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Run Smoke Tests

Community
tamdogood
run-smoke-tests

Inspect an unfamiliar repository, interpret a broad Markdown user journey at runtime, operate the real product through its supported web, API, CLI, desktop, or mobile surface, and produce an auditable pass, fail, or blocked judgment with screenshots, logs, recordings, and a step timeline when available. Use when asked to run smoke tests, validate an end-to-end user journey, dogfood a product, test a release candidate, or execute a non-deterministic workflow that may include authentication or human checkpoints. Do not use as a substitute for deterministic unit, integration, or scenario tests.

Overview

Publishertamdogood
Repositorybuilder-essential-skills
Skill namerun-smoke-tests
Stars
225
Forks
8
Bundled files
5
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.

  • 5 bundled files

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

  • Open source

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

Installation

Install the Run Smoke 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.
    https://github.com/tamdogood/builder-essential-skills/tree/main/skills/run-smoke-tests
  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/tamdogood/builder-essential-skills.git /tmp/builder-essential-skills
mkdir -p .claude/skills
cp -r /tmp/builder-essential-skills/skills/run-smoke-tests .claude/skills/run-smoke-tests
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Run Smoke 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 Run Smoke 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 Run Smoke 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.

Run Smoke Tests

Execute a broad user journey against a real build and leave enough evidence for a person to audit the judgment. Understand the host project before starting the product or touching its state.

Operating contract

  • Treat the Markdown as runtime instructions, not source to compile into code.
  • Test through a supported user-facing surface: web UI, API, CLI, desktop, mobile, or a documented combination.
  • Separate execution from repair. Do not edit the product, tests, or journey during a run. Finish the report before starting any requested fix.
  • Default to local, test, preview, or staging environments. Never mutate production, spend money, send real invitations, or change real user data without explicit authorization.
  • Make every pass or fail claim traceable to captured evidence.
  • Keep the first failed run. Do not erase evidence by retrying until green.

Workflow

1. Pass the repository-understanding gate

Read the nearest repository instructions, product README, runbook, contribution guide, manifests, launch scripts, existing smoke or end-to-end tests, and the smallest relevant product documentation. Search for auth setup, seed data, test accounts, feature flags, observability, artifact conventions, and cleanup commands. Trace the journey across the relevant UI, service, state, and external-system boundaries so failures can be attributed to the right part of the product.

Before launching anything, be able to state:

text
product and user surface:
authoritative journey source:
relevant architecture and system boundaries:
build and start commands:
target environment and data boundary:
interaction driver or available tools:
authentication and human checkpoints:
evidence capture capabilities:
safe cleanup path:
known abort conditions:

Do not guess missing commands, URLs, credentials, or permissions. If a missing item blocks safe execution, report it as a preflight blocker.

2. Validate the journey contract

Read references/smoke-format.md. Normalize supplied prose without silently adding product behavior.

Confirm that the journey:

  • represents one coherent user goal;
  • names its environment and allowed mutations;
  • defines critical and noncritical oracles;
  • identifies authentication, payment, security, or destructive checkpoints;
  • has a cleanup or quarantine plan;
  • names conditions that require an immediate abort.

Move small deterministic behaviors into scenario tests. Keep smoke coverage for the larger path and the seams between systems.

3. Preflight without changing product behavior

Build or launch the product with documented commands. Confirm health, test-data availability, driver connectivity, recording support, and enough storage for artifacts.

Prefer the repository's existing artifact directory. Otherwise use .context/smoke-runs/ when .context already exists; if it does not, create a temporary directory and report its absolute path. Do not add generated evidence to version control unless the user asks.

Create a run manifest containing:

text
journey ID and source path
commit or build identifier
environment and base URL or executable
start time and executor
human checkpoints
recording and log sources

Abort before step one when the build is unhealthy, the environment is wrong, the account or data boundary is unsafe, or required evidence capture is unavailable for a critical oracle.

4. Interpret and execute the Markdown

Follow the journey in order through the real product surface. For each step, record:

text
timestamp
instruction
action taken
observable result
evidence path or log reference
provisional judgment
deviation, latency, or uncertainty

Use normal user affordances. Do not call internal APIs to bypass UI steps unless the journey explicitly tests an API or uses setup APIs only for isolated fixtures.

Pause at a declared human checkpoint. State the exact action needed and resume from the same state after the human completes it. Never request that a user paste secrets into chat.

If the driver supports video, record the complete run and generate subtitles or a timestamped transcript from the step timeline. Otherwise capture screenshots, terminal output, structured logs, and state snapshots. Do not claim to have a video when the environment cannot produce one.

5. Judge once, retry carefully

Assign one primary result:

  • PASS: every critical oracle was observed and no abort condition occurred;
  • FAIL: a critical oracle was violated or an unexpected product error prevented the goal;
  • BLOCKED: an external prerequisite, permission, environment, or human checkpoint prevented a valid judgment.

Add FLAKY as a flag when the same build and inputs produce inconsistent results.

Do not convert missing evidence into a pass. A noncritical issue may preserve a pass only when the report calls it out separately.

Retry at most once, and only when evidence points to an environmental or driver failure rather than a product failure. Preserve both runs and explain why the retry was allowed.

6. Clean up and hand off

Run only the cleanup authorized by the journey. If cleanup fails, quarantine the test data, record identifiers, and report the residue. Do not hide a cleanup failure behind a passing product judgment.

Write a report with:

  1. result, confidence, build, environment, and duration;
  2. a timestamped step table;
  3. critical oracle results with evidence links;
  4. human checkpoints and deviations;
  5. console, network, log, screenshot, and recording locations;
  6. cleanup result and residual data;
  7. candidate deterministic tests exposed by the run.

The final user message must distinguish product failures, test-environment failures, and uncertain judgments.

Demonstration examples

Use the examples to demonstrate runtime interpretation. Replace every command, URL, account, and expected result with evidence from the host repository.

Failure handling

  • Product cannot start: capture build and launch output, mark BLOCKED, and do not improvise a different environment.
  • Authentication unavailable: pause at the checkpoint or mark BLOCKED; never bypass it with unapproved credentials.
  • Product behavior diverges from the Markdown: preserve evidence and mark FAIL unless the behavior source is genuinely contradictory.
  • Evidence capture fails mid-run: abort when it affects a critical oracle; otherwise continue with the limitation visible.
  • A defect is found: finish the report first. Diagnose or fix only under a separate user request.

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 Run Smoke Tests AI skill do?

Inspect an unfamiliar repository, interpret a broad Markdown user journey at runtime, operate the real product through its supported web, API, CLI, desktop, or mobile surface, and produce an auditable pass, fail, or blocked judgment with screenshots, logs, recordings, and a step timeline when available. Use when asked to run smoke tests, validate an end-to-end user journey, dogfood a product, test a release candidate, or execute a non-deterministic workflow that may include authentication or human checkpoints. Do not use as a substitute for deterministic unit, integration, or scenario tests.

Why use Run Smoke Tests on TypingMind?

Because you install it once and use it with any model. Run Smoke 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 Run Smoke Tests in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/tamdogood/builder-essential-skills/tree/main/skills/run-smoke-tests. 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 Run Smoke 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 Run Smoke Tests?

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

Is the Run Smoke Tests AI skill free?

Yes. It is published on GitHub by tamdogood 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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