Algolia Release QA

Use this skill before launching or after a risky change. Prioritize defects that affect discoverability, conversion, analytics integrity, security, or operational rollback.

Customer-Facing Standard

• Lead with launch blockers and high-risk findings, not a generic summary.
• State what was tested, what was not tested, and what evidence supports each finding.
• Use public academy.algolia.com for learning alignment and public algolia.com/doc for current implementation guidance when source-backed context is needed.
• When an Academy metadata reference pack is available, use only its title, url, course, module, learning_objectives, and updated_at fields for structure. If it is stale or no match exists, fall back to live Academy/docs lookup. Do not treat cached metadata as course content or implementation authority.
• Do not require custom Academy/docs access; use customer-provided sources only as optional context.
• When live Algolia data, analytics, index inspection, settings changes, or account actions are needed, use Algolia MCP, the Algolia CLI, or official Algolia skills for the live operation, then apply this skill to interpret results and validate the customer-ready implementation path.
• Do not say launch-ready without validating data, settings, UI, events, security, and rollback where relevant.
• Build an evidence matrix before assigning a release status: evidence must identify the surface, scenario, source, result, owner, and remaining risk.

Official Companion Skills

• Use official algolia-mcp for live search, analytics, recommendations, index discovery, and evidence gathering.
• Use official algolia-cli for settings, rules, synonyms, records, keys, backups, and admin verification.
• Use official instantsearch for UI implementation correctness, framework-specific source-of-truth checks, and frontend search QA.
• Use official algobot-cli for Agent Studio, conversational, RAG, memory, tool, and deployment QA.
• Use this skill after official tool calls to write severity-led customer findings, owners, fixes, tests run, and residual risk.

Source-Of-Truth Rules

• Do not mark an implementation launch-ready from screenshots or code alone when data, settings, events, security, or rollback are in scope.
• Use official tools for live evidence and current docs for feature-specific requirements.
• Separate verified findings from untested risks. If evidence is missing, say exactly what could not be inspected.
• For search implementations, use the cross-skill readiness signposts from algolia-search-implementation before saying launch-ready, prototype-ready, or ready with documented follow-ups.

Workflow

1. Read references/release-qa-checklist.md for the relevant surface.
2. Read references/example-output.md when writing a customer-facing launch or regression report.
3. Capture what changed: data model, settings, UI, events, environment, credentials, or deployment.
4. Test representative happy paths and failure paths.
5. Verify attribution as a chain: search or browse request, queryID, hit identity and position, event payload, debugger/arrival, and downstream usability.
6. For experiments, confirm the hypothesis, isolated change, traffic/data sufficiency, event coverage, confidence state, and rollback decision instead of treating a dashboard metric as self-explanatory.
7. Report findings by severity with concrete reproduction steps, expected behavior, evidence source, and owner.
8. Write the report so a non-specialist customer can decide what to fix now versus later.
9. Include residual risk when production data, analytics windows, credentials, or live tools cannot be inspected.

QA Areas

• Data and indexing: objectIDs, record counts, freshness, replicas, secured/hidden records, partial updates.
• Relevance: top queries, zero-result queries, filters, facets, synonyms, rules, sort orders, typo tolerance.
• UI: routing, pagination, empty states, loading/errors, mobile filters, accessibility, analytics attribution.
• Events: userToken consistency, queryID propagation, positions, conversion taxonomy, duplicate-event risk.
• Security and operations: API key exposure, secured API keys, environments, settings backup, rollback.
• AI surfaces: NeuralSearch event prerequisites, semantic/evaluation query regressions, Agent Studio tool authority, memory, guardrails, and feedback loops.

Algolia Search Readiness Signposts

Use these signposts to decide whether the implementation is launch-ready, prototype-ready, or ready with accepted follow-ups:

• Data contract exists.
• Index settings match the UI.
• Records include all display, facet, ranking, merchandising, inventory, and event-attribution fields used by the experience.
• Autocomplete has source and selection contracts when autocomplete is included.
• Search results or browse pages handle query, hits, filters, sort, current refinements, recovery, pagination or infinite loading, empty states, loading, and errors when those surfaces are included.
• Events taxonomy exists.
• Click events are implemented, validated, or explicitly deferred by the user.
• Conversion events are planned, implemented, validated, or explicitly deferred by the user.
• Live index validation queries pass when live data is available.
• Browser QA passes for the relevant UI surfaces when a browser is available.

If any item is missing, report it as a launch blocker, accepted deferral, or residual risk depending on severity and the user's explicit decisions.

Anti-Patterns

• Reporting a checklist as passed without evidence, screenshots, payloads, settings exports, query results, or code references.
• Burying launch blockers under a positive summary.
• Treating event payload presence as success without checking identity, attribution, duplicate risk, and downstream visibility.
• Ignoring rollback, environment separation, API-key scope, or secured data because the UI appears to work.
• Calling an A/B test a win before its data and confidence state support interpretation, or when multiple unrelated changes prevent causal attribution.
• Marking an event as valid only because it reached the debugger without checking queryID, userToken, objectID, position, duplicate ownership, and downstream feature usability.

Academy And Customer Education Alignment

When source-backed guidance is needed, search public Academy sources for launch and QA learning objectives and public Algolia docs for data, relevance, UI, events, analytics, security, and AI-readiness details. Convert source guidance into maturity-aware checks and validation artifacts for the customer team.

Maturity Behavior

• Beginner implementation: focus on whether the basic data, settings, UI, and event paths work at all.
• Production readiness: lead with launch blockers, evidence matrix, security, rollback, attribution chain, mobile UX, and indexing reliability.
• Optimization: test representative queries, analytics quality, A/B readiness and confidence, merchandising behavior, and regression risk.
• AI readiness: verify NeuralSearch event prerequisites and evaluation queries plus Agent Studio data, permissions, tool authority, guardrails, feedback loops, and readiness gates.

Output Contract

Lead with findings, not a generic summary. Include:

• Severity and affected surface.
• Evidence or reproduction.
• Recommended fix.
• Owner or role needed to fix it.
• Tests run and tests not run.
• Evidence matrix and residual risk.

If no issues are found, say so clearly and identify the remaining risks.