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 Pstasov/mcp-tester-helper --skill skill-examplegit clone --depth 1 https://github.com/Pstasov/mcp-tester-helperWrote 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/pstasov/mcp-tester-helper/skill-example)<a href="https://agentmods.dev/skills/pstasov/mcp-tester-helper/skill-example"><img src="https://agentmods.dev/badge/skills/pstasov/mcp-tester-helper/skill-example/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/pstasov/mcp-tester-helper/skill-example"><img src="https://agentmods.dev/badge/skills/pstasov/mcp-tester-helper/skill-example.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00060 | $0.01093 |
| Opus 5.5 | $0.00024 | $0.00437 |
| Sonnet 5.5 | $0.00012 | $0.00219 |
| Haiku 4.5 | $0.00006 | $0.00109 |
Grade A, and why
mcp-tester-helper 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 1mo 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 — 95 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Rules for Working with Multi-Stand Infrastructure
This skill describes the algorithm for the AI assistant to execute requests to corporate services, logs, and databases using the mcp-tester-helper MCP server and a local configuration file.
1. Infrastructure Configuration
All information about stands (environments), their addresses, databases, and OpenAPI specs is stored in the configuration file config.json in the project root.
The AI assistant must before any request:
- Read
config.json. - Verify that the target stand (e.g.
dev) and service exist in the config.
Structure of config.json:
- Stands (e.g.
dev,test,staging,prod).auth— stand authorization settings (URL, env variable names for login/password).elastic— OpenSearch/Elasticsearch cluster access settings (for logs).database— PostgreSQL database connection settings for this stand.services— list of microservices, where the key is the system name (e.g.billing,users).url— base URL of the service.openapi— path to local Swagger/OpenAPI spec file or URL.
2. HTTP API Requests (Tool: test_request)
When the user asks to call a microservice:
- The AI reads
config.jsonand finds the serviceurlandopenapispec. - If needed, studies the Swagger/OpenAPI file using
view_file. - Calls the
test_requesttool:
{
"stand": "dev",
"service": "billing",
"method": "POST",
"endpoint": "/payments",
"body": { ... }
}
Auth is automatic: the server obtains a token from the stand's auth URL and injects it into the request.
3. OpenSearch / Log Search (Tools: os_search, os_indices, os_mappings)
OpenSearch is part of each stand's infrastructure (the elastic section in config).
Default index pattern:
If the stand's config sets elastic.indexPattern, it's used as the default index for
os_search and os_indices — you can omit index/index_pattern entirely and query
logs right away. Check list_services output for the configured indexPattern before
falling back to discovery below.
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.
- 1mo ago Changed · +6 lines c7a6454ba508
- 2mo ago First seen · 89 lines · 60 tokens per session scan A 915bb9a863a5
mcp-tester-helper is a skill published in the GitHub repository Pstasov/mcp-tester-helper (0 stars, last pushed 1mo ago), licensed MIT. It adds 60 tokens to every session and 1,093 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-08-31.
Other skills, from other repositories
verify-omnigent
Drive Omnigent the way a user does and prove a behavior with recorded evidence, using an isolated server, runner, and mock model plus a feature map of every user entry point. Load before reproducing a user-facing bug, before claiming a fix works, or when reviewing whether a change covered every surface (session vs.…
test-master
Generates test files, creates mocking strategies, analyzes code coverage, designs test architectures, and produces test plans and defect reports across functional, performance, and security testing disciplines. Use when writing unit tests, integration tests, or E2E tests; creating test strategies or automation…
eval-dataset-design
A guide to creating reliable test sets for AI agents. Each evaluation task includes the request, the simulated user's behaviour, a reset starting state, and a success check that can be independently verified.
dogfood
Systematically explore and test a mobile app on iOS/Android with agent-device to find bugs, UX issues, and other problems. Use when asked to dogfood, QA, exploratory test, find issues, bug hunt, or test this app on mobile.
cua-driver
Use Cua Driver for desktop or browser tasks that are awkward or unavailable through Bash/APIs, or when the user explicitly wants GUI interaction: app testing, visual bug reproduction, form filling, calendar entry, screenshots, and demo recording. Also covers Cua setup; not OpenAI Codex Computer Use or web research.
test-warp-ui
Guides testing Warp UI features and changes using the computer use tool. Use this skill only when computer-use testing was requested (explicit request or accepted offer) and the computeruse tool is available to the agent. Covers launching Warp and verifying UI behavior.