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1c Ui Regression

Community
comol
1c-ui-regression

Create or maintain reusable UI regression tests for a 1C web client, runnable without an AI agent. Optional: choose when repeatable coverage is useful; ordinary interactive UI checks remain with 1c-tester.

Overview

Publishercomol
Repositoryai_rules_1c
Skill name1c-ui-regression
Stars
496
Forks
112
Bundled files
Instructions only
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 comol on GitHub. Read the source before you install it.

Installation

Install the 1c Ui Regression 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/comol/ai_rules_1c/tree/main/content/skills/1c-ui-regression
  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/comol/ai_rules_1c.git /tmp/ai_rules_1c
mkdir -p .claude/skills
cp -r /tmp/ai_rules_1c/content/skills/1c-ui-regression .claude/skills/1c-ui-regression
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable 1c Ui Regression 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 1c Ui Regression 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 1c Ui Regression 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.

1c-ui-regression — reusable UI tests

Turn a relevant acceptance scenario into a saved, executable test with deterministic assertions. The model decides whether this is worth doing for the current task: recurring workflows, a reproduced UI regression or a requested automated suite are good candidates. Do not create a suite for every UI edit. Prefer extending the project's existing tests over introducing another framework.

Scope and execution

Authoring test files and running a browser are separate operations. The skill can be selected implicitly; selection neither changes UI_TESTING nor authorizes deployment, writes to an infobase or installation of a new service.

  • Read content/rules/dev-standards-env.md for the target and effective UI policy. manual permits a run on an explicit UI-test request; essential and auto permit applicable checks with a configured publication; off prevents execution. Interactive checks in the thin client go through QA MCP (content/skills/1c-qa-testing/SKILL.md); this skill covers saved web-client suites. Do not enable UI tests as a side effect of selecting this skill.
  • Use an authorized dev/test infobase with the current feature loaded. Resolve the main configuration / extension and source-to-target match through content/rules/extension-workspace.md. Missing runtime prerequisites leave execution unverified; useful local authoring may continue.
  • Interactive discovery uses content/rules/ui-testing-tools.md and content/rules/web-client-driving.md. A saved suite uses its verified runner and the applicable TOOL_BROWSER / driver policies. Do not use it to bypass an off or unavailable required provider. A reusable Playwright runner does not require replacing a project's interactive driver.
  • Read connection values from the project's .dev.env through an existing loader or explicit runtime environment mapping. Never copy URLs from another project or embed credentials / saved authentication state in a committed test.

Build the smallest useful suite

  1. Specify the observation. Use the task's acceptance criteria or an existing OpenSpec scenario. Record initial state, user action and expected visible result before coding. Include the negative or boundary case that exposed the regression. No visual effect to assert means choose a business-logic check; do not change the product just to make a test possible.
  2. Choose the runner. Inspect the existing package manifest, lockfile, test configuration and helpers. Reuse them. If none exists and a saved UI suite is useful, create a minimal Playwright Test setup in the target project, merging existing configuration instead of overwriting it. Check the installed version and its official documentation at https://playwright.dev/docs/intro before relying on APIs. Retain a lockfile and document the setup/run command. Do not auto-install services or browsers outside the authorized task.
  3. Establish real controls. Use a current authorized browser observation and, when relevant, content/skills/1c-form-inspect/SKILL.md. Form metadata identifies logical elements; it does not prove their DOM selectors. Prefer observed accessible roles/names, scoped to the intended form/grid. Do not save ephemeral snapshot refs or generated form-instance IDs as reusable locators. If selectors cannot be verified, report that limit instead of emitting a runnable-looking stub or a passing empty test.
  4. Write independent cases. Save tests in the project's established directory (otherwise tests/ui/). Wait for the relevant UI state, not a fixed sleep. Follow the verified input behavior from web-client-driving.md, then assert the value retained after leaving the field. Reuse existing login and data helpers. Each case owns its data or starts from a documented stable fixture; test order must not carry business state between cases.
  5. Make failures machine-detectable. Use runner assertions and retain its nonzero exit code. Missing controls, setup failures, timeouts and zero executed cases cannot produce a pass. Emit the runner's JSON/JUnit report, or bounded JSONL observations alongside it, with case ID and expected/actual values. Capture permitted failure evidence without secrets. Logs complement assertions; an AI reading a log is not the test oracle.
  6. Run when allowed. Execute the focused suite against the verified target. If test actions write/post/delete, establish the authorized test-data and cleanup scope first; cleanup can remove only artifacts owned by this run. Deployment, if needed and authorized, stays with /update1cbase or /deploy-and-test, never a hidden fixture hook. Preserve failure status even if cleanup also fails.

Completion

The deliverable names the test files, covered acceptance criteria, setup/run command, required target and actual result. Distinguish authored, executed: passed, executed: failed and blocked/unverified. A suite that only loads or lists its cases has not verified the feature. State the checked source / deployed state and report path for a live run.

Fix an in-scope defect through normal project gates. Re-run affected cases only after a relevant source, test or environment change; do not weaken an expected result to make the test green. Repeated deploy/fix cycles remain the explicitly requested /test-fix-loop workflow.

For reusable server-side checks use content/skills/1c-business-tests/SKILL.md; neither skill replaces applicable static validators or the existing focused Gate 3a check.

Frequently asked questions

What does the 1c Ui Regression AI skill do?

Create or maintain reusable UI regression tests for a 1C web client, runnable without an AI agent. Optional: choose when repeatable coverage is useful; ordinary interactive UI checks remain with 1c-tester.

Why use 1c Ui Regression on TypingMind?

Because you install it once and use it with any model. 1c Ui Regression 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 1c Ui Regression in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/comol/ai_rules_1c/tree/main/content/skills/1c-ui-regression. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use 1c Ui Regression?

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 1c Ui Regression?

As many as you like. As long as a model supports skills, you can use 1c Ui Regression with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the 1c Ui Regression AI skill free?

It is published on GitHub by comol. Check the repository for licensing terms. 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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