run-acceptance-tests▌
hashicorp/agent-skills · updated Apr 8, 2026
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Execute and diagnose Go acceptance tests for Terraform providers with structured troubleshooting.
- ›Run focused acceptance tests using go test -run=TestAccFeatureHappyPath with TF_ACC=1 environment variable
- ›Diagnose failures progressively: retry with -count=1 , enable verbose output with -v , activate debug logging via TF_LOG=debug , and persist Terraform workspace with TF_ACC_WORKING_DIR_PERSIST=1
- ›Validate test reliability by intentionally breaking a TestCheckFunc, re-running the test
An acceptance test is a Go test function with the prefix TestAcc.
To run a focussed acceptance test named TestAccFeatureHappyPath:
-
Run
go test -run=TestAccFeatureHappyPathwith the following environment variables:TF_ACC=1
Default to non-verbose test output.
-
The acceptance tests may require additional environment variables for specific providers. If the test output indicates missing environment variables, then suggest how to set up these environment variables securely.
To diagnose a failing acceptance test, use these options, in order. These options are cumulative: each option includes all the options above it.
- Run the test again. Use the
-count=1option to ensure thatgo testdoes not use a cached result. - Offer verbose
go testoutput. Use the-voption. - Offer debug-level logging. Enable debug-level logging with the environment
variable
TF_LOG=debug. - Offer to persist the acceptance test's Terraform workspace. Enable
persistance with the environment variable
TF_ACC_WORKING_DIR_PERSIST=1.
A passing acceptance test may be a false negative. To "flip" a passing
acceptance test named TestAccFeatureHappyPath:
- Edit the value of one of the TestCheckFuncs in one of the TestSteps in the TestCase.
- Run the acceptance test. Expect the test to fail.
- If the test fails, then undo the edit and report a successful flip. Else, keep the edit and report an unsuccessful flip.
How to use run-acceptance-tests on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add run-acceptance-tests
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches run-acceptance-tests from GitHub repository hashicorp/agent-skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate run-acceptance-tests. Access the skill through slash commands (e.g., /run-acceptance-tests) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
Task Automation & Efficiency
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Knowledge Enhancement
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Quality Improvement
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client with skill support
- ›Clear understanding of task or problem to solve
- ›Willingness to iterate and refine outputs
Time Estimate
15-45 minutes depending on use case complexity
Installation Steps
- 1.Install skill using provided installation command
- 2.Test with simple use case relevant to your work
- 3.Evaluate output quality and relevance
- 4.Iterate on prompts to improve results
- 5.Integrate into regular workflow if valuable
Common Pitfalls
- ⚠Expecting perfect results without iteration
- ⚠Not providing enough context in prompts
- ⚠Using skill for tasks outside its intended scope
- ⚠Accepting outputs without review and validation
Best Practices▌
✓ Do
- +Start with clear, specific prompts
- +Provide relevant context and constraints
- +Review and refine all outputs before using
- +Iterate to improve output quality
- +Document successful prompt patterns
✗ Don't
- −Don't use without understanding skill limitations
- −Don't skip validation of outputs
- −Don't share sensitive information in prompts
- −Don't expect skill to replace human judgment
💡 Pro Tips
- ★Be specific about desired format and style
- ★Ask for multiple options to choose from
- ★Request explanations to understand reasoning
- ★Combine AI efficiency with human expertise
When to Use This▌
✓ Use When
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid When
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
Learning Path▌
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.8★★★★★53 reviews- ★★★★★Mei Torres· Dec 28, 2024
run-acceptance-tests reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Dhruvi Jain· Dec 20, 2024
We added run-acceptance-tests from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Ama Rahman· Dec 4, 2024
I recommend run-acceptance-tests for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Kabir Harris· Nov 23, 2024
Useful defaults in run-acceptance-tests — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Luis Park· Nov 19, 2024
run-acceptance-tests has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Oshnikdeep· Nov 11, 2024
run-acceptance-tests fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Ira Gupta· Oct 14, 2024
run-acceptance-tests has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Kiara Anderson· Oct 10, 2024
Useful defaults in run-acceptance-tests — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Ganesh Mohane· Oct 2, 2024
run-acceptance-tests is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Aarav Dixit· Sep 25, 2024
Registry listing for run-acceptance-tests matched our evaluation — installs cleanly and behaves as described in the markdown.
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