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testrail
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TestRail Integration
Bidirectional sync between Playwright tests and TestRail test management.
Prerequisites
Environment variables must be set:
TESTRAIL_URL— e.g.,https://your-instance.testrail.ioTESTRAIL_USER— your emailTESTRAIL_API_KEY— API key from TestRail
If not set, inform the user how to configure them and stop.
Capabilities
1. Import Test Cases → Generate Playwright Tests
/pw:testrail import --project <id> --suite <id>
Steps:
- Call
testrail_get_casesMCP tool to fetch test cases - For each test case:
- Read title, preconditions, steps, expected results
- Map to a Playwright test using appropriate template
- Include TestRail case ID as test annotation:
test.info().annotations.push({ type: 'testrail', description: 'C12345' })
- Generate test files grouped by section
- Report: X cases imported, Y tests generated
2. Push Test Results → TestRail
/pw:testrail push --run <id>
Steps:
- Run Playwright tests with JSON reporter:
npx playwright test --reporter=json > test-results.json - Parse results: map each test to its TestRail case ID (from annotations)
- Call
testrail_add_resultMCP tool for each test:- Pass → status_id: 1
- Fail → status_id: 5, include error message
- Skip → status_id: 2
- Report: X results pushed, Y passed, Z failed
3. Create Test Run
/pw:testrail run --project <id> --name "Sprint 42 Regression"
Steps:
- Call
testrail_add_runMCP tool - Include all test case IDs found in Playwright test annotations
- Return run ID for result pushing
4. Sync Status
/pw:testrail status --project <id>
Steps:
- Fetch test cases from TestRail
- Scan local Playwright tests for TestRail annotations
- Report coverage:
TestRail cases: 150 Playwright tests with TestRail IDs: 120 Unlinked TestRail cases: 30 Playwright tests without TestRail IDs: 15
5. Update Test Cases in TestRail
/pw:testrail update --case <id>
Steps:
- Read the Playwright test for this case ID
- Extract steps and expected results from test code
- Call
testrail_update_caseMCP tool to update steps
MCP Tools Used
| Tool | When |
|---|---|
testrail_get_projects | List available projects |
testrail_get_suites | List suites in project |
testrail_get_cases | Read test cases |
testrail_add_case | Create new test case |
testrail_update_case | Update existing case |
testrail_add_run | Create test run |
testrail_add_result | Push individual result |
testrail_get_results | Read historical results |
Test Annotation Format
All Playwright tests linked to TestRail include:
test('should login successfully', async ({ page }) => {
test.info().annotations.push({
type: 'testrail',
description: 'C12345',
});
// ... test code
});
This annotation is the bridge between Playwright and TestRail.
Output
- Operation summary with counts
- Any errors or unmatched cases
- Link to TestRail run/results
File metadata
name: "testrail" description: >- Sync tests with TestRail. Use when user mentions "testrail", "test management", "test cases", "test run", "sync test cases", "push results to testrail", or "import from testrail".
View original text
---
name: "testrail"
description: >-
Sync tests with TestRail. Use when user mentions "testrail", "test management",
"test cases", "test run", "sync test cases", "push results to testrail",
or "import from testrail".
---
# TestRail Integration
Bidirectional sync between Playwright tests and TestRail test management.
## Prerequisites
Environment variables must be set:
- `TESTRAIL_URL` — e.g., `https://your-instance.testrail.io`
- `TESTRAIL_USER` — your email
- `TESTRAIL_API_KEY` — API key from TestRail
If not set, inform the user how to configure them and stop.
## Capabilities
### 1. Import Test Cases → Generate Playwright Tests
```
/pw:testrail import --project <id> --suite <id>
```
Steps:
1. Call `testrail_get_cases` MCP tool to fetch test cases
2. For each test case:
- Read title, preconditions, steps, expected results
- Map to a Playwright test using appropriate template
- Include TestRail case ID as test annotation: `test.info().annotations.push({ type: 'testrail', description: 'C12345' })`
3. Generate test files grouped by section
4. Report: X cases imported, Y tests generated
### 2. Push Test Results → TestRail
```
/pw:testrail push --run <id>
```
Steps:
1. Run Playwright tests with JSON reporter:
```bash
npx playwright test --reporter=json > test-results.json
```
2. Parse results: map each test to its TestRail case ID (from annotations)
3. Call `testrail_add_result` MCP tool for each test:
- Pass → status_id: 1
- Fail → status_id: 5, include error message
- Skip → status_id: 2
4. Report: X results pushed, Y passed, Z failed
### 3. Create Test Run
```
/pw:testrail run --project <id> --name "Sprint 42 Regression"
```
Steps:
1. Call `testrail_add_run` MCP tool
2. Include all test case IDs found in Playwright test annotations
3. Return run ID for result pushing
### 4. Sync Status
```
/pw:testrail status --project <id>
```
Steps:
1. Fetch test cases from TestRail
2. Scan local Playwright tests for TestRail annotations
3. Report coverage:
```
TestRail cases: 150
Playwright tests with TestRail IDs: 120
Unlinked TestRail cases: 30
Playwright tests without TestRail IDs: 15
```
### 5. Update Test Cases in TestRail
```
/pw:testrail update --case <id>
```
Steps:
1. Read the Playwright test for this case ID
2. Extract steps and expected results from test code
3. Call `testrail_update_case` MCP tool to update steps
## MCP Tools Used
| Tool | When |
|---|---|
| `testrail_get_projects` | List available projects |
| `testrail_get_suites` | List suites in project |
| `testrail_get_cases` | Read test cases |
| `testrail_add_case` | Create new test case |
| `testrail_update_case` | Update existing case |
| `testrail_add_run` | Create test run |
| `testrail_add_result` | Push individual result |
| `testrail_get_results` | Read historical results |
## Test Annotation Format
All Playwright tests linked to TestRail include:
```typescript
test('should login successfully', async ({ page }) => {
test.info().annotations.push({
type: 'testrail',
description: 'C12345',
});
// ... test code
});
```
This annotation is the bridge between Playwright and TestRail.
## Output
- Operation summary with counts
- Any errors or unmatched cases
- Link to TestRail run/results
Review the source
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- License
- MIT
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Skill source recorded
Skill instructions are recorded. This is not a runtime test, safety guarantee or compatibility certification.
Review before install: Avoid automatic install
License: MIT
- Dependency or permission surface needs review
- Permission surface may require sandboxing
- AI review approval is missing
- Quality score needs review
- Permission surface needs review: secrets or environment access, shell or command execution
- GitHub adoption: 57 GitHub stars
- Stars/forks activity: 57 stars, 12 forks; issue activity unavailable in current metadata
- README/SKILL.md completeness: Public metadata needs stronger README/SKILL.md context
- Dependency/runtime risk: command execution surface, credential or environment access
- Permission surface: secrets or environment access, shell or command execution
- Review status: AI review approval is missing
Listed tools are metadata hints, not tested compatibility. Agent prompts are suggested handoffs.
Start with one small task
- 1Read the source. Confirm the input, expected output, dependencies and permissions.
- 2Ask your agent for a plan. Approve setup and any costs before running a small isolated test.
- 3Check the output and changed files. Report only what actually ran; keep the source revision for reproduction.
Check the source for dependencies, API keys and third-party costs. A public repository does not mean every service is free.
Source & usage notes
IndexedStatic Checked
Repository metadata and review signals are advisory. Popularity, source discovery and successful execution are different facts.
- Source repository
- adriannoes/awesome-agentic-ai
- License
- MIT
- Version
- 1.0.0
- Last GitHub push
- Aug 28, 2026
- Registry updated
- Sep 8, 2026
Version reported in registry metadata; check source releases before relying on it.
Quality
56/100
Promising
Trust
57/100
Do not auto-install
Audit
70/100
Needs review
- Dependency or permission surface needs review
- Permission surface may require sandboxing
- AI review approval is missing
- Quality score needs review
- Permission surface needs review: secrets or environment access, shell or command execution
- GitHub adoption: 57 GitHub stars
- Stars/forks activity: 57 stars, 12 forks; issue activity unavailable in current metadata
- README/SKILL.md completeness: Public metadata needs stronger README/SKILL.md context
- Dependency/runtime risk: command execution surface, credential or environment access
- Permission surface: secrets or environment access, shell or command execution
- Review status: AI review approval is missing
- Verified installs
- —
- Outcomes
- —
Copies are not installs. Installation counts require a reported successful installation; they are not a blanket quality guarantee.
Agent access
This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.
More details
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}For the creator
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