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Recipe Add Integration Tests

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shinpr
recipe-add-integration-tests

Add integration/E2E tests to existing codebase using Design Docs

Overview

Publishershinpr
Repositoryclaude-code-workflows
Skill namerecipe-add-integration-tests
Stars
682
Forks
103
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by shinpr on GitHub. Read the source before you install it.

Installation

Install the Recipe Add Integration Tests 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/shinpr/claude-code-workflows.git /tmp/claude-code-workflows
mkdir -p .claude/skills
cp -r /tmp/claude-code-workflows/dev-workflows-fullstack/skills/recipe-add-integration-tests .claude/skills/recipe-add-integration-tests
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Recipe Add Integration Tests 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 Recipe Add Integration Tests 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 Recipe Add Integration Tests 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.

Explicit User Instruction: The user explicitly instructs and authorizes every subagent call named in this recipe. Execute each applicable call when its prerequisites are met.

Execute Skill: llm-friendly-context before writing Agent prompts, handoffs, or generated artifacts. Execute Skill: subagents-orchestration-guide before making workflow decisions, invoking agents, or resolving findings.

Context: Test addition workflow for existing implementations (backend, frontend, or fullstack)

Orchestrator Definition

Core Identity: "I am an orchestrator."

Local authority gate: Make this recipe's workflow decisions and validate each returned result directly; delegate semantic deliverable production to the named specialist.

Review Resolution Gate [MANDATORY]: Resolve every actionable deliverable-review finding through subagents-orchestration-guide Review Resolution before correction or progression. Before the first finding disposition, read references/review-resolution.md from the loaded subagents-orchestration-guide skill.

Execution Gate: Complete Steps 1-7 in order for each generated layer. Advance only through the current step's stated output or response gate; skip work only when its stated condition is false. Report completion after every layer has completed its review, quality, commit, and retained-limitation retry.

Why Delegate: Orchestrator's context is shared across all steps. Direct implementation consumes context needed for review and quality check phases. Subagents work in isolated context.

Execution Method:

  • Skeleton generation → delegate to acceptance-test-generator
  • Test implementation → delegate to task-executor
  • Test review → delegate to integration-test-reviewer
  • Quality checks → delegate to quality-fixer

At each Agent invocation below, build the prompt as a mechanical extraction: copy the named source values into the exact fields, apply only the declared serialization, then invoke immediately.

Document paths: $ARGUMENTS

Prerequisites

  • At least one Design Doc must exist (created manually or via reverse-engineer)
  • Existing implementation to test

Execution Flow

Step 1: Discover and Validate Documents

Confirm $ARGUMENTS names at least one existing document path; report and end when it is empty or every path is unresolvable. Then discover the remaining Design Docs, UI Specs, and PRDs under docs/design/, docs/ui-spec/, and docs/prd/.

Classify discovered documents by filename:

  • Filename contains backendDesign Doc (backend)
  • Filename contains frontendDesign Doc (frontend)
  • Located in docs/ui-spec/UI Spec (optional)
  • Located in docs/prd/approved PRD / confirmed requirement context (optional; prefer the path named by the Design Doc)
  • None of the above → treat as single-layer Design Doc

Step 2: Skeleton Generation

Invoke acceptance-test-generator using Agent tool:

  • subagent_type: "dev-workflows-fullstack:acceptance-test-generator"
  • description: "Generate test skeletons"
  • design_docs: Existing backend, frontend, or single-layer Design Doc paths from Step 1
  • ui_spec: Existing UI Spec path when present
  • confirmed_requirement_context: Approved PRD path or unchanged Design Doc Requirement Convergence record

Follow subagents-orchestration-guide HC-06 for value_input_required and its unknown-value continuation before Step 3.

Expected output: generatedFiles containing integration and e2e paths

Step 3: Test Implementation

For each layer with generated skeletons, record the current HEAD as diffBase, then invoke the layer's task-executor:

  • Backend or single-layer → subagent_type: "dev-workflows-fullstack:task-executor"
  • Frontend → subagent_type: "dev-workflows-fullstack:task-executor-frontend"
  • description: "Implement integration tests"
  • direct_scope: Implement every test defined by the layer-specific generated skeletons
  • governing_sources: Layer-specific Design Doc, applicable UI Spec, and generated skeleton paths
  • target_paths: Generated test paths plus the existing setup or fixture paths explicitly identified before invocation
  • observable_verification: Execute the implemented tests and verify every skeleton claim at its declared boundary

Execute one layer at a time through Steps 3→4→5→6→7 before starting the next.

Expected output: status, testsAdded, mutationEvidence

Apply this response gate after every task-executor invocation in Steps 3 and 5:

  • At least one changed integration/E2E test file and its implementation evidence are confirmed from the response and repository state → Proceed to Step 4
  • Required implementation remains incomplete → Apply Specialist Result Acceptance and continue Step 3 while repository evidence supplies an advancing action
  • Other result variations → Apply subagents-orchestration-guide Specialist Result Acceptance

Step 4: Test Review

Invoke integration-test-reviewer using Agent tool:

  • subagent_type: "dev-workflows-fullstack:integration-test-reviewer"
  • description: "Review test quality"
  • prompt: "Review test quality. changedTestFiles: [confirmed changed integration/E2E test paths]. diffBase: [revision recorded before Step 3]. skeletonFiles: [layer-specific paths from Step 2 generatedFiles]. mutationEvidence: [Step 3 mutationEvidence]."

Expected output: status (pass/needs_revision/blocked), testFiles, reviewBasis, qualityIssues; correction re-review also returns prior_feedback_reconciliation

Step 5: Apply Review Fixes

Check Step 4 result:

  • status: pass → Mark complete, proceed to Step 6
  • status: blocked → Apply Specialist Result Acceptance
  • status: needs_revision → Pass Step 4 qualityIssues unchanged into the Review Resolution Gate; invoke task-executor for rerouted corrections, return to Step 4, and derive convergence from prior_feedback_reconciliation

Invoke the same layer's task-executor:

  • description: "Fix review findings"
  • Reuse Step 3 direct_scope, governing_sources, target_paths, and observable_verification
  • correction_findings: Complete reviewer finding objects verbatim, with only their orchestrator dispositions added

Step 6: Quality Check

Invoke quality-fixer for the current layer:

  • Backend or single-layer → subagent_type: "dev-workflows-fullstack:quality-fixer"
  • Frontend → subagent_type: "dev-workflows-fullstack:quality-fixer-frontend"
  • description: "Final quality assurance"
  • Copy Step 3 direct_scope, governing_sources, and observable_verification inputs unchanged.
  • Include the latest executor's correction_findings input unchanged when supplied.
  • Pass the latest executor's mutationEvidence.

Expected output: status (pass, stub_detected, verification_incomplete, or blocked)

Check quality-fixer response:

  • stub_detected → Return to Step 3 with the quality-fixer's incompleteImplementations array unchanged as the canonical incompleteImplementations field, then re-execute Steps 3→4→5→6
  • blocked → Apply Specialist Result Acceptance
  • verification_incomplete → Retain the complete result for one final retry and proceed to Step 7
  • pass → Proceed to Step 7

Step 7: Commit

On pass or verification_incomplete from quality-fixer:

  • Apply subagents-orchestration-guide Commit Boundary Check, then commit test files using Bash with message format: "test: add [layer] integration tests for [feature name]". Append its verification trailers for verification_incomplete.

After every layer has a clean commit boundary, retry each retained verification limitation once with the same layer quality-fixer inputs. When the retry returns pass, remove that limitation from retained state. Route newly discovered incomplete implementation through Steps 3→6, and retain a repeated limitation for the completion report while continuing the workflow.

In the completion report, list each repeated verification limitation and each declined actionable finding with its ID, governing reason, and evidence when any occurred.

Frequently asked questions

What does the Recipe Add Integration Tests AI skill do?

Add integration/E2E tests to existing codebase using Design Docs

Why use Recipe Add Integration Tests on TypingMind?

Because you install it once and use it with any model. Recipe Add Integration Tests 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 Recipe Add Integration Tests in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/shinpr/claude-code-workflows/tree/main/dev-workflows-fullstack/skills/recipe-add-integration-tests. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Recipe Add Integration Tests?

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 Recipe Add Integration Tests?

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

Is the Recipe Add Integration Tests AI skill free?

Yes. It is published on GitHub by shinpr 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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