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Multica Test T1 Design

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it235
multica-test-t1-design

T1 test design—AC↔design traceability, case templates and coverage self-check, Confluence master copy, XMind/JIRA after passphrase, Apifox scenarios and e2e planning. Integrates ac-design-trace + test-case-generator-squad.

Overview

Publisherit235
Repositorymultica-best-practices
Skill namemultica-test-t1-design
Stars
199
Forks
29
Bundled files
19
LicenseMIT
Links
Skill sourceRepository
  • 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.

  • 19 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by it235 on GitHub. Read the source before you install it.

Installation

Install the Multica Test T1 Design 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.
    https://github.com/it235/multica-best-practices/tree/main/templates/en_US/skills/tester/multica-test-t1-design
  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/it235/multica-best-practices.git /tmp/multica-best-practices
mkdir -p .claude/skills
cp -r /tmp/multica-best-practices/templates/en_US/skills/tester/multica-test-t1-design .claude/skills/multica-test-t1-design
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Multica Test T1 Design 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 Multica Test T1 Design 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 Multica Test T1 Design 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.

Test Design (T1 Test Design · Content + Landing)

Position

The only T1 skill: traceability → cases → full Confluence → review → (after 👤 passphrase) XMind/JIRA; parallel Apifox + automation planning.
Not for T2/T3—T2 see multica-test-t2-coverage; T3 see -t3 + multica-test-orchestration.

LayerSkillResponsibility
Orchestrationmultica-test-orchestrationCross-phase routing; automation into repo see references/automation-assets-lifecycle.md
T1This skillContent + Confluence + XMind/JIRA after passphrase
Platformmultica-platform-*Read upstream; Apifox writes scenarios
Reviewmultica-review-testReview Confluence; review only, no import

Three-phase delivery: Phase A is Confluence only (no XMind generated); only Phase C after 👤 passphrase does XMind + JIRA. Details in references/generation-workflow.md.

Platform Collaboration

Platform skillUsed for
multica-platform-jirafetch_all → get_issue.py; after passphrase import_to_tracker.py (team-provided, see references/import-contract.md)
multica-platform-confluenceCollect fetch_page; publish t1-cases.md (see § Confluence landing)
multica-platform-figmafetch_all → fetch_file.py
multica-platform-apifoxT1 parallel write interface scenarios
team-knowledge-base (optional)Step 0.5 terms/entry (binary usage; not bundled in this repo, connect per platform used)

Credentials: JIRA_USERNAME / JIRA_PASSWORD preferred → see platform skill + data-sources.md.

references/ Index

FilePurpose
generation-workflow.mdTwo phases, passphrase, test-management platform naming/validation, steps/priority, dedup/merge
executability-and-permissions.mdPermission identification order, dual targets, module naming, post-generation self-check
output-format.mdJSON 1:1, generated_summary, coverage self-check table, Output Delivery
multimodal-content.mdHTML/image Read and fused understanding
agent-instructions.mdCopyable Agent instructions
design-trace.mdAC ↔ design traceability
test-case-template.mdFunctional case fields
coverage-dimensions.mdDimension selection, scale, coverage matrix, XMind
coverage-checklist.mdPre-generation self-check list
web-ui-checkpoints.mdFigma → UI test points
api-testing-guide.mdInterface scenario patterns
data-sources.mdCollection orchestration and credentials

T1 Flow

Step 0 — Read MULTICA.md (automation path)

Issue repo matrix → each repo Read root MULTICA.md §2 (one per split repo; one per repo must be complete). Missing → BLOCKED.

Step 0.5 — Knowledge base (Step 0.5)

Team knowledge-base Q&A script (optional) → --health + Q&A. Binary usage: operational bucket goes into steps, rules bucket is not AC. See generation-workflow.md.

Step 1 — Design traceability

design-trace.md → write traceability table into coverage report.

Step 2 — Collect

bash
python scripts/fetch_all.py --jira-url "http://jira.../browse/PROJ-123" -o ./data

Multimodal: multimodal-content.md.

Step 3–4 — Cases + self-check

By test-case-template.md + generation-workflow.md + output-format.md → local JSON (working file); self-check with coverage-checklist.md. No XMind.

Step 5 — Phase A: Confluence + Apifox parallel

  • Write the full case body to docs/test/<ISSUE-KEY>/t1-cases.md (template see platform-confluence test-t1-template.md)
  • Confluence landing (see § below) → return link
  • API contract ready → multica-platform-apifox supplements scenarios by contract (AI branch; write manifest planning section)
  • → multica-review-test (review Confluence)
  • Delivery note: Status: pending human review (Confluence updated, not XMind / not imported to JIRA)

Step 6 — Phase C: XMind + JIRA (after 👤 explicit passphrase)

  1. generate_xmind.py ← latest JSON (consistent with Confluence)
  2. import_to_tracker.py (team-provided, interface see references/import-contract.md)
  3. Republish Confluence status "imported to JIRA" + case-set link

Confluence Landing (T1 · master copy)

Parent page: JIRA Issue Hub (PRD requirement page) child page.

Local draft: docs/test/<ISSUE-KEY>/t1-cases.md
Baseline: multica-platform-confluence/scripts/templates/test-t1-template.md

Must include each CASE step/expected body, Apifox section, UI e2e plan (path in MULTICA.md §2, not hardcoded). T1/T2 do not generate XMind.

Publish:

bash
python multica-platform-confluence/scripts/publish_design.py \
  <ISSUE-KEY> docs/test/<ISSUE-KEY>/t1-cases.md \
  --append-jira --json

JIRA block: h3. T1 Test Cases (Test Cases T1).

Apifox (T1 parallel)

After contract published: read API contract → multica-platform-apifox supplements scenarios (AI branch) → Confluence records branch/scenario → manifest path in MULTICA.md §2.

Files

text
multica-test-t1-design/
├── SKILL.md
├── config.local.env.example
├── references/
└── scripts/

Why it works

T1 content and scripts are unified; generation-workflow + output-format preserve the original generator's hard rules; platform uniformly collects via REST.

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 Multica Test T1 Design AI skill do?

T1 test design—AC↔design traceability, case templates and coverage self-check, Confluence master copy, XMind/JIRA after passphrase, Apifox scenarios and e2e planning. Integrates ac-design-trace + test-case-generator-squad.

Why use Multica Test T1 Design on TypingMind?

Because you install it once and use it with any model. Multica Test T1 Design 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 Multica Test T1 Design in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/it235/multica-best-practices/tree/main/templates/en_US/skills/tester/multica-test-t1-design. 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 Multica Test T1 Design?

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 Multica Test T1 Design?

As many as you like. As long as a model supports skills, you can use Multica Test T1 Design with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Multica Test T1 Design AI skill free?

Yes. It is published on GitHub by it235 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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