Borrowing it
Nothing to install: this file belongs to testrelic-ai/testrelic-mcp-server. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/testrelic-ai/testrelic-mcp-server/main/.agents/skills/testrelic-mcp/SKILL.mdgit clone --depth 1 https://github.com/testrelic-ai/testrelic-mcp-serverWrote 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/testrelic-ai/testrelic-mcp-server/testrelic-mcp)<a href="https://agentmods.dev/skills/testrelic-ai/testrelic-mcp-server/testrelic-mcp"><img src="https://agentmods.dev/badge/skills/testrelic-ai/testrelic-mcp-server/testrelic-mcp/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/testrelic-ai/testrelic-mcp-server/testrelic-mcp"><img src="https://agentmods.dev/badge/skills/testrelic-ai/testrelic-mcp-server/testrelic-mcp.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 | $0.00104 | $0.03420 |
| Opus 5 | $0.00052 | $0.01710 |
| Sonnet 5 | $0.00021 | $0.00684 |
| Haiku 4.5 | $0.00010 | $0.00342 |
Grade B, and why
testrelic-mcp scanned grade B with 1 finding 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 11d 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.
Reads agent configuration directoriesmediumAgent snooping
.claude/, .codex/, .gemini/ hold keys, settings and other credentials a mod has no legitimate need for.
| Codex CLI | `~/.codex/config.toml` | `[mcp_servers.<id>]` (**TOML**) | `command`/`args` vs `url` | The source is not reproduced here
Licensed AGPL-3.0
The repository is licensed AGPL-3.0, which this catalogue does not treat as permission to reproduce the file. Read it at the source.
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 11d ago First seen · 316 lines · 104 tokens per session scan B 6af9fdfb8cb4
testrelic-mcp is a skill published in the GitHub repository testrelic-ai/testrelic-mcp-server (2 stars, last pushed 5d ago), licensed AGPL-3.0. It adds 104 tokens to every session and 3,420 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it B with 1 finding (reads agent configuration directories). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
langsmith-observability
LLM observability platform for tracing, evaluation, and monitoring. Use when debugging LLM applications, evaluating model outputs against datasets, monitoring production systems, or building systematic testing pipelines for AI applications.
langsmith-observability
LLM observability platform for tracing, evaluation, and monitoring. Use when debugging LLM applications, evaluating model outputs against datasets, monitoring production systems, or building systematic testing pipelines for AI applications.
dd-triage-flaky-test
Load when investigating a specific flaky test. Gets history, failure pattern, and category, then recommends fix, quarantine, or escalate.
dd-unblock-pr
Load when investigating a failing PR CI pipeline or checking PR health. Attributes each CI failure as flaky, infra, or regression, proposes a targeted action, and reports code coverage.
agent-inspect
Local evidence debugger and trajectory-test toolkit for TypeScript AI agents. Use when capturing framework-faithful traces, asserting TraceContract/TraceFacts, packaging Evidence v2, or inspecting local runs over read-only MCP (gettracefacts).
debugging-workflow
Find the cause of a defect by hypothesis and bisection rather than by guessing, then fix it behind a regression test. Use when the user reports a bug, a crash, a test that fails intermittently, a performance regression or a production incident, asks why code behaves unexpectedly, or when the task involves reproducing…