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Template-agnostic Orchestrator Skill for generating and executing exhaustive testing suites for any migration template.
Install Meta Test Orchestrator
$ npx skills add GoogleCloudPlatform/DataflowTemplates --skill meta-test-orchestrator -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install GoogleCloudPlatform/DataflowTemplates meta-test-orchestrator --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/GoogleCloudPlatform/DataflowTemplates.git skills-src && mkdir -p .claude/skills && cp -r skills-src/v2/spanner-common/.agents/skills/meta-test-orchestrator .claude/skills/meta-test-orchestrator && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "meta-test-orchestrator" agent skill from https://github.com/GoogleCloudPlatform/DataflowTemplates/tree/main/v2/spanner-common/.agents/skills/meta-test-orchestrator into .claude/skills/meta-test-orchestrator/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "meta-test-orchestrator", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/GoogleCloudPlatform/DataflowTemplates/tree/main/v2/spanner-common/.agents/skills/meta-test-orchestratorType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add GoogleCloudPlatform/DataflowTemplates --skill meta-test-orchestrator -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install GoogleCloudPlatform/DataflowTemplates meta-test-orchestrator --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/GoogleCloudPlatform/DataflowTemplates.git skills-src && mkdir -p .agents/skills && cp -r skills-src/v2/spanner-common/.agents/skills/meta-test-orchestrator .agents/skills/meta-test-orchestrator && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "meta-test-orchestrator" agent skill from https://github.com/GoogleCloudPlatform/DataflowTemplates/tree/main/v2/spanner-common/.agents/skills/meta-test-orchestrator into .agents/skills/meta-test-orchestrator/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "meta-test-orchestrator", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add GoogleCloudPlatform/DataflowTemplates --skill meta-test-orchestrator -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install GoogleCloudPlatform/DataflowTemplates meta-test-orchestrator --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/GoogleCloudPlatform/DataflowTemplates.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/v2/spanner-common/.agents/skills/meta-test-orchestrator .cursor/skills/meta-test-orchestrator && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "meta-test-orchestrator" agent skill from https://github.com/GoogleCloudPlatform/DataflowTemplates/tree/main/v2/spanner-common/.agents/skills/meta-test-orchestrator into .cursor/skills/meta-test-orchestrator/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "meta-test-orchestrator", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/GoogleCloudPlatform/DataflowTemplates.git --path v2/spanner-common/.agents/skills/meta-test-orchestrator--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add GoogleCloudPlatform/DataflowTemplates --skill meta-test-orchestrator -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install GoogleCloudPlatform/DataflowTemplates meta-test-orchestrator --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/GoogleCloudPlatform/DataflowTemplates.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/v2/spanner-common/.agents/skills/meta-test-orchestrator .gemini/skills/meta-test-orchestrator && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "meta-test-orchestrator" agent skill from https://github.com/GoogleCloudPlatform/DataflowTemplates/tree/main/v2/spanner-common/.agents/skills/meta-test-orchestrator into .gemini/skills/meta-test-orchestrator/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "meta-test-orchestrator", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install GoogleCloudPlatform/DataflowTemplates meta-test-orchestratorInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add GoogleCloudPlatform/DataflowTemplates --skill meta-test-orchestrator -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/GoogleCloudPlatform/DataflowTemplates.git skills-src && mkdir -p .github/skills && cp -r skills-src/v2/spanner-common/.agents/skills/meta-test-orchestrator .github/skills/meta-test-orchestrator && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "meta-test-orchestrator" agent skill from https://github.com/GoogleCloudPlatform/DataflowTemplates/tree/main/v2/spanner-common/.agents/skills/meta-test-orchestrator into .github/skills/meta-test-orchestrator/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "meta-test-orchestrator", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add GoogleCloudPlatform/DataflowTemplates --skill meta-test-orchestrator -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install GoogleCloudPlatform/DataflowTemplates meta-test-orchestrator --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/GoogleCloudPlatform/DataflowTemplates.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/v2/spanner-common/.agents/skills/meta-test-orchestrator .opencode/skills/meta-test-orchestrator && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "meta-test-orchestrator" agent skill from https://github.com/GoogleCloudPlatform/DataflowTemplates/tree/main/v2/spanner-common/.agents/skills/meta-test-orchestrator into .opencode/skills/meta-test-orchestrator/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "meta-test-orchestrator", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
meta-test-orchestrator- GitHub stars
- 1.3k
- Token cost
- ~2.8k tokens
- SKILL.md length
- 1,069 words
- Files
- 1
- Skills in repo
- 12
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Template-agnostic Orchestrator Skill for generating and executing exhaustive testing suites for any migration template.
- Works in 6 steps: Goal → Global Constraint: Orchestrator Heartbeats → Initialization Requirements → …
- Agent Workflows work in your project
- SKILL.md covers 1. Goal, 2. Global Constraint:…, 3. Initialization Requirements and 4. Orchestration Rules, plus 2 more sections
- Calls ssh and mvn; reaches raw.githubusercontent.com
What it does
Meta Test Orchestrator is an agent skill from GoogleCloudPlatform/DataflowTemplates. Template-agnostic Orchestrator Skill for generating and executing exhaustive testing suites for any migration template. It will generate the functional and datatype related test for the source.
Its SKILL.md is about 2.8k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in Agent Workflows. It works with Google Cloud and Google BigQuery. The repository describes itself as: Cloud Dataflow Google-provided templates for solving in-Cloud data tasks. The licence is Apache-2.0.
When your agent uses it
- Agent Workflows work in your project
Example prompts
- “/meta-test-orchestrator”
Workflow steps
6 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit c95daba. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Shell commands in SKILL.md call:
sshmvn
From the folder's file list and the shell code blocks in SKILL.md.
Network
Hosts in commands or code, which the agent is likely to contact:
raw.githubusercontent.com
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Meta Test Orchestrator loads about 2.8k tokens when it runs. Until then it costs about 54 tokens; SKILL.md has 1,069 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from GoogleCloudPlatform/DataflowTemplates at commit c95daba, republished under its Apache-2.0 licence (© GoogleCloudPlatform). 1,069 words, ~2,784 tokens.
Download SKILL.mdSave it as
.claude/skills/meta-test-orchestrator/SKILL.md (or your agent's skills folder).- name
- meta-test-orchestrator
- description
- Template-agnostic Orchestrator Skill for generating and executing exhaustive testing suites for any migration template. It will generate the functional and datatype related test for the source.
Template-Agnostic Orchestrator Meta-Skill
This skill instructs an AI Agent to act as the "Project Manager" for onboarding a new database source into ANY given Dataflow testing template. It automates the generation and execution of the template's entire testing suite by spawning specialized subagents.
1. Goal
Orchestrate a fully automated, pipeline to build, test, and verify every single scenario defined in the template's src/test/manifest.yaml.
You MUST execute all spawned subagents sequentially. Do NOT run subagents concurrently, to prevent Maven staging conflicts.
2. Global Constraint: Orchestrator Heartbeats
[!CRITICAL] Resilience & Subagent Monitoring Rule: Because the full execution pipeline can take hours, backend server maintenance restarts might occasionally drop background polling scripts, leaving your subagents stranded in a "waiting" state while tests actually finish in the cloud.
- As the Orchestrator, you MUST maintain a proactive heartbeat.
- You MUST set a recurring
scheduletool timer (e.g.CronExpression: "*/2 * * * *",IsDaemon: false) dedicated to babysitting.- Whenever the heartbeat timer fires, explicitly use
manage_subagents listto check statuses. If you notice a subagent stuck inwaiting_for_messagefor an extended period, you must manually runssh ... tailagainst the remote logs to check the pipeline's true status, and then explicitly usesend_messageto blast the subagent awake with the logs so it resumes working.
3. Initialization Requirements
When a user begins a session with this Meta-Skill, ensure you have the following inputs before starting:
- Target Source Database Name:
- Reference Mapping Matrix File Path:
- Testing Environment Setup Path: Confirm that
testing_execution.envis populated in the workspace root. - Target Template Path:
- Smoke Test Scenarios: (comma-separated list of scenario IDs)
- Datatype Test Scenarios: (comma-separated list of scenario IDs)
- Manifest File Path:
[!IMPORTANT] Mapping Matrix Schema Validation: You must dynamically validate the user's provided
.csvmapping file against the canonical schema before proceeding with any orchestration or code generation.
- Use the
read_url_contenttool to fetch the raw canonical reference from:https://raw.githubusercontent.com/GoogleCloudPlatform/spanner-migration-tool/master/.agents/skills/source_research_helper/sampleOutput/mysql_datatype_mapping_matrix.csv- Parse the headers (first line) of both the fetched sample matrix and the local file at
Reference Datatype Mapping Matrix File Path.- Verify that every column header present in the fetched sample is also present in the local provided matrix (a subset match; the local matrix may contain extra custom columns, which is fine).
If ANY of the prompt inputs are missing, or if the testing_execution.env file does not exist in the root directory, or if the provided matrix is missing canonical headers, you MUST HALT EXECUTION IMMEDIATELY. Do not attempt to guess, hallucinate paths, or proceed. Output a direct question asking the user to provide the missing inputs, create the missing environment file, or fix the explicitly missing columns.
4. Orchestration Rules
- Sequential Threading: You may only have one active subagent running at any time.
- Template Onboarding Report: You MUST maintain a live markdown artifact called
template_onboarding_report.md.- Progress Tracker: Maintain a high-level counter (e.g.,
5 / 20 scenarios completed). - Consolidated Bug Log: Aggregate all source-code bugs discovered by subagents and the fixes applied to the
<Target_Template_Path>source code. - Challenges: Summarize any roadblocks or unsupported features (e.g., missing Spanner APIs).
- Progress Tracker: Maintain a high-level counter (e.g.,
- Error Escalation: If a subagent exhausts its self-healing retries and fails, report back to the user with a summary of the roadblock before halting the pipeline.
- Artifact Relocation & Syncing: Subagents frequently save their final migration reports (e.g.,
test_automation_migration_report.mdandlive_logs) into their isolated 'brain' execution sandboxes. As the parent Orchestrator, upon validating a subagent's success, you MUST natively copy their generated reports out of their system-isolated directories and move them directly into the correct workspace root directory using the following exact structure:- For Datatype tests:
src/test/resources/<target_db_name_lowercase>/reports/datatype_testing/<Scenario_ID>_<Timestamp>/ - For Functional tests:
src/test/resources/<target_db_name_lowercase>/reports/functional_testing/<Scenario_ID>_<Timestamp>/
- For Datatype tests:
- Continuous Meta-Report Syncing: You MUST copy the overarching meta-skill report (
template_onboarding_report.md) and orchestration execution logs to a persistent directory in the workspace atsrc/test/resources/<target_db_name_lowercase>/reports/meta-reports/right from the beginning, and you must constantly update/overwrite this workspace file as tests run and statuses change iteratively!
Show full SKILL.md (424 more words)Show less
5. The Execution Pipeline
You must guide the workflow through these steps sequentially, waiting for one subagent to complete successfully before advancing or spinning up the next.
Infrastructure Smoke Tests
Goal: Prove the infrastructure and testing_execution.env works before adding complex data types.
Action: Spawn a subagent using Prompt Template A targeting only the scenarios provided in the Smoke Test Scenarios input list (e.g., bulk-simple).
Complete Datatypes Validation
Goal: Validate all datatypes for the template (including alternate dialects like PostgreSQL Spanner deployments) using the provided reference mapping file. Action: Spawn subagents sequentially using Prompt Template B for the explicit scenarios provided in the Datatype Test Scenarios input list.
Once those are complete, aggressively scan the provided Manifest File Path for any additional scenarios tagged with type: datatypes (that you haven't executed yet) and spawn subagents for them one at a time. Wait for each subagent to complete before spawning the next.
Functional Scenarios Scale-Out
Goal: Translate the remaining complex features (e.g., sharding, foreign keys, limits). Action: Scan the provided Manifest File Path for all remaining functional scenarios (not covered in the previous steps).
- Spawn subagents for each scenario one at a time using Prompt Template A.
- Crucial Rule: As soon as one subagent completes successfully, instantly capture its logs and reports, update your tracker, and proactively move linearly to spawn the next subagent scenario. Do absolutely NOT halt the pipeline or wait for user input/confirmation between scenarios!
Final Validation
Goal: Run a complete verification regression suite across all generated tests to guarantee zero regressions. Action:
- To construct the final regression test command, read
v2/spanner-common/.agents/skills/add-source-functional-integ-test/SKILL.mdto learn how to natively assemble themvn verifyparameter list from the environment configs. - Once assembled, swap the target class name parameter for the overarching wildcard corresponding to the dialect (e.g.,
-Dtest=Oracle*IT). - Maintain the
-DdirectRunnerTestflag to run the regression safely and rapidly. Execute this on the remote test VM. - If the regression suite fails on any specific test, you must re-invoke a subagent dedicated to that specific failing test scenario, passing it the failure logs and instructing it to self-heal the source code and verify its individual test again.
- Capture the final outcome of the regression run in your meta-report and securely copy the final executed regression logs to the workspace.
6. Subagent Prompt Templates
Prompt Template A (Functional Worker)
Use this prompt when invoking subagents for Smoke Tests and Functional Scenarios.
Skill to load: v2/spanner-common/.agents/skills/add-source-functional-integ-test/SKILL.md
Please load and execute the `v2/spanner-common/.agents/skills/add-source-functional-integ-test/SKILL.md` skill to generate a functional integration test.
Inputs:
1. Scenario ID: [INSERT_SCENARIO_ID]
2. Manifest File Path: [INSERT_MANIFEST_FILE_PATH]
3. Target Source Database Name: [INSERT_DB_NAME]
4. Reference Datatype Mapping Matrix File Path: [INSERT_MAPPING_FILE_PATH]
5. Testing Environment Setup Path: [INSERT_ENV_PATH]
CRITICAL CONSTRAINTS:
- Treat the provided Reference Mapping File as your absolute source of truth to derive baseline mapping schemas. You MUST strictly use this matrix to generate testing mappings. Do NOT perform independent type research.
- Load execution strategy from `testing_execution.env`.
- Follow the Production Code Priority Rule: if tests fail, investigate the target template source code before assuming the test is wrong.
- Use the `-DdirectRunnerTest` flag for iterative testing. Once the DirectRunner loop passes, you MUST perform a final execution directly against Cloud Dataflow (omitting the flag) and ensure that run completely succeeds before generating your final report.
- Upon completing your artifact report, ensure `RequestFeedback: false` is set so your status naturally changes back to idle. Do NOT pause waiting for conversational human feedback.
Prompt Template B (Datatype Worker)
Use this prompt when invoking subagents for Datatypes Validation.
Skill to load: v2/spanner-common/.agents/skills/add-source-datatype-integ-test/SKILL.md
Please load and execute the `v2/spanner-common/.agents/skills/add-source-datatype-integ-test/SKILL.md` skill to generate a datatype integration test.
Inputs:
1. Scenario ID: [INSERT_SCENARIO_ID]
2. Manifest File Path: [INSERT_MANIFEST_FILE_PATH]
3. Target Source Database Name: [INSERT_DB_NAME]
4. Reference Datatype Mapping Matrix File Path: [INSERT_MAPPING_FILE_PATH]
5. Testing Environment Setup Path: [INSERT_ENV_PATH]
CRITICAL CONSTRAINTS:
- Treat the provided Reference Mapping File as your absolute source of truth to derive baseline mapping schemas. You MUST strictly use this matrix to generate testing mappings. Do NOT perform independent type research.
- Load execution strategy from `testing_execution.env`.
- Use the `-DdirectRunnerTest` flag for iterative testing.Once the DirectRunner loop passes, you MUST perform a final execution directly against Cloud Dataflow (omitting the flag) and ensure that run completely succeeds before generating your final report.
- Upon completing your artifact report, ensure `RequestFeedback: false` is set so your status naturally changes back to idle. Do NOT pause waiting for conversational human feedback.
© GoogleCloudPlatform, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in v2/spanner-common/.agents/skills/meta-test-orchestrator of GoogleCloudPlatform/DataflowTemplates.
Open the folder on GitHubat commit c95daba
Compare with similar skills
Meta Test Orchestrator next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Meta Test Orchestrator this skillGoogleCloudPlatform/DataflowTemplates | 1.3k | — | ~2.8k | Automated safety check: Pass | Apache-2.0 | |
| Google Cloud Storage Basicsgoogle/skills | 21k | — | ~2.8k | Automated safety check: Pass | Apache-2.0 | |
| Io ConnectorsKilo-Org/kilo-marketplace | 189 | — | ~1.3k | Automated safety check: Pass | Apache-2.0 | |
| Retail Product Search Agentgoogle/adk-recipes | 10k | — | ~3k | Automated safety check: Pass | Apache-2.0 | |
| Cxas Configurable DashboardsGoogleCloudPlatform/cxas-scrapi | 106 | — | ~1.8k | Automated safety check: Pass | Apache-2.0 | |
| GCP DrawIO Diagram Generatora5c-ai/babysitter | 1.8k | — | ~3.7k | Automated safety check: Pass | MIT |
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Related
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Categories
Questions about Meta Test Orchestrator
What does Meta Test Orchestrator do?
Template-agnostic Orchestrator Skill for generating and executing exhaustive testing suites for any migration template. Meta Test Orchestrator is an agent skill from GoogleCloudPlatform/DataflowTemplates. Template-agnostic Orchestrator Skill for generating and executing exhaustive testing suites for any migration template.
When should I use Meta Test Orchestrator?
Meta Test Orchestrator fits situations like: agent Workflows work in your project.
How do I install Meta Test Orchestrator in Claude Code?
Run `npx skills add GoogleCloudPlatform/DataflowTemplates --skill meta-test-orchestrator -a claude-code`. Or copy the skill folder (v2/spanner-common/.agents/skills/meta-test-orchestrator in GoogleCloudPlatform/DataflowTemplates) into .claude/skills/meta-test-orchestrator in your project. Claude Code loads it when a task matches its description.
How do I install Meta Test Orchestrator in Codex?
Run `npx skills add GoogleCloudPlatform/DataflowTemplates --skill meta-test-orchestrator -a codex`. Or copy the skill folder (v2/spanner-common/.agents/skills/meta-test-orchestrator in GoogleCloudPlatform/DataflowTemplates) into .agents/skills/meta-test-orchestrator in your project. Codex loads it when a task matches its description.
Can I use Meta Test Orchestrator in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add GoogleCloudPlatform/DataflowTemplates --skill meta-test-orchestrator -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/meta-test-orchestrator, .gemini/skills/meta-test-orchestrator, .github/skills/meta-test-orchestrator and .opencode/skills/meta-test-orchestrator in your project.
What does Meta Test Orchestrator need to run?
Going by SKILL.md and its folder, Meta Test Orchestrator needs the command-line tools its instructions call (ssh and mvn).
Does Meta Test Orchestrator access the network?
SKILL.md names 1 domain. In commands or code: raw.githubusercontent.com; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.
Is Meta Test Orchestrator safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Meta Test Orchestrator use?
Meta Test Orchestrator is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Meta Test Orchestrator use?
About 2.8k tokens (SKILL.md is roughly 11k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Meta Test Orchestrator?
Skills that share tags, products or a category with Meta Test Orchestrator: Google Cloud Storage Basics (google/skills, 21k stars), Io Connectors (Kilo-Org/kilo-marketplace, 189 stars), Retail Product Search Agent (google/adk-recipes, 10k stars) and Cxas Configurable Dashboards (GoogleCloudPlatform/cxas-scrapi, 106 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Meta Test Orchestrator?
GoogleCloudPlatform (a GitHub organization) maintains it in GoogleCloudPlatform/DataflowTemplates, which has 1,315 GitHub stars. The repository holds 12 skills in this directory. The repository was last updated on October 7, 2026.
Source: GoogleCloudPlatform/DataflowTemplates on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.