Qcsd Refinement Swarm

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proffesor-for-testing

qcsd-refinement-swarm

Use when running Sprint Refinement sessions with SFDIPOT product factors, generating BDD scenarios, or validating requirements in the QCSD Refinement phase.

Overview

Publisher	proffesor-for-testing
Repository	`agentic-qe`
Skill name	`qcsd-refinement-swarm`
Stars	480
Forks	92
Bundled files	12
License	MIT
Links	Skill source Repository Homepage

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
12 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by proffesor-for-testing on GitHub. Read the source before you install it.

Installation

Install the Qcsd Refinement Swarm AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/proffesor-for-testing/agentic-qe/tree/main/assets/skills/qcsd-refinement-swarm

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/proffesor-for-testing/agentic-qe.git /tmp/agentic-qe
mkdir -p .claude/skills
cp -r /tmp/agentic-qe/assets/skills/qcsd-refinement-swarm .claude/skills/qcsd-refinement-swarm

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Qcsd Refinement Swarm in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Qcsd Refinement Swarm on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Qcsd Refinement Swarm is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

QCSD Refinement Swarm v1.0

Shift-left quality engineering swarm for Sprint Refinement sessions.

Overview

The Refinement Swarm takes user stories that passed Ideation and prepares them for Sprint commitment using SFDIPOT product factors, BDD scenarios, and INVEST validation. It renders a READY / CONDITIONAL / NOT-READY decision.

QCSD Phase Positioning

Phase	Swarm	Decision	When
Ideation	qcsd-ideation-swarm	GO / CONDITIONAL / NO-GO	PI/Sprint Planning
Refinement	qcsd-refinement-swarm	READY / CONDITIONAL / NOT-READY	Sprint Refinement
Development	qcsd-development-swarm	SHIP / CONDITIONAL / HOLD	During Sprint
Verification	qcsd-cicd-swarm	RELEASE / REMEDIATE / BLOCK	Pre-Release / CI-CD
Production	qcsd-production-swarm	HEALTHY / DEGRADED / CRITICAL	Post-Release

Parameters

STORY_CONTENT: User story with acceptance criteria (required)
OUTPUT_FOLDER: Where to save reports (default: ${PROJECT_ROOT}/Agentic QCSD/refinement/)

ENFORCEMENT RULES - READ FIRST

Rule	Enforcement
E1	MUST spawn ALL THREE core agents in Step 2.
E2	MUST put all parallel Task calls in a SINGLE message.
E3	MUST STOP and WAIT after each batch.
E4	MUST spawn conditional agents if flags are TRUE.
E5	MUST apply READY/CONDITIONAL/NOT-READY logic exactly.
E6	MUST generate the full report structure.
E7	Each agent MUST read its reference files before analysis.
E8	MUST apply qe-test-idea-rewriter transformation in Step 8.
E9	MUST execute Step 7 learning persistence.

Step Execution Protocol

Execute steps sequentially by reading each step file with the Read tool.

Steps

Flag Detection -- steps/01-flag-detection.md -- Analyze story content, evaluate all 7 flags
Core Agents -- steps/02-core-agents.md -- Spawn qe-product-factors-assessor, qe-bdd-generator, qe-requirements-validator
Batch 1 Results -- steps/03-batch1-results.md -- Wait and extract metrics
Conditional Agents -- steps/04-conditional-agents.md -- Spawn flagged agents
Decision Synthesis -- steps/05-decision-synthesis.md -- Apply READY/CONDITIONAL/NOT-READY logic
Report Generation -- steps/06-report-generation.md -- Generate refinement report
Learning Persistence -- steps/07-learning-persistence.md -- Store findings to memory
Transformation -- steps/08-transformation.md -- Run test idea rewriter on all test ideas
Final Output -- steps/09-final-output.md -- Display completion summary

Execution Instructions

Use the Read tool to load the current step file
Execute the step's instructions completely
Verify all success criteria are met
Pass output as context to next step
If a step fails, halt and report

Resume Support

To resume from a specific step: specify --from-step N.

Agent Inventory

Agent	Type	Domain	Batch
qe-product-factors-assessor	Core	requirements-validation	1
qe-bdd-generator	Core	requirements-validation	1
qe-requirements-validator	Core	requirements-validation	1
qe-contract-validator	Conditional (HAS_API)	contract-testing	2
qe-impact-analyzer	Conditional (HAS_REFACTORING)	code-intelligence	2
qe-dependency-mapper	Conditional (HAS_DEPENDENCIES)	code-intelligence	2
qe-middleware-validator	Conditional (HAS_MIDDLEWARE)	enterprise-integration	2
qe-odata-contract-tester	Conditional (HAS_SAP_INTEGRATION)	enterprise-integration	2
qe-sod-analyzer	Conditional (HAS_AUTHORIZATION)	enterprise-integration	2
qe-test-idea-rewriter	Transformation (always)	test-generation	3

Total: 10 agents (3 core + 6 conditional + 1 transformation)

Key Principle

Refinement quality determines sprint success. This swarm ensures stories are testable, complete, and ready for development commitment.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Qcsd Refinement Swarm AI skill do?

Use when running Sprint Refinement sessions with SFDIPOT product factors, generating BDD scenarios, or validating requirements in the QCSD Refinement phase.

Why use Qcsd Refinement Swarm on TypingMind?

Because you install it once and use it with any model. Qcsd Refinement Swarm is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Qcsd Refinement Swarm in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/proffesor-for-testing/agentic-qe/tree/main/assets/skills/qcsd-refinement-swarm. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Qcsd Refinement Swarm?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Qcsd Refinement Swarm?

As many as you like. As long as a model supports skills, you can use Qcsd Refinement Swarm with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Qcsd Refinement Swarm AI skill free?

Yes. It is published on GitHub by proffesor-for-testing under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

QCSD Refinement Swarm v1.0

Overview

QCSD Phase Positioning

Parameters

ENFORCEMENT RULES - READ FIRST

Step Execution Protocol

Steps

Execution Instructions

Resume Support

Agent Inventory

Key Principle

Bundled files

Frequently asked questions

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