Overview

Overview

This public intake copy packages

plugins/antigravity-awesome-skills-claude/skills/hugging-face-community-evals

https://github.com/sickn33/antigravity-awesome-skills

Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.

This intake keeps the copied upstream files intact and uses

metadata.json

ORIGIN.md

Overview

Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: Option A: inspect-ai with local inference providers path, Option B: inspect-ai on Local GPU, Option C: lighteval on Local GPU, Limitations.

When to Use This Skill

Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.

• inspect-ai with local inference
• lighteval with local inference
• choosing between vllm, Hugging Face Transformers, and accelerate
• smoke tests, task selection, and backend fallback strategy
• Hugging Face Jobs orchestration
• model-card or model-index edits

Operating Table

metadata.json

ORIGIN.md

examples/.env.example

examples/USAGE_EXAMPLES.md

## Related Skills

Workflow

This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.

1. Confirm the user goal, the scope of the imported workflow, and whether this skill is still the right router for the task.
2. Read the overview and provenance files before loading any copied upstream support files.
3. Load only the references, examples, prompts, or scripts that materially change the outcome for the current request.
4. Execute the upstream workflow while keeping provenance and source boundaries explicit in the working notes.
5. Validate the result against the upstream expectations and the evidence you can point to in the copied files.
6. Escalate or hand off to a related skill when the work moves out of this imported workflow's center of gravity.
7. Before merge or closure, record what was used, what changed, and what the reviewer still needs to verify.

Imported Workflow Notes

Imported: Option A: inspect-ai with local inference providers path

Best when the model is already supported by Hugging Face Inference Providers and you want the lowest local setup overhead.

uv run scripts/inspect_eval_uv.py \
  --model meta-llama/Llama-3.2-1B \
  --task mmlu \
  --limit 20

Use this path when:

• you want a quick local smoke test
• you do not need direct GPU control
• the task already exists in

  inspect-evals

Examples

Example 1: Ask for the upstream workflow directly

Use @hugging-face-community-evals to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.

Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.

Example 2: Ask for a provenance-grounded review

Review @hugging-face-community-evals against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.

Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.

Example 3: Narrow the copied support files before execution

Use @hugging-face-community-evals for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.

Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.

Example 4: Build a reviewer packet

Review @hugging-face-community-evals using the copied upstream files plus provenance, then summarize any gaps before merge.

Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.

Best Practices

Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.

• Keep the imported skill grounded in the upstream repository; do not invent steps that the source material cannot support.
• Prefer the smallest useful set of support files so the workflow stays auditable and fast to review.
• Keep provenance, source commit, and imported file paths visible in notes and PR descriptions.
• Point directly at the copied upstream files that justify the workflow instead of relying on generic review boilerplate.
• Treat generated examples as scaffolding; adapt them to the concrete task before execution.
• Route to a stronger native skill when architecture, debugging, design, or security concerns become dominant.

Troubleshooting

Problem: The operator skipped the imported context and answered too generically

Symptoms: The result ignores the upstream workflow in

plugins/antigravity-awesome-skills-claude/skills/hugging-face-community-evals

metadata.json

ORIGIN.md

Problem: The imported workflow feels incomplete during review

Symptoms: Reviewers can see the generated

SKILL.md

Problem: The task drifted into a different specialization

Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better. Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.

Related Skills

• @github-issue-creator

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @github-workflow-automation

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @gitlab-automation

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @gitlab-ci-patterns

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

Additional Resources

Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.

references

references/n/a

examples

examples/.env.example

scripts

scripts/inspect_eval_uv.py

agents

agents/n/a

assets

assets/n/a

• .env.example
• USAGE_EXAMPLES.md
• inspect_eval_uv.py
• inspect_vllm_uv.py
• lighteval_vllm_uv.py
• .env.example

Imported Reference Notes

Imported: Option B: inspect-ai on Local GPU

Best when you need to load the Hub model directly, use

vllm

Local GPU:

uv run scripts/inspect_vllm_uv.py \
  --model meta-llama/Llama-3.2-1B \
  --task gsm8k \
  --limit 20

Transformers fallback:

uv run scripts/inspect_vllm_uv.py \
  --model microsoft/phi-2 \
  --task mmlu \
  --backend hf \
  --trust-remote-code \
  --limit 20

Imported: Option C: lighteval on Local GPU

Best when the task is naturally expressed as a

lighteval

Local GPU:

uv run scripts/lighteval_vllm_uv.py \
  --model meta-llama/Llama-3.2-3B-Instruct \
  --tasks "leaderboard|mmlu|5,leaderboard|gsm8k|5" \
  --max-samples 20 \
  --use-chat-template

accelerate

uv run scripts/lighteval_vllm_uv.py \
  --model microsoft/phi-2 \
  --tasks "leaderboard|mmlu|5" \
  --backend accelerate \
  --trust-remote-code \
  --max-samples 20

Remote Execution Boundary

This skill intentionally stops at local execution and backend selection.

If the user wants to:

• run these scripts on Hugging Face Jobs
• pick remote hardware
• pass secrets to remote jobs
• schedule recurring runs
• inspect / cancel / monitor jobs

then switch to the

hugging-face-jobs

Task Selection

inspect-ai

• mmlu

• gsm8k

• hellaswag

• arc_challenge

• truthfulqa

• winogrande

• humaneval

lighteval

suite|task|num_fewshot

• leaderboard|mmlu|5

• leaderboard|gsm8k|5

• leaderboard|arc_challenge|25

• lighteval|hellaswag|0

Multiple

lighteval

--tasks

Backend Selection

• Prefer

  inspect_vllm_uv.py --backend vllm

  for fast GPU inference on supported architectures.
• Use

  inspect_vllm_uv.py --backend hf

  when

  vllm

  does not support the model.
• Prefer

  lighteval_vllm_uv.py --backend vllm

  for throughput on supported models.
• Use

  lighteval_vllm_uv.py --backend accelerate

  as the compatibility fallback.
• Use

  inspect_eval_uv.py

  when Inference Providers already cover the model and you do not need direct GPU control.

Hardware Guidance

< 3B

3B - 13B

13B+

hugging-face-jobs

For smoke tests, prefer cheaper local runs plus

--limit

--max-samples

Troubleshooting

• CUDA or vLLM OOM:
  • reduce

    --batch-size

  • reduce

    --gpu-memory-utilization

  • switch to a smaller model for the smoke test
  • if necessary, hand off to

    hugging-face-jobs

• Model unsupported by

  vllm

  :
  • switch to

    --backend hf

    for

    inspect-ai

  • switch to

    --backend accelerate

    for

    lighteval

• Gated/private repo access fails:
  • verify

    HF_TOKEN

• Custom model code required:
  • add

    --trust-remote-code

Examples

See:

• examples/USAGE_EXAMPLES.md

  for local command patterns

• scripts/inspect_eval_uv.py

• scripts/inspect_vllm_uv.py

• scripts/lighteval_vllm_uv.py

Imported: Limitations

• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.