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 zjshen14/opencli --skill run-testsgit clone --depth 1 https://github.com/zjshen14/opencliWrote 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/zjshen14/opencli/run-tests)<a href="https://agentmods.dev/skills/zjshen14/opencli/run-tests"><img src="https://agentmods.dev/badge/skills/zjshen14/opencli/run-tests/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/zjshen14/opencli/run-tests"><img src="https://agentmods.dev/badge/skills/zjshen14/opencli/run-tests.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 | $0.00023 | $0.00306 |
| Opus 5.5 | $0.00009 | $0.00122 |
| Sonnet 5.5 | $0.00005 | $0.00061 |
| Haiku 4.5 | $0.00002 | $0.00031 |
Grade A, and why
run-tests 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.
What it actually says
Run the test suite for this project.
Project manifest (for test command detection): !{cat package.json 2>/dev/null | python3 -c "import sys,json; s=json.load(sys.stdin).get('scripts',{}); print('\n'.join(f'{k}: {v}' for k,v in s.items() if 'test' in k))" 2>/dev/null || grep -E '^test' Makefile 2>/dev/null | head -5 || echo "(no manifest found)"}
Task: $ARGUMENTS
Steps:
- Detect the test command from the project manifest:
- Node.js:
npm testor thetestscript in package.json - Python:
pytestorpython -m pytest - Go:
go test ./... - Rust:
cargo test - Other: check Makefile or CI config (
.github/workflows/)
- Node.js:
- Run the test suite and capture output
- Summarise: total passed / failed / skipped
- For each failure: show test name, error message, and the most relevant stack frame
- If failures are obvious to fix, suggest the fix — but do not apply it without instruction
Do not guess the test command — always read the manifest first.
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 First seen · 29 lines · 23 tokens per session scan A 49264b5c1531
run-tests is a skill published in the GitHub repository zjshen14/opencli (2 stars, last pushed 1mo ago), licensed MIT. It adds 23 tokens to every session and 306 once invoked, about $0.0001 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
evaluating-llms-harness
Evaluates LLMs across 60+ academic benchmarks (MMLU, HumanEval, GSM8K, TruthfulQA, HellaSwag). Use when benchmarking model quality, comparing models, reporting academic results, or tracking training progress. Industry standard used by EleutherAI, HuggingFace, and major labs. Supports HuggingFace, vLLM, APIs.
dogfood
Exploratory QA of web apps: find bugs, evidence, reports.
test-driven-development
TDD: enforce RED-GREEN-REFACTOR, tests before code.
test-reporting
Run the Level 2 dummy agent integration test suite and produce a detailed HTML report with per-test input → outcome analysis.
verify-changes
Verify code changes by running the project's typecheck, build, lint, and targeted tests, then fix and re-run until clean. Use after editing any source file.
self-improve-with-tinyfish
Enables Hermes to create new reusable skills for itself by researching live web sources with TinyFish Search and Fetch, analyzing source coverage, writing SKILL.md files, and installing them into Hermes memory. Use when the user asks Hermes to learn, teach itself, upgrade itself, or save a reusable capability.