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Qcsd Development Swarm

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proffesor-for-testing
qcsd-development-swarm

Use when monitoring in-sprint code quality with TDD adherence checks, complexity analysis, coverage gap detection, or defect prediction in the QCSD Development phase.

Overview

Publisherproffesor-for-testing
Repositoryagentic-qe
Skill nameqcsd-development-swarm
Stars
480
Forks
92
Bundled files
12
LicenseMIT
Links
  • 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 Development Swarm AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

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.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

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-development-swarm .claude/skills/qcsd-development-swarm
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Qcsd Development 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 Development 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 Development 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 Development Swarm v1.0

Shift-left quality engineering swarm for in-sprint code quality assurance.


Overview

The Development Swarm takes refined stories (that passed Refinement) and validates code quality during sprint execution. Where the Ideation Swarm asks "Should we build this?" and the Refinement Swarm asks "How should we test this?", the Development Swarm asks "Is the code quality sufficient to ship?"

QCSD Phase Positioning

PhaseSwarmDecisionWhen
Ideationqcsd-ideation-swarmGO / CONDITIONAL / NO-GOPI/Sprint Planning
Refinementqcsd-refinement-swarmREADY / CONDITIONAL / NOT-READYSprint Refinement
Developmentqcsd-development-swarmSHIP / CONDITIONAL / HOLDDuring Sprint
Verificationqcsd-cicd-swarmRELEASE / REMEDIATE / BLOCKPre-Release / CI-CD
Productionqcsd-production-swarmHEALTHY / DEGRADED / CRITICALPost-Release

Parameters

  • SOURCE_PATH: Source code directory to analyze (required, e.g., src/auth/)
  • TEST_PATH: Test directory for coverage analysis (optional, e.g., tests/auth/)
  • OUTPUT_FOLDER: Where to save reports (default: ${PROJECT_ROOT}/Agentic QCSD/development/)

ENFORCEMENT RULES - READ FIRST

RuleEnforcement
E1You MUST spawn ALL THREE core agents (qe-tdd-specialist, qe-code-complexity, qe-coverage-specialist) in Step 2. No exceptions.
E2You MUST put all parallel Task calls in a SINGLE message.
E3You MUST STOP and WAIT after each batch. No proceeding early.
E4You MUST spawn conditional agents if flags are TRUE. No skipping.
E5You MUST apply SHIP/CONDITIONAL/HOLD logic exactly as specified in Step 5.
E6You MUST generate the full report structure. No abbreviated versions.
E7Each agent MUST read its reference files before analysis.
E8You MUST apply qe-defect-predictor analysis on ALL code changes in Step 8. Always.
E9You MUST execute Step 7 learning persistence. No skipping.

PROHIBITED BEHAVIORS:

  • Summarizing instead of spawning agents
  • Skipping agents "for brevity"
  • Proceeding before background tasks complete
  • Providing your own analysis instead of spawning specialists
  • Omitting report sections or using placeholder text

Step Execution Protocol

This skill uses a micro-file step architecture. Each step is a self-contained file loaded one at a time to avoid "lost in the middle" context degradation.

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

Steps

  1. Flag Detection -- steps/01-flag-detection.md -- Scan source code and tests, detect all 6 flags
  2. Core Agents -- steps/02-core-agents.md -- Spawn qe-tdd-specialist, qe-code-complexity, qe-coverage-specialist in parallel
  3. Batch 1 Results -- steps/03-batch1-results.md -- Wait for core agents, extract all metrics
  4. Conditional Agents -- steps/04-conditional-agents.md -- Spawn flagged conditional agents in parallel
  5. Decision Synthesis -- steps/05-decision-synthesis.md -- Apply SHIP/CONDITIONAL/HOLD logic
  6. Report Generation -- steps/06-report-generation.md -- Generate executive summary and full report
  7. Learning Persistence -- steps/07-learning-persistence.md -- Store findings to memory, save persistence record
  8. Defect Predictor -- steps/08-defect-predictor.md -- Run qe-defect-predictor analysis on all code changes
  9. Final Output -- steps/09-final-output.md -- Display completion summary with all scores

Execution Instructions

  1. Use the Read tool to load the current step file (e.g., Read({ file_path: ".claude/skills/qcsd-development-swarm/steps/01-flag-detection.md" }))
  2. Execute the step's instructions completely
  3. Verify all success criteria are met before proceeding
  4. Pass the step's output as context to the next step
  5. If a step fails, halt and report the failure point -- do not skip ahead

Resume Support

To resume from a specific step: specify --from-step N and the orchestrator will skip to step N. Ensure you have the required prerequisite data from prior steps.


Agent Inventory

AgentTypeDomainBatch
qe-tdd-specialistCore (always)test-generation1
qe-code-complexityCore (always)code-intelligence1
qe-coverage-specialistCore (always)coverage-analysis1
qe-security-scannerConditional (HAS_SECURITY_CODE)security-compliance2
qe-performance-testerConditional (HAS_PERFORMANCE_CODE)chaos-resilience2
qe-mutation-testerConditional (HAS_CRITICAL_CODE)test-generation2
qe-message-broker-testerConditional (HAS_MIDDLEWARE)enterprise-integration2
qe-sap-idoc-testerConditional (HAS_SAP_INTEGRATION)enterprise-integration2
qe-sod-analyzerConditional (HAS_AUTHORIZATION)enterprise-integration2
qe-defect-predictorAnalysis (always)defect-intelligence3

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


Quality Gate Thresholds

MetricSHIPCONDITIONALHOLD
TDD Adherence>= 80%60 - 79%< 60%
Code ComplexityAvg <= 10Avg 11-15Avg > 15
Test Coverage>= 80%60 - 79%< 60%
Mutation Score>= 70%50 - 69%< 50%
Security IssuesNo HIGH/CRITICALMEDIUM onlyHIGH/CRITICAL found

Report Filename Mapping

AgentReport FilenameStep
qe-tdd-specialist02-tdd-analysis.md2
qe-code-complexity03-complexity-analysis.md2
qe-coverage-specialist04-coverage-analysis.md2
qe-security-scanner05-security-scan.md4
qe-performance-tester06-performance-analysis.md4
qe-mutation-tester07-mutation-testing.md4
qe-message-broker-tester08-middleware-health.md4
qe-sap-idoc-tester09-sap-integration.md4
qe-sod-analyzer10-sod-compliance.md4
Learning Persistence11-learning-persistence.json7
qe-defect-predictor12-defect-prediction.md8
Synthesis01-executive-summary.md6

Execution Model Options

ModelWhen to UseAgent Spawn
Workflow (PRIMARY, ADR-102)Harness with the Workflow toolWorkflow({ name: "qcsd-development-review", args: { sourcePath, testPath } })
Task Tool (fallback)Claude Code sessions without Workflow supportTask({ subagent_type, run_in_background: true })
MCP ToolsMCP server availablefleet_init({}) / task_submit({})
CLITerminal/scriptsswarm init / agent spawn

Workflow execution (ADR-102)

.claude/workflows/qcsd-development-review.js runs the review as a deterministic pipeline: one finder per quality dimension (TDD adherence, complexity, coverage gaps — args.dimensions selects a subset) → 3 blind adversarial refuters per finding (Loki-mode, ADR-074: refuters see only the bare claim + evidence, never the finder's confidence or each other; uncertainty defaults to refuted) → deterministic synthesis into finding-verdict@1 envelopes (ADR-103, schemas/finding-verdict.schema.json). A finding survives only if fewer than ⌈N/2⌉ refuters kill it. The final report contains ONLY confirmed findings; killed findings are retained under killed with their refutations for audit.

Args: sourcePath (required), testPath, dimensions (subset of tdd-adherence|complexity|coverage-gaps), maxFindings per dimension (default 5).

When the Workflow tool is unavailable, fall back to the Task-tool protocol below — the report format and gates are identical, minus the adversarial verification stage (note this in the report header as verification: none).


Key Principle

Code quality is measured by evidence, not intentions. This swarm provides in-sprint quality assessment to ensure code meets engineering standards before entering the CI/CD pipeline.

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 Development Swarm AI skill do?

Use when monitoring in-sprint code quality with TDD adherence checks, complexity analysis, coverage gap detection, or defect prediction in the QCSD Development phase.

Why use Qcsd Development Swarm on TypingMind?

Because you install it once and use it with any model. Qcsd Development 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 Development 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-development-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 Development 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 Development Swarm?

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

Is the Qcsd Development 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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