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Plugin Test

Organization
oh-my-dsh
plugin-test

Use when writing or reviewing tests for DeepSeek Harness plugins, external DSH plugin packages, or package changes in the deepseek-harness repository. Select the minimum sufficient levels from unit tests, coverage, real-API end-to-end tests, snapshots, Web tests, real compositions, and built-artifact smoke tests. For Harness version migrations, use this Skill's built-in seven-touchpoint test workflow and validate the exact target-version runtime.

Overview

Publisheroh-my-dsh
Repositorydsh-plugin-upgrade-skill
Skill nameplugin-test
Stars
300
Forks
36
Bundled files
6
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.

  • 6 bundled files

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

  • Open source

    Published by oh-my-dsh on GitHub. Read the source before you install it.

Installation

Install the Plugin Test 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/oh-my-dsh/dsh-plugin-upgrade-skill/tree/main/skills/plugin-test
  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/oh-my-dsh/dsh-plugin-upgrade-skill.git /tmp/dsh-plugin-upgrade-skill
mkdir -p .claude/skills
cp -r /tmp/dsh-plugin-upgrade-skill/skills/plugin-test .claude/skills/plugin-test
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Plugin Test 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 Plugin Test 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 Plugin Test 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 DeepSeek Harness Plugins

Select the smallest set of test levels that proves the change is correct. Do not run the full suite by default or repeat checks that have already passed.

Test Harness Version Migrations

When adapting an existing plugin to a new DSH host version:

  1. Read references/version-migration-testing.md, build a migration ledger for the exact from/to versions, and scan all seven touchpoint classes.
  2. Add a targeted regression test for every applicable breaking or behavior change. A change-level test proves only that migration mapping; it does not prove the whole plugin is valid.
  3. Cold-start the exact target version through the real product entry point and complete a full user turn. Typechecking, config parsing, Loader smoke tests, and mock Contexts cannot replace this runtime proof.
  4. Report unavailable credentials, providers, operating systems, browsers, PTYs, and destructive-migration boundaries honestly. Do not claim comprehensive compatibility when any remain unverified.

The commands and repository paths below use test-level names from the official Harness monorepo. For external plugins, use equivalent scripts and paths from their own repositories. Do not add nonexistent Harness root commands or impose the monorepo layout on them.

Run a Docker Release Smoke Test

For a pre-release external plugin check, read references/docker-release-smoke.md and use the included runner on the packaged artifact. Pin one exact DSH version and a non-latest Node image, install the artifact into an isolated Profile, cold-start the real DSH entry, and add one argv-based functional probe when startup alone does not exercise the changed behavior.

Treat the generated JSON and Markdown as narrow evidence for that artifact and target. Report skipped providers, browsers, operating systems, security checks, and additional versions as unverified; a passing Docker smoke test does not expand its own coverage. Keep generated reports, raw logs, temporary Profiles, and Docker cache outside the Skill directory.

Test Levels

LevelCommandWhat it proves
Unit testspnpm run testRuns vitest cases under each package's tests/** and script tests under repository scripts/**/*.spec.ts. Cover edge cases, error paths, event ordering, concurrency races, and contract regressions. Every registry needs an HMR-safety test: dispose the fiber that contributed the registration and assert that the resource is removed.
Coverage gatepnpm run test:coverageIn the Harness monorepo, every file under packages/*/*/src must reach 100% coverage. In an external plugin, follow the coverage threshold declared by its repository. Coverage proves only that lines executed, not that the published feature actually works.
Real-API end-to-end testspnpm run test:e2eValidates behavior against real provider APIs, including DeepSeek models and provider smoke tests gated by their own credentials such as EXA_API_KEY and PERPLEXITY_API_KEY. Each suite skips itself when its credential is absent so credential-free CI remains green.
Snapshot testspnpm run test:snapshotValidates expected credential-free output, pinning transport contracts and presentation while using persisted logs to fix the assembled backend behavior.
Web browser snapshotspnpm run test:webCompares Chromium replay output with apps/web/tests/snapshots/. This is a required Linux PR gate. CI forces read-only DSH_SNAPSHOT=replay and never writes expected output. Record or refresh snapshots only locally and review every difference.

When provider credentials are available and real-API execution is authorized, run the corresponding end-to-end tests. Do not skip them merely to save inference spend. Credential-free tests prove only that the pipeline is connected; only credentialed runs prove that the Agent can work with a real model. Based on the change, cover file-writing prompts, multi-turn sessions, tool use, and cancellation during streaming output. The highest-value smoke test starts a real example, sends one prompt, and verifies the outcome from outside the Agent. Automatic skipping keeps credential-free CI unblocked. Record every skipped provider boundary, and never request, expose, or persist credentials merely to make tests pass.

Select Levels from the Change Surface

  • Pure logic or internal helpers → run unit tests only.
  • New or modified package source → run the repository's coverage gate. Apply the per-file 100% rule only when the Harness monorepo explicitly defines it.
  • Model-visible behavior such as prompts, tool Schemas, tool output, or Skill directories → add a credential-free snapshot to the owning example suite, then add a real-composition test.
  • Protocol-visible behavior such as ACP, JSON-RPC, or wire transport → add a credential-free snapshot to the owning example suite.
  • User-visible behavior such as CLI transcripts, interactive terminals, or GUI flows → in the Harness monorepo, use apps/cli/tests/snapshots/ or apps/web/tests/snapshots/; in an external plugin, use the product-entry test suite owned by that repository.
  • Provider behavior such as a new adapter or real provider feature → run real-API end-to-end tests when credentials are available and execution is authorized.
  • A plugin that users will actually run → execute a non-unit real-composition test as described below. Never test only a manually assembled ctx.plugin(...).

When Snapshot Tests Are Mandatory

Every nontrivial model-visible, protocol-visible, or user-visible change must add or update a credential-free scenario through a runnable composition owned by the repository. Package tests, end-to-end assertions, test-only mock compositions, and PR descriptions cannot replace the complete assembled record. In the Harness monorepo, ACP snapshots live under examples/<name>/tests/snapshots/, headless JSONL snapshots live under examples/headless-agent, terminal flows live under apps/cli/tests/snapshots/, and browser flows live under apps/web/tests/snapshots/. Use the record or refresh command provided by the exact target checkout and review every generated difference. External plugins should use their own snapshot framework. If they have none, add a minimal composition that runs through the real Loader.

Test the Real Entry Path

  • User-visible plugins need a real-composition test. Start a test-only cordis.yml through the Loader and application or process entry point. Mock only external services or nondeterministic inputs. Assert the model-visible request or log, persisted state, or user-visible output. Do not add test options to published defaults.
  • A guard test is useful only if the regression truly makes it fail. When the exact target contract requires a bundled or composition module without inject to use named exports, add explicit expect('default' in mod).toBe(false) and unwrapExports round-trip assertions. Prove that the test turns red when the regression is introduced and green after the fix. Do not apply this guard to default plugin objects or Service classes supported by the target version. Validate those forms through their actual Loader contract.
  • The "real entry path" means the published artifact. A package bin must run its built entry under native Node to expose issues that tsx may hide, including shutdown races, module resolution, and swallowed load failures. Apply the same rule to runtime entries outside the default index and singleton modules shared across bundle compositions. In the Harness monorepo, keep its built-artifact smoke tests green, such as packages/examples/*/tests/built-bin.e2e.ts and packages/code-runtime/code-runtime-worker/tests/built-lib.e2e.ts when they exist in the target version. In an external plugin, smoke-test its packaged entry. Assert that the process exits nonzero when required configuration is truly missing.
  • In the Harness monorepo, normal tests resolve through the configured source plane, while only explicit built-entry smoke tests consume build artifacts. In an external plugin, follow its resolver but still add one explicit packaged-artifact consumer so source aliases cannot hide a missing export or a second runtime singleton.
  • For subprocess startup in the Harness monorepo, CI and built-test channels must use the shared launcher from the target checkout to run every example or Cordis config subprocess from built lib/. Never hand-write --import tsx for those subprocesses. Protocol and operating-system fixtures that do not load Cordis follow the current Node/TypeScript convention of the target repository. External plugins follow their own launcher but must cover the packaged artifact. Choose src only when source-path resolution itself is the test subject, and document that contract in the test.

Keep Tests Effective

  • Prefer real implementations over mocks. Mock only expensive or nondeterministic boundaries such as LLM adapters, networks, and clocks, while keeping everything downstream real. A handwritten fake proves only that a bridge moved bytes; it does not prove that the published tool delivers its claimed behavior. Tests for bridged tool calls should use a scripted mock model while keeping the tool and executor real.
  • Verify the external world instead of trusting the Agent's report. End-to-end assertions should rerun commands or reread files outside the Agent. Checking only keywords in Agent output lets a cheating Agent pass. Assert that untouched files remain byte-for-byte identical.
  • End-to-end tests must own their resources. Create the Harness inside the test and dispose it in afterEach, including after failure, retry, or timeout. Put shared fixtures in a normal tests/harness.ts, never another *.e2e.ts; importing a test file registers its describe again and duplicates real-API calls.
  • Recovery tests must separate failures before and after each chunk boundary and prove that a failed chunk derives no messages or tool side effects. Cover exhaustion, cancellation, policy composition, persistence, state, wire counts, transport-close idle timeouts, and the production Loader composition.

Commands

In the Harness monorepo, use commands that actually exist in the target checkout, such as pnpm run test, test:coverage, test:e2e, test:snapshot, and test:web. Confirm each script exists instead of assuming a historical command list is still valid. In an external plugin, use its own scripts, package the artifact, install it into an isolated Profile running the exact target version, and execute a cold-start plus core-path smoke test through the product entry. Run the smallest set that covers the changed surface only once. CI proves only the gates it actually defines.

See references/README.md for the reference index.

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 Plugin Test AI skill do?

Use when writing or reviewing tests for DeepSeek Harness plugins, external DSH plugin packages, or package changes in the deepseek-harness repository. Select the minimum sufficient levels from unit tests, coverage, real-API end-to-end tests, snapshots, Web tests, real compositions, and built-artifact smoke tests. For Harness version migrations, use this Skill's built-in seven-touchpoint test workflow and validate the exact target-version runtime.

Why use Plugin Test on TypingMind?

Because you install it once and use it with any model. Plugin Test 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 Plugin Test in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/oh-my-dsh/dsh-plugin-upgrade-skill/tree/main/skills/plugin-test. 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 Plugin Test?

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 Plugin Test?

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

Is the Plugin Test AI skill free?

Yes. It is published on GitHub by oh-my-dsh 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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