huggingface-community-evals
Run reproducible local or remote model evaluations with inspect-ai or LightEval, bounded smoke tests, captured configurations, and comparable results.
Skill by metaspartan
Chalk Labs
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Install
Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
npx agentmarket install skill/metaspartan-huggingface-community-evals
Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.
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About
Use cases: ml
Reviews
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Permissions
What this skill can do and what it asks for, from its SKILL.md and code. Checked Oct 25, 2026.
- Tools
- No tools found
- Secrets
- None asked for
- Install scripts
- None found
Network hosts it talks to 1
codeload.github.com· found in install command
Files it touches
~/.claude/skills/huggingface-community-evals
What's inside
- Skill name
huggingface-community-evals- Description
- Run reproducible local or remote model evaluations with inspect-ai or LightEval, bounded smoke tests, captured configurations, and comparable results.
- License
- MIT
- Instructions
- 19 lines in SKILL.md · view on GitHub
Files (1)
- SKILL.md
SKILL.md (first lines)
# Hugging Face Community Evaluations Use Inspect or LightEval for models hosted on the Hub. Keep evaluation execution separate from publishing results. ## Workflow 1. Record the model ID, immutable revision, inference backend, tokenizer, chat template, dtype, quantization, generation parameters, task revision, and seed. 2. Check gated-model authentication without printing credentials. 3. Choose a backend supported by the model and hardware. Prefer vLLM for supported throughput workloads and Transformers or Accelerate as compatibility fallbacks. 4. Start with a bounded smoke run such as 10 examples. 5. Inspect failures and sample outputs before scaling. 6. Save raw results, aggregate metrics, environment metadata, and the exact command. 7. 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.
From metaspartan/cybara, MIT.
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Signals
- Stars
- 32
- Forks
- 9
- Open issues
- 0
- Last commit (this folder)
- Jul 30, 2026 (3 months ago)
- Last release
- Oct 24, 2026 (12 days ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- MIT
- Repository
- github.com/metaspartan/cybara
- Website
- cybara.ai
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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huggingface-community-evals skill. Agent Mart. https://agentmart.dev/skills/metaspartan-huggingface-community-evals (last updated 2026-10-24)