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 agentmods add skills/physics91/openrouter-mcp/testnpx skills add physics91/openrouter-mcp --skill testgit clone --depth 1 https://github.com/physics91/openrouter-mcpWrote 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/physics91/openrouter-mcp/test)<a href="https://agentmods.dev/skills/physics91/openrouter-mcp/test"><img src="https://agentmods.dev/badge/skills/physics91/openrouter-mcp/test.svg" alt="Measured on agentmods" 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 | $0.00027 | $0.00938 |
| Opus 5 | $0.00014 | $0.00469 |
| Sonnet 5 | $0.00005 | $0.00188 |
| Haiku 4.5 | $0.00003 | $0.00094 |
Grade A, and why
test 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 3d 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 — 89 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Test Skill
Overview
This repository's canonical local test entrypoint is python3 run_tests.py <suite> -v.
Use it even when package.json exposes wrappers; the Python runner is the source of truth for local assurance behavior.
Execution Baseline
- Prefer
python3for runningrun_tests.pyand installing Python dependencies. - If
python3is unavailable butpythonexists, usepythonas fallback. - If pip installation is blocked by externally-managed environment (PEP 668), create and use local
.venv. - Default recommendation:
assurancefor PR readiness. - For the fastest confidence check, use
quickfirst and thenregressionif needed. - Do not substitute
npm run test:assuranceunless the user explicitly wants the npm wrapper.
Preflight
- Confirm
python3andnpmare available. - For
assuranceorcoverage, ensurepytest-covis installed:python3 -m pip install -r requirements-dev.txt - If pip is externally managed, switch to:
python3 -m venv .venv .venv/bin/python -m pip install -r requirements-dev.txt - For the
realsuite, requireOPENROUTER_API_KEYand remember the run is interactive and billable.
Canonical Command
python3 run_tests.py <suite> -v
Quick Reference
| Suite | Command | When to use |
|---|---|---|
assurance |
python3 run_tests.py assurance -v |
PR gate (unit+contract+property+replay+security, coverage>=70%) |
unit |
python3 run_tests.py unit -v |
Fast isolated checks during development |
integration |
python3 run_tests.py integration -v |
Mocked API integration smoke test |
quick |
python3 run_tests.py quick -v |
Fastest sanity (two regression guards only) |
regression |
python3 run_tests.py regression -v |
Critical bug-prevention tests |
coverage |
python3 run_tests.py coverage -v |
Full HTML coverage report (htmlcov/index.html) |
all |
python3 run_tests.py all -v |
Everything except real API tests |
real |
python3 run_tests.py real -v |
Live API calls - requires OPENROUTER_API_KEY, consumes credits |
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.
- 3d ago First seen · 89 lines · 27 tokens per session scan A 38a0b9a18180
test is a skill published in the GitHub repository physics91/openrouter-mcp (9 stars, last pushed 21d ago), licensed MIT. It adds 27 tokens to every session and 938 once invoked, about $0.0001 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 2026-08-31.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…