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Gradle Run

CommunityPopular
chrisbanes
gradle-run

Use when planning to execute Gradle through `gradle`, `./gradlew`, or a custom `gradlew*` wrapper script, or diagnosing a Gradle build, compact workflow ledger, repeated failure fingerprint, check, test, lint, warning, or failure even when no new Gradle run is appropriate.

Overview

Publisherchrisbanes
Repositoryskills
Skill namegradle-run
Stars
1.1K
Forks
53
Bundled files
4
LicenseApache-2.0
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.

  • 4 bundled files

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

  • Open source

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

Installation

Install the Gradle Run 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/chrisbanes/skills/tree/main/skills/gradle-run
  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/chrisbanes/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/skills/gradle-run .claude/skills/gradle-run
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Gradle Run 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 Gradle Run 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 Gradle Run 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.

Gradle run

Core principle

Treat complete Gradle output as a temporary, sensitive artifact. Every agent-initiated Gradle command runs through the compact-output wrapper; never stream, tee, paste, or reopen a full build log.

Every create, run, and finish invocation is the entire shell command for that tool call. Prefixes, assignments, conditionals, pipes, command chains, and follow-up inspection invalidate lifecycle evidence even when Gradle succeeds.

Procedure

  1. Classify the request. Reuse a current successful result when unchanged source and inputs already answer the question. A focused task that validates another change is incidental; build/check/warning/failure work is a Gradle-centered workflow.

  2. Resolve this skill directory and confirm python3 plus scripts/gradle_run.py. If either is unavailable, stop and report that prerequisite; never run Gradle directly as a fallback.

  3. Create one workflow before its first command and retain its opaque ID:

    sh
    python3 <skill-dir>/scripts/gradle_run.py create

    Run create, each run, and finish as standalone shell commands. Do not combine one with test, variable setup, git, rg, &&, ;, a pipe, or a newline containing another command. If create fails, retry a fresh standalone create before any run. Use the wrapper exclusively; it supplies --console=plain and --no-scan unless console behavior was selected or the user authorized --scan. Add --warning-mode all only for warning discovery or an explicit request. A workflow is busy result is an ownership violation: wait for the owner or correct ownership; do not start or finish concurrently.

  4. For incidental validation, stay in the current agent and run the narrowest task that answers a non-empty verification question:

    sh
    python3 <skill-dir>/scripts/gradle_run.py run \
      --workflow <id> --scope targeted \
      --question "Does :module:test pass after this change?" -- \
      ./gradlew :module:test

    Do not substitute compilation for requested fixture tests. Read only the bounded JSON summary; report both the managed wrapper and nested Gradle task, the question, and its bounded answer.

  5. For Gradle-centered work, keep diagnosis, repository edits, and wrapper execution with the current implementation owner by default. Delegate a bounded read-only investigation only when it offers useful independent analysis. Select and brief an optional helper using the shared selection and handoff reference. Keep one wrapper execution owner at a time; a helper reports evidence and recommendations to that owner. Continue directly when no helper is available.

  6. Reuse actionable summaries, group warnings/failures by fingerprint, and return source/line evidence plus the narrowest next command. Run broad only for an aggregate question that targeted evidence cannot answer. On a repeated primary source/compiler fingerprint, stop rebuilding; inspect the cited source line and nearby declaration, import, or receiver context before revising the diagnosis. Verify each source change with the same wrapper and narrowest applicable task. In the final diagnosis, name the focused inspection as the next action; do not claim a source fix before it. A new question does not permit a blind repeat.

  7. Treat the full log as raw sensitive material even though summaries and ledgers redact common credentials. Never expose it in model-visible context. On interruption, use the wrapper's recorded signal and bounded partial diagnostics; it owns process-group/process-tree cleanup and durable ledger updates. Only logs still represented by the bounded recent-run ledger remain.

  8. Finish after the last requested validation (targeted or broad) passes, or report unresolved fingerprints and why validation cannot continue. In the final response, quote each non-empty --question and give its bounded answer; command history is not a substitute for reported evidence. Then run:

    sh
    python3 <skill-dir>/scripts/gradle_run.py finish --workflow <id>

    Report that finish removed only wrapper-owned logs. A finished known ID is idempotent; an unknown ID or active workflow fails closed and leaves files in place. This skill does not constrain unrelated review, implementation, or subagents.

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 Gradle Run AI skill do?

Use when planning to execute Gradle through `gradle`, `./gradlew`, or a custom `gradlew*` wrapper script, or diagnosing a Gradle build, compact workflow ledger, repeated failure fingerprint, check, test, lint, warning, or failure even when no new Gradle run is appropriate.

Why use Gradle Run on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/chrisbanes/skills/tree/main/skills/gradle-run. 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 Gradle Run?

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 Gradle Run?

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

Is the Gradle Run AI skill free?

Yes. It is published on GitHub by chrisbanes under the Apache-2.0 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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