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kensauruskensaurus/cursor-kenji/skills/deploy-verify/SKILL.md

deploy-verify

Skill9 starsChanged 42 days agoMITView on GitHub

Post-deploy smoke test across browser, Sentry, Supabase, Langfuse, and the public web. Use when "verify deploy", "smoke test production", "post-release check", or "ship or rollback". Deploy plus watch → workflow-ship-and-observe.

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  • bitwize-music-studioSkill

    session-start

    Runs the session startup procedure - verifies setup, loads config and state, checks skill models, and reports project status. Use at the beginning of a fresh session.

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The file

Show its rules and text

We split this skill into the rules it gives an agent

13 rules. 0 are common, 11 are distinctive, and 2 are very specific to this project.

  • 0

    Common

    Most of the 7 edited versions we read keep these.

  • 11

    Distinctive

    This author's own choices. Worth a look.

  • 2

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Use the detected org slug and project slug from Phase 0c.

    line 139
  2. Distinctive:

    Run a quick data integrity check on the most important tables:

    line 304
  3. Distinctive:

    All logs clean, migrations applied, no advisor warnings → PASS

    line 314
  4. Distinctive:

    No new traces but deploy just happened → MONITOR (wait 5 more minutes)

    line 352
  5. Very specific:

    Never block in one long sleep — use the incremental sleep 2 → snapshot cycle (max 3)

    line 368
  6. Very specific:

    Use snapshot to detect ready state

    line 369
  7. Distinctive:

    If the app has auth:

    line 391
  8. Distinctive:

    Verify login form renders

    line 393
  9. Distinctive:

    If it's a form: fill and submit

    line 399
  10. Distinctive:

    If it's a list: verify items render

    line 400
  11. Distinctive:

    If it's a dashboard: verify data loads (not empty state when data should exist)

    line 401
  12. Distinctive:

    If it's an AI feature: trigger one generation and verify response appears

    line 402
  13. Distinctive:

    All critical paths load, no console errors, no 5xx network requests → PASS

    line 411

“Common” is measured: kept by at least 6 in 10 of the 7 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Post-Deploy Verification

Degree of freedom: MIXED. Verdict and MONITOR escalation [HIGH freedom]; probe sequences and the report table [LOW freedom — run exactly].

Automated post-deploy health check across all services. Run after every deploy to catch regressions before users do. Works with any project — auto-detects configuration.

How to reason

  1. Observe — what did each probe actually return (counts, logs, screenshots)?
  2. Interpret — new-since-deploy vs pre-existing; skip-missing vs real fail
  3. Classify — PASS / MONITOR / FAIL per phase
  4. Severity — auth/data-loss/5xx on the main path outranks a Langfuse quiet window

Worked example

Observe: home loads; Sentry shows 12 new TypeError on /checkout; Supabase API 200s. Interpret: checkout is a critical path; errors started with this release. Classify: Sentry FAIL, smoke FAIL on checkout, DB PASS. Verdict: ROLLBACK — main money path is 5xx, not a MONITOR item.

Self-critique before reporting

  • Evidence — every row cites a probe result, not "looks fine"
  • Binary verdict — SHIP / MONITOR / HOTFIX / ROLLBACK with a next action
  • Skip honesty — missing Sentry/Supabase/Langfuse is SKIP, not PASS
  • Right owner — npm publish → deploy-npm; observe window → workflow-ship-and-observe

Rules

Run within 15 minutes of deploy. The sooner you check, the smaller the blast radius.

Parallel where possible. Sentry, Supabase, and Langfuse checks are independent — issue them in one message (or one subagent each) so they run in parallel. Time matters here: do not spend time that can be avoided.

The verdict must be binary. SHIP, ROLLBACK, or MONITOR (with clear criteria for when MONITOR escalates to ROLLBACK).

Evidence over opinion. Every check produces a PASS/FAIL with specific data. Never say "looks fine."

Always use the protocol-browser-anti-stall protocol when using playwright-cli.


Phase 0: Auto-Detect Deployment Context [LOW freedom — run exactly]

0a. Detect Deployment Platform

Grep(pattern: "vercel\\.json|netlify\\.toml|fly\\.toml|render\\.yaml|railway\\.json|Dockerfile|docker-compose|appspec\\.yml", output_mode: "files_with_matches")
Grep(pattern: "VERCEL_URL|RENDER_EXTERNAL_URL|FLY_APP_NAME|RAILWAY_STATIC_URL", glob: ".env*")

0b. Find Production URL

Grep(pattern: "NEXT_PUBLIC_APP_URL|NEXT_PUBLIC_BASE_URL|VITE_APP_URL|PUBLIC_URL|PRODUCTION_URL|APP_URL", glob: ".env*")
Grep(pattern: "https://.*\\.vercel\\.app|https://.*\\.netlify\\.app|https://.*\\.fly\\.dev|https://.*\\.railway\\.app", glob: ".env*")

Also check framework config files:

Grep(pattern: "url|domain|hostname", glob: "{vercel.json,netlify.toml,next.config.*}")

0c. Detect Sentry Release Tracking

Grep(pattern: "SENTRY_ORG|SENTRY_PROJECT|SENTRY_DSN|SENTRY_AUTH_TOKEN|@sentry/nextjs|@sentry/react|@sentry/node|sentry-cli", glob: "{.env*,package.json,.sentryclirc,sentry.*.config.*}")

0d. Detect Supabase Project

Grep(pattern: "SUPABASE_URL|NEXT_PUBLIC_SUPABASE_URL|SUPABASE_SERVICE_ROLE_KEY|SUPABASE_ANON_KEY", glob: ".env*")

The project ref is the subdomain of the Supabase URL (https://<ref>.supabase.co). Confirm it with supabase:get_project_url.

The pack's Supabase MCP config is project-scoped (--project-ref), so its tools take no project_id and account tools such as list_projects are not registered. On a server started without a project ref, call supabase:list_projects to match the URL, then add "project_id": "<PROJECT_ID>" to each Supabase call below.

0e. Detect Langfuse Integration

Grep(pattern: "LANGFUSE_PUBLIC_KEY|LANGFUSE_SECRET_KEY|LANGFUSE_HOST|@langfuse", glob: "{.env*,package.json}")

0f. Find Critical User Flows

Detect the app's route structure to identify critical paths for smoke testing:

Glob("**/app/**/page.{tsx,jsx,ts,js}")
Glob("**/pages/**/*.{tsx,jsx,ts,js}")
Glob("**/src/routes/**/*.{tsx,jsx,svelte}")

Identify the critical paths (prioritized):

  1. Landing/home page
  2. Authentication (login/signup)
  3. Main feature (the primary value proposition)
  4. One CRUD operation (create or update something)
  5. Any AI/LLM feature (if present)

Record all detected values:

  • PRODUCTION_URL
  • SENTRY_ORG, SENTRY_PROJECT
  • SUPABASE_PROJECT_ID
  • LANGFUSE_AVAILABLE (boolean)
  • CRITICAL_PATHS (list of URLs to smoke test)

Phase 1: Sentry Error Check [LOW freedom — run exactly]

1a. Find Organization and Project

sentry:find_organizations
{}

Use the detected org slug and project slug from Phase 0c.

1b. Check Recent Releases

find_releases is a catalog tool, not a direct one, so call it through execute_sentry_tool. If the call is rejected, run search_sentry_tools with "find releases" to get the current name and schema.

sentry:execute_sentry_tool
{
 "name": "find_releases",
 "arguments": {
  "organizationSlug": "<ORG_SLUG>",
  "projectSlug": "<PROJECT_SLUG>"
 }
}

Verify:

  • Latest release matches the deploy (commit hash or version tag)
  • Release was created within the expected deploy window

1c. Search for New Issues Since Deploy

sentry:search_issues
{
 "organizationSlug": "<ORG_SLUG>",
 "projectSlugOrId": "<PROJECT_SLUG>",
 "query": "is:unresolved firstSeen:-1h",
 "sort": "freq"
}

Also check for regressions (previously resolved issues that re-opened):

sentry:search_issues
{
 "organizationSlug": "<ORG_SLUG>",
 "projectSlugOrId": "<PROJECT_SLUG>",
 "query": "is:regressed",
 "sort": "freq"
}

1d. Seer Analysis on P0 Issues

For any new issue with high event count or critical severity, run Sentry's AI root-cause analysis:

sentry:analyze_issue_with_seer
{
 "organizationSlug": "<ORG_SLUG>",
 "issueId": "<ISSUE_ID>"
}

This provides:

  • Root cause explanation with code-level detail
  • Specific file locations and line numbers
  • Concrete code fix suggestions

Decision criteria:

  • 0 new issues → PASS
  • New issues but low frequency (<5 events) and non-critical → MONITOR
  • New high-frequency issues or regressions → FAIL (potential rollback trigger)
  • Seer identifies a one-line fix → APPLY FIX instead of rollback

Phase 2: Supabase Health Check [LOW freedom — run exactly]

Skip this phase if no Supabase integration detected.

2a. Migration Health

supabase:list_migrations
{}

Verify:

  • Latest migration matches what the deploy expected
  • No failed or pending migrations

2b. API Logs (last 30 minutes)

Logs come from query_logs: read-only ClickHouse SQL over one logs table, filtered by source. Current Supabase MCP servers, local and hosted, list it instead of get_logs. Set iso_timestamp_start to 30 minutes ago; without it the window is the last 24 hours.

supabase:query_logs
{
 "sql": "select timestamp, event_message, log_attributes['request.method'] as method, log_attributes['request.path'] as path, log_attributes['response.status_code'] as status_code from logs where source = 'edge_logs' order by timestamp desc limit 100",
 "iso_timestamp_start": "<ISO_8601_30_MIN_AGO>"
}

Check for:

  • 5xx errors (server errors)
  • Unusual 4xx spike (broken client code)
  • Slow queries (>1s response time)

2c. Edge Function Logs

supabase:query_logs
{
 "sql": "select timestamp, event_message, log_attributes['response.status_code'] as status_code, log_attributes['function_id'] as function_id, log_attributes['execution_time_ms'] as execution_time_ms from logs where source = 'function_edge_logs' order by timestamp desc limit 100",
 "iso_timestamp_start": "<ISO_8601_30_MIN_AGO>"
}

Boot errors and exceptions print to the console stream, function_logs:

supabase:query_logs
{
 "sql": "select timestamp, event_message, log_attributes['level'] as level, log_attributes['function_id'] as function_id from logs where source = 'function_logs' order by timestamp desc limit 100",
 "iso_timestamp_start": "<ISO_8601_30_MIN_AGO>"
}

Check for:

  • Function boot errors
  • Runtime exceptions
  • Timeout errors

2d. Auth Logs

supabase:query_logs
{
 "sql": "select timestamp, event_message, log_attributes['level'] as level, log_attributes['status'] as status, log_attributes['path'] as path, log_attributes['error'] as error from logs where source = 'auth_logs' order by timestamp desc limit 100",
 "iso_timestamp_start": "<ISO_8601_30_MIN_AGO>"
}

Check for:

  • Auth failures unrelated to wrong credentials
  • Token refresh errors
  • RLS policy violations

2e. Security and Performance Advisors

supabase:get_advisors
{
 "type": "security"
}
supabase:get_advisors
{
 "type": "performance"
}

Flag any new warnings that weren't present before the deploy.

2f. Critical Data Integrity

Run a quick data integrity check on the most important tables:

supabase:execute_sql
{
 "query": "SELECT schemaname, relname, n_dead_tup, last_autovacuum FROM pg_stat_user_tables WHERE n_dead_tup > 10000 ORDER BY n_dead_tup DESC LIMIT 5"
}

Decision criteria:

  • All logs clean, migrations applied, no advisor warnings → PASS
  • Minor warnings in logs but no 5xx → MONITOR
  • 5xx errors in API/Edge logs or failed migrations → FAIL

Phase 3: Langfuse Health Check

Skip this phase if no Langfuse integration detected.

3a. Check Recent Traces

npx langfuse-cli api traces list --limit 10

Verify:

  • New traces are arriving (timestamps within last 15 minutes)
  • Trace names match expected AI features
  • No error status on traces

3b. Check Latency and Errors

From the trace list, examine:

  • Average latency compared to baseline (is it significantly higher post-deploy?)
  • Any traces with error status or missing generations
  • Token usage anomalies (sudden spike could indicate prompt regression)

3c. Verify Prompt Versions

npx langfuse-cli api prompts list

Confirm that active prompt versions match what the deploy should be using (check the production label).

Decision criteria:

  • Traces flowing, normal latency, correct prompt versions → PASS
  • No new traces but deploy just happened → MONITOR (wait 5 more minutes)
  • Error traces or latency spike → FAIL

Phase 4: Playwright Smoke Test

4a. Navigate to Production

PW="npx --yes @playwright/cli@latest"
$PW -s=deploy-verify open --headed "<PRODUCTION_URL>"

Important: Apply the protocol-browser-anti-stall protocol:

  • Set 15-second timeout expectations
  • Never block in one long sleep — use the incremental sleep 2 → snapshot cycle (max 3)
  • Use snapshot to detect ready state

4b. Test Critical Paths

For each critical path identified in Phase 0f:

  1. Navigate to the page
  2. Snapshot to verify it loaded (not a blank page or error screen)
  3. Check console for errors:
$PW -s=deploy-verify console
  1. Check network for failed requests:
$PW -s=deploy-verify requests

4c. Test Authentication (if applicable)

If the app has auth:

  1. Navigate to login page
  2. Verify login form renders
  3. (If test credentials available) Log in and verify redirect to authenticated area

4d. Test Main Feature

Navigate to the primary feature and perform one basic interaction:

  • If it's a form: fill and submit
  • If it's a list: verify items render
  • If it's a dashboard: verify data loads (not empty state when data should exist)
  • If it's an AI feature: trigger one generation and verify response appears

4e. Take Evidence Screenshot

$PW -s=deploy-verify screenshot --filename ".playwright-mcp/deploy-verify-home.png"

Decision criteria:

  • All critical paths load, no console errors, no 5xx network requests → PASS
  • Minor visual glitches but functional → MONITOR
  • Pages fail to load, 5xx errors, or auth broken → FAIL

Phase 5: Ship-or-Rollback Verdict

Aggregate all phase results into a final verdict.

Decision Matrix

ScenarioVerdict
All phases PASSSHIP — Deploy is healthy
All phases PASS except 1-2 MONITOR itemsMONITOR — Watch for 1 hour, re-check
Any phase FAIL with a quick fix availableHOTFIX — Apply fix and re-verify
Any critical phase FAIL (auth broken, data loss, 5xx on main feature)ROLLBACK — Revert immediately
Seer identifies a simple root cause for the only FAILHOTFIX — Apply Seer's fix, re-verify

Report

═══════════════════════════════════════════════════════
 POST-DEPLOY VERIFICATION REPORT
 Project: <PROJECT_NAME>
 Deploy Time: <TIMESTAMP>
 Production URL: <URL>
 Checked At: <CHECK_TIMESTAMP>
═══════════════════════════════════════════════════════

## HEALTH CHECK RESULTS

| Check | Status | Details |
|------------------------------|---------|--------------------------------------|
| Sentry: new errors | ✅/❌ | [0 new / N new — list top issues] |
| Sentry: regressions | ✅/❌ | [0 regressed / N regressed] |
| Sentry: release tracked | ✅/❌ | [release ID and timestamp] |
| Supabase: migrations | ✅/❌ | [all applied / N pending/failed] |
| Supabase: API logs | ✅/❌ | [clean / N errors in last 30m] |
| Supabase: Edge Function logs | ✅/❌ | [clean / N errors] |
| Supabase: Auth logs | ✅/❌ | [clean / N errors] |
| Supabase: advisors | ✅/❌ | [no new warnings / N new warnings] |
| Langfuse: traces flowing | ✅/❌ | [N traces in last 15m / no traces] |
| Langfuse: latency | ✅/❌ | [normal / Xms above baseline] |
| Langfuse: prompt versions | ✅/❌ | [match expected / mismatch on N] |
| Smoke: home page | ✅/❌ | [loads in Xs / error] |
| Smoke: auth flow | ✅/❌ | [works / broken] |
| Smoke: main feature | ✅/❌ | [works / broken] |
| Smoke: console errors | ✅/❌ | [0 errors / N errors] |
| Smoke: network 5xx | ✅/❌ | [0 / N failed requests] |

## SEER ANALYSIS (if any P0 issues)

Issue: <ISSUE_ID>
Root Cause: <SEER_EXPLANATION>
Suggested Fix: <CODE_FIX>
Files Affected: <FILE_LIST>

## VERDICT

┌─────────────────────────────────────────────────┐
│ │
│ [SHIP / MONITOR / HOTFIX / ROLLBACK] │
│ │
│ Reason: <one-line explanation> │
│ │
│ Next action: <what to do now> │
│ │
└─────────────────────────────────────────────────┘

## MONITOR ESCALATION (if verdict is MONITOR)

Re-check in: [30 minutes / 1 hour]
Escalate to ROLLBACK if: [specific condition, e.g., error count exceeds 50]
Watch: [specific metrics to monitor]

═══════════════════════════════════════════════════════

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