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SKILL verified MIT Self-run

Claude Agent Pre Deploy Qa

skill-pavel-molyanov-molyanov-ai-dev-claude-agent-pre-deploy-qa · by pavel-molyanov

Converted Codex role prompt from Claude agent `pre-deploy-qa`. Use when the user asks for this reviewer/validator role or when a workflow explicitly references it.

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Install

$ agentstack add skill-pavel-molyanov-molyanov-ai-dev-claude-agent-pre-deploy-qa

✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.

Security review

✓ Passed

No issues found. Passed automated security review. · v0.1.0 How review works →

  • Prompt-injection patterns
  • Secret / credential exfiltration
  • Dangerous shell & filesystem operations
  • Untrusted network calls
  • Known-malicious package signatures

What it can access

  • Network access No
  • Filesystem access No
  • Shell / process execution No
  • Environment & secrets No
  • Dynamic code execution No

From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.

View the full security report →

Verified badge

Passed review? Show it. Paste this badge into your README, it links to the public security report.

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Reliability & compatibility

Security review passed
0 installs to date
no reviews yet
1mo ago

Declared compatibility

Claude CodeClaude Desktop

Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.

Preview Execution monitoring

We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.

How agent discovery & health will work →
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About

Converted Role: pre-deploy-qa

Generated from ~/.claude/agents/pre-deploy-qa.md. Codex does not have native Claude custom agent types. Use this as a role/reference prompt with worker or explorer subagents when subagents are explicitly appropriate.

Follow the pre-deploy-qa skill methodology loaded above.

Input

Receive from orchestrator:

  • Feature working directory path (e.g., work/{feature}/)
  • Project Knowledge path (if exists) — for architecture.md, patterns.md (incl. Testing section)

Output

Write JSON report to work/{feature}/logs/working/pre-deploy-qa-report.json:

{
  "status": "passed | failed",
  "summary": {
    "totalChecks": 0,
    "passed": 0,
    "failed": 0,
    "notVerifiable": 0,
    "criticals": 0,
    "majors": 0,
    "minors": 0
  },
  "testSuite": {
    "status": "passed | failed",
    "details": "All 42 tests passed"
  },
  "acceptanceCriteria": [
    {
      "criterion": "User can login with email",
      "status": "passed | failed | not_verifiable",
      "evidence": "Test login_test.py::test_email_login passes"
    }
  ],
  "findings": [
    {
      "severity": "critical | major | minor",
      "title": "Login fails for emails with + sign",
      "expected": "Login succeeds",
      "actual": "400 Bad Request",
      "reproduction": "Steps to reproduce..."
    }
  ],
  "deferredToPostDeploy": [
    {
      "criterion": "US-5: Titles generated with correct declensions",
      "reason": "Requires live LLM call with real data",
      "verificationCondition": "New survey entry processed after deploy",
      "verificationSteps": "Run a survey entry through the bot, check generated title"
    }
  ]
}

Status Decision

  • passed — zero criticals
  • failed — one or more criticals

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

Install and usage instructions live in the source repository linked above.

Reviews

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Versions

  • v0.1.0 Imported from the upstream source.