Does this launch an autonomous QA department?

It coordinates quality responsibilities in the connected agent session. The host performs the authorized project work. It can delegate independent roles when that host supports subagents; otherwise it labels its role checks as self-review.

What is included beyond role prompts?

Versioned handoff contracts, a standard-library Python state and evidence control plane, required-case gates, stale-evidence checks, a work-item overview, feedback records and local metrics. The workflow also specifies interviewing, test sufficiency, defects/RCA and production closure.

Will it deploy my application?

Deployment remains with your authorized operator or deployment tools. The quality system records the observed deployment and validates its identity before accepting production results.

Can a simulated production check mark the task Done?

No. Simulations support rehearsals but cannot satisfy the real production Done gate.

Does the purchase include AI usage or shared team seats?

Model/tool usage comes from your existing host and accounts. Buyer licensing follows Agensi's terms; this listing does not grant shared seats or redistribution.

Quality Department — Idea to Production QA

Example session with this skill installed

Local fictional fixture request: “Our checkout discount may be wrong. For 1000 cents with a 10% discount, the expected total is 900 cents. Clarify rounding, plan proportionate tests, reproduce and investigate the defect, verify the fix, and show what remains before production closure. Use the included local calculation fixture only; do not call a payment or production service.”

Read your context and instructions
Compiled the quality department

Observed seller-side local fixture demonstration; all product data and deployment records below are synthetic. No payment or production provider was called.

The context interviewer first held the work because rounding was unresolved. The fixture then supplied round-half-up to cents as its explicit answer. The strategy selected a nominal calculation check and a boundary matrix, with identifiable source, plan and environment versions.

The first actual local calculation produced 810 cents where the oracle required 900. The execution observation and an open blocking defect kept release readiness false. Root cause analysis identified the discount factor being applied twice. After the local fix, the same nominal calculation produced 900. The recorded boundary matrix covered zero discount, full discount, half-cent rounding and rejected negative input. Two required non-production cases had current passing evidence, and the blocking defect was resolved with its source and regression evidence.

The quality lead advanced the fixture to ready-to-deploy after the evidence-review handoff. A simulated deployment and simulated smoke observation were then recorded for rehearsal. The Done attempt was rejected: “Done requires a real production deployment; simulation is not production validation.” The current owner remained the production validator. The generated overview showed the unresolved production gate rather than turning the local pass into a real release claim.

This demonstrates the local control-plane and host-observation workflow. It does not prove the behavior of a real checkout, deployment service, or customer environment.

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About this skill

Launch pricing: $39 USD, one-time, until 29 October 2026 at 10:53:46 UTC (11:53:46 CET, Europe/Amsterdam). The regular price will be $69 USD thereafter.

Give a software change a quality owner from its first unclear idea to the evidence needed after deployment.

Quality Department guides the connected coding agent through customer discovery, testable acceptance criteria, risk-based strategy, traceable test cases, authorized test implementation and execution, defect reproduction, root cause analysis, and release verification. Each handoff has an input, output, acceptance rule, current owner, and next action. The strategy decides what to test, at which layer, how deeply, and when enough evidence exists.

The included Python reference control plane records customer-owned work items and imported run observations, validates artifact integrity and lifecycle gates, and makes earlier evidence stale when recorded source, plan, or environment identities change. A process overview shows stages, owners, blockers, required evidence, and the next action across explicit work-item roots. Ready to deploy is a recommendation. Done requires a recorded real production deployment and matching required production checks. Simulated evidence cannot close Done.

This is a host-assisted system loaded on demand through Agensi. The host provides the model, repository tools, test execution, browser or connected systems, and optional independent subagents. The package does not run an always-on service, deploy a product, supply credentials, or independently authenticate imported observations. Bundled helpers run in-session; customer-specific tests, briefs, evidence and reports stay in the customer's project.

How to use

Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.

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Quality Department — Idea to Production QA