Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add adriannoes/awesome-agentic-ai --skill testrailgit clone --depth 1 https://github.com/adriannoes/awesome-agentic-aiWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/adriannoes/awesome-agentic-ai/testrail)<a href="https://agentmods.dev/skills/adriannoes/awesome-agentic-ai/testrail"><img src="https://agentmods.dev/badge/skills/adriannoes/awesome-agentic-ai/testrail/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/adriannoes/awesome-agentic-ai/testrail"><img src="https://agentmods.dev/badge/skills/adriannoes/awesome-agentic-ai/testrail.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium MCP Rug Pull · line 48 npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.Fix: Pin the version: npx @scope/[email protected]
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00050 | $0.00876 |
| Opus 5 | $0.00025 | $0.00438 |
| Sonnet 5 | $0.00010 | $0.00175 |
| Haiku 4.5 | $0.00005 | $0.00088 |
Grade A, and why
testrail scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 130 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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>
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago First seen · 130 lines · 50 tokens per session scan A 4f94a5087913
testrail is a skill published in the GitHub repository adriannoes/awesome-agentic-ai (57 stars, last pushed 12d ago), licensed MIT. It adds 50 tokens to every session and 876 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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