the user wants no-code integrations, visual workflows, n8n automation, or to connect Personize to HubSpot, Salesforce, Google Sheets, Slack, Postgres, or any app without writing code. Also trigger when they mention 'workflow automation', 'scheduled sync without code', 'visual pipeline', or 'connect Personize to [app]' and don't want to write TypeScript.
12code-pipelines
Builds, deploys, and iterates production-ready AI agent pipelines using Trigger.dev and the Personize SDK. Handles the full lifecycle: interview the user about what they want, design the schema and governance, write the pipeline code, deploy it, monitor results, and iterate based on feedback. Generates TypeScript tasks for outbound sequences, inbound lead processing, conversational reply handlers, enrichment pipelines, and account signal monitoring — all backed by Personize memory, AI context, and governance. Use this skill whenever someone wants to build an AI agent, automated workflow, email sequence, drip campaign, cold outreach, lead enrichment, reply handler, account monitor, CRM automation, daily digest, or any durable pipeline — whether they provide technical specs or just describe what they want in plain language. Also trigger for Trigger.dev, background tasks, self-scheduling follow-ups, GTM automation, 'build me an agent that...', or 'I want to automate...'.
11collaboration
Turn any record into a shared workspace where agents and humans collaborate. Attach a simple workspace schema to any entity — contacts, companies, deals, projects, tickets — and let any participant contribute updates, tasks, notes, and issues. Use this skill whenever the user wants multi-agent collaboration, shared context on an entity, agent handoffs, workspace-based coordination, or the three-layer agent operating model (Guidelines + Memory + Workspace). Also trigger when they mention multiple agents working on the same record, deal rooms, account intelligence, customer health monitoring, cross-functional coordination, or progressive autonomy for AI agents.
11governance
Manages organizational guidelines, policies, and best practices as governance variables accessible to all AI agents via SmartContext. Use this skill whenever the user wants to create, update, or manage guidelines, brand voice, compliance policies, playbooks, ICPs, sales playbooks, tone rules, or any organizational rules. Also trigger when the user mentions smartGuidelines, governance variables, GitOps sync of policies, team knowledge sharing, AI agent rules, or when they want all their AI tools to follow the same policies. Even if they just say 'set up rules' or 'add a policy', this is the right skill.
11