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 metaspartan/cybara --skill huggingface-community-evalsgit clone --depth 1 https://github.com/metaspartan/cybaraWrote 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/metaspartan/cybara/huggingface-community-evals)<a href="https://agentmods.dev/skills/metaspartan/cybara/huggingface-community-evals"><img src="https://agentmods.dev/badge/skills/metaspartan/cybara/huggingface-community-evals/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/metaspartan/cybara/huggingface-community-evals"><img src="https://agentmods.dev/badge/skills/metaspartan/cybara/huggingface-community-evals.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00033 | $0.00305 |
| Opus 5 | $0.00016 | $0.00152 |
| Sonnet 5 | $0.00007 | $0.00061 |
| Haiku 4.5 | $0.00003 | $0.00030 |
Grade A, and why
huggingface-community-evals 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 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.
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
Hugging Face Community Evaluations
Use Inspect or LightEval for models hosted on the Hub. Keep evaluation execution separate from publishing results.
Workflow
- Record the model ID, immutable revision, inference backend, tokenizer, chat template, dtype, quantization, generation parameters, task revision, and seed.
- Check gated-model authentication without printing credentials.
- Choose a backend supported by the model and hardware. Prefer vLLM for supported throughput workloads and Transformers or Accelerate as compatibility fallbacks.
- Start with a bounded smoke run such as 10 examples.
- Inspect failures and sample outputs before scaling.
- Save raw results, aggregate metrics, environment metadata, and the exact command.
- Compare only runs with compatible task versions, prompts, few-shot settings, and inference parameters.
Use uv run for Python evaluation environments. Run remote evaluation through the huggingface-jobs workflow only after confirming paid hardware and timeout.
Do not cherry-pick favorable tasks, silently discard failures, or present incomparable scores in one ranking. Publishing model-card results, opening a pull request, or modifying a leaderboard is a separate external action that requires confirmation.
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 · 24 lines · 33 tokens per session scan A 212f9ea1674a
huggingface-community-evals is a skill published in the GitHub repository metaspartan/cybara (28 stars, last pushed yesterday), licensed MIT. It adds 33 tokens to every session and 305 once invoked, about $0.0002 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.
Other skills, from other repositories
deepeval
Use when discussing or working with DeepEval (the python AI evaluation framework).
hatch3r-ai-feature
Eval-driven development workflow for shipping AI features — write eval before prompt, measure, iterate, ship with caching + cost telemetry + model fallback + hallucination SLI.
kodama-verification
Define measurable success criteria and collect targeted test, build, lint, type-check, or smoke-test evidence before claiming work is complete.
prompt-evaluation-runner
Use when evaluating prompts, LLM outputs, red-team suites, or model behavior with local eval configs and safe provider/cost controls.
superpowers-test-driven-development
Use when implementing any feature or bugfix, before writing implementation code.
ecc-cost-aware-llm-pipeline
Cost optimization patterns for LLM API usage — model routing by task complexity, budget tracking, retry logic, and prompt caching. Use when LLM spend needs to come down, or when routing tasks across model tiers and budgets.