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Javdb Cli Test

Community
FlanChanXwO
javdb-cli-test

Select and run javdb-cli TDD, Go, SDK, CLI, pipeline, media, documentation, and release-tool checks. Use to reproduce failures, validate source or documentation changes, and report readiness while keeping routine verification offline and credential-free.

Overview

PublisherFlanChanXwO
Repositoryjavdb-cli
Skill namejavdb-cli-test
Stars
76
Forks
15
Bundled files
1
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.

  • 1 bundled files

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

  • Open source

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

Installation

Install the Javdb Cli 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/FlanChanXwO/javdb-cli/tree/main/.agents/skills/javdb-cli-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/FlanChanXwO/javdb-cli.git /tmp/javdb-cli
mkdir -p .claude/skills
cp -r /tmp/javdb-cli/.agents/skills/javdb-cli-test .claude/skills/javdb-cli-test
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Javdb Cli 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 Javdb Cli 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 Javdb Cli 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 javdb-cli

Establish Red and scope

Inspect the diff, actual owner, and existing tests. Use the Go toolchain declared in go.mod; run from the repository root. Missing tools/dependencies are explicit environment blockers, not permission to install or weaken a gate.

For a feature or fix, select an existing test that exposes the missing behavior, or extend it only where coverage is missing, and confirm the expected failure before implementation. Build/import/fixture setup failures do not count as Red. Make the smallest implementation pass, then refactor with regression checks. Pure restructuring reuses passing characterization before and after; any behavior correction needs failing evidence. Obtain an explicit exception when an applicable Red requirement cannot be met.

Use the existing Go test framework, httptest, temporary directories, and synthetic auth/HTTP fixtures. Test real CLI/SDK behavior rather than restating internal logic. Keep tests near their owner, name them by behavior, and table-drive equivalent cases when it improves clarity. Use an external test package where the public surface suffices; use same-package tests for a justified private seam rather than exporting internals solely for tests. Follow established same-stem/platform file conventions; do not create task-numbered regression files.

Decide whether test code must change

Before adding a test, name the observable contract or credible failure that existing tests do not protect. Inspect their assertions, not just their names or coverage percentage. Prefer running an existing test, then extending a relevant case/table, then adding a new test only for an uncovered scenario. Record the choice briefly in the existing handoff, not a new test-plan file.

  • Test behavior, not the existence of each function or file. A getter, forwarding wrapper, simple field projection, or newly extracted helper needs no separate test when its behavior is already protected at the calling boundary. Short security, parsing, or persistence code can still carry independent risk and require coverage.
  • For pure moves, renames, or extraction, run existing characterization before and after. Do not invent a behavior change, alter an assertion to manufacture Red, or duplicate a test solely because a helper appeared.
  • Test at the narrowest stable boundary that catches the defect. Extra CLI/SDK/adapter layers earn tests for distinct serialization, validation, permissions, or failure semantics, not repeated assertions of the same mapping. Mock real external boundaries rather than recreating the implementation in a test.
  • Keep fixtures and assertions direct. Use table-driven cases only when they share setup and semantics; avoid a generic fixture builder, mock hierarchy, assertion DSL, or new framework for a small case. Do not test standard-library behavior instead of the project's own contract.
  • Ordinary prose comments, headings, and internal code layout do not need exact-text or AST/regex tests. Machine-consumed directives, generated contracts, parsers, and security boundaries may need targeted existing checks; distinguish those from style preferences.
  • Remove or consolidate tests only within the authorized scope and after identifying the same contract protected by retained checks. Preserve regression inputs and run the retained checks; fewer lines alone do not justify deleting evidence.

Finish adding tests when changed contracts and credible failure paths are protected. There is no per-function, per-file, test-count, or invented coverage-percentage target. TDD governs the order of evidence, not the quantity of new test code; mandatory repository gates still apply.

Choose verification

Start with go test ./path/to/owner -run '^TestName$' -count=1, substituting the inspected package/test. Expand as needed:

ChangeRelevant checks
Documentation or agent/product instructionsCheck English content, links, frontmatter, command examples, and git diff --check; run affected existing script/tool contracts
Local Go implementationFocused and related package tests plus scoped go vet; sh scripts/build.sh when executable/build behavior is affected
Shared contract, public SDK, core behavior, or release candidatego test ./... -count=1, go vet ./..., and sh scripts/build.sh, in addition to focused regression
Concurrent batch, auth/state, files, or core contractsAdd go test -race ./... -count=1; cover cancellation, partial failures, cleanup, and no-overwrite behavior
Metadata and PR verificationgo test ./tools/prmeta ./tools/verification -count=1
Build/platform/releasego test ./tools/release ./tools/platformmatrix -count=1, go test ./scripts/... -count=1, and affected scripts/test-*.sh contracts
Image/HLS/TS/MP4javdb-cli-media, using complete and malformed synthetic fixtures

Format affected Go with gofmt and inspect its diff. Follow .pre-commit-config.yaml; run installed hooks when applicable and report unavailable tooling rather than installing it silently. JavDB is a pure-Go build; no Rust or Pixiv staticlib step belongs here. The local binary is build/javdb (.exe on Windows).

CI scope is determined by trusted base-branch .github/ci-change-scope.gitignore through scripts/classify-change-scope.sh. ci.yml owns Quality; trusted pr-smoke-gates.yml classifies smoke scope and dispatches base-ref Platform/Container workers. Required Quality/Platform/Container gates are real Actions jobs and use a native skipped conclusion when their scope is not required. Ordinary branch/main pushes do not run CI; stable tag pushes run Release without a duplicate Quality workflow. Run the required checks for the actual classifier result. A fixture for workflow permissions is not proof of GitHub environment/secret settings or native platform execution.

Contracts worth testing when touched

  • Output: TTY text, default piped records, explicit legacy JSON, explicit javdb.pipeline/v1 NDJSON, stable entity/list IDs, ordered per-item errors, and aggregate nonzero status. Keep the separate asset TYPE<TAB>URL stream intact.
  • Identity/auth: exact number resolution before writes, known-ID bypass, optional-auth anonymous behavior, configured auto-relogin, and redacted errors. Use fake accounts only.
  • State: CLI/environment/file/default precedence, fixed versus automatic host routing, private file publication, malformed cache behavior, and preservation of existing data.
  • Updates: signature/origin/tag/platform binding, archive and extracted-binary hashes, no candidate execution, and preservation of the previous executable on failure.

Offline versus live

Normal tests use fixtures and must not inspect ~/.javdb-cli/auth.json or real account state. A real API request may refresh credentials, update route/tag caches, upload an image, or mutate remote marks. Run it only with explicit authorization, exact scope, and protected credentials. Do not reuse a production account because local tests lack a fixture, and do not mistake an offline pass for upstream acceptance.

For a failed gate, isolate the causal failure and run relevant safely independent phases that fail-fast hid. Rerun only after a relevant change or for a stated flaky/infrastructure hypothesis. Separate task regressions from baseline/environment failures; never edit a test to hide an unexplained mismatch.

Finish with exact commands and outcomes, tested commit, unrun/live/platform checks, and risks. Required blocked evidence remains blocked.

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 Javdb Cli Test AI skill do?

Select and run javdb-cli TDD, Go, SDK, CLI, pipeline, media, documentation, and release-tool checks. Use to reproduce failures, validate source or documentation changes, and report readiness while keeping routine verification offline and credential-free.

Why use Javdb Cli Test on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/FlanChanXwO/javdb-cli/tree/main/.agents/skills/javdb-cli-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 Javdb Cli 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 Javdb Cli Test?

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

Is the Javdb Cli Test AI skill free?

Yes. It is published on GitHub by FlanChanXwO 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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