LLM Benchmarking with lm-evaluation-harness
Orchestra-Research/AI-Research-SKILLs
Runs lm-evaluation-harness to benchmark language models on academic suites such as MMLU, GSM8K and HumanEval, compare models and track training checkpoints.
Evaluate any output file against a structured evals.yaml assertions file and produce a score report with per-assertion pass/fail results.
Install Eval Run
$ npx skills add digipulse-engineering/GAAI-framework --skill eval-run -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install digipulse-engineering/GAAI-framework eval-run --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/digipulse-engineering/GAAI-framework.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.gaai/core/skills/cross/eval-run .claude/skills/eval-run && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "eval-run" agent skill from https://github.com/digipulse-engineering/GAAI-framework/tree/main/.gaai/core/skills/cross/eval-run into .claude/skills/eval-run/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "eval-run", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/digipulse-engineering/GAAI-framework/tree/main/.gaai/core/skills/cross/eval-runType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add digipulse-engineering/GAAI-framework --skill eval-run -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install digipulse-engineering/GAAI-framework eval-run --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/digipulse-engineering/GAAI-framework.git skills-src && mkdir -p .agents/skills && cp -r skills-src/.gaai/core/skills/cross/eval-run .agents/skills/eval-run && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "eval-run" agent skill from https://github.com/digipulse-engineering/GAAI-framework/tree/main/.gaai/core/skills/cross/eval-run into .agents/skills/eval-run/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "eval-run", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add digipulse-engineering/GAAI-framework --skill eval-run -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install digipulse-engineering/GAAI-framework eval-run --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/digipulse-engineering/GAAI-framework.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/.gaai/core/skills/cross/eval-run .cursor/skills/eval-run && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "eval-run" agent skill from https://github.com/digipulse-engineering/GAAI-framework/tree/main/.gaai/core/skills/cross/eval-run into .cursor/skills/eval-run/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "eval-run", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/digipulse-engineering/GAAI-framework.git --path .gaai/core/skills/cross/eval-run--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add digipulse-engineering/GAAI-framework --skill eval-run -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install digipulse-engineering/GAAI-framework eval-run --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/digipulse-engineering/GAAI-framework.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/.gaai/core/skills/cross/eval-run .gemini/skills/eval-run && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "eval-run" agent skill from https://github.com/digipulse-engineering/GAAI-framework/tree/main/.gaai/core/skills/cross/eval-run into .gemini/skills/eval-run/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "eval-run", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install digipulse-engineering/GAAI-framework eval-runInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add digipulse-engineering/GAAI-framework --skill eval-run -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/digipulse-engineering/GAAI-framework.git skills-src && mkdir -p .github/skills && cp -r skills-src/.gaai/core/skills/cross/eval-run .github/skills/eval-run && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "eval-run" agent skill from https://github.com/digipulse-engineering/GAAI-framework/tree/main/.gaai/core/skills/cross/eval-run into .github/skills/eval-run/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "eval-run", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add digipulse-engineering/GAAI-framework --skill eval-run -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install digipulse-engineering/GAAI-framework eval-run --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/digipulse-engineering/GAAI-framework.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/.gaai/core/skills/cross/eval-run .opencode/skills/eval-run && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "eval-run" agent skill from https://github.com/digipulse-engineering/GAAI-framework/tree/main/.gaai/core/skills/cross/eval-run into .opencode/skills/eval-run/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "eval-run", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
eval-run- GitHub stars
- 163
- Token cost
- ~1.7k tokens
- SKILL.md length
- 633 words
- Files
- 2 (incl. references)
- Skills in repo
- 55
- Repo updated
- First seen
- Licence
- Custom licence
At a glance
Evaluate any output file against a structured evals.yaml assertions file and produce a score report with per-assertion pass/fail results.
- Works in 4 steps: Load inputs → Run code assertions → Run llm-judge assertions → …
- Tasks that involve LLM evaluation
- SKILL.md covers Purpose / When to Activate, Process, Quality Checks and Outputs, plus 1 more section
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Eval Run is an agent skill from digipulse-engineering/GAAI-framework. Evaluate any output file against a structured evals.yaml assertions file and produce a score report with per-assertion pass/fail results. Activate when the Discovery Agent runs the Skill Optimize protocol to measure output quality or detect regressions after skill instruction changes.
Its SKILL.md is about 1.7k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files, including reference files (for example `references/evals-format.md`). Compatibility notes: Works with any filesystem-based AI coding agent
It sits in AI & LLM Engineering, covering LLM evaluation. The repository describes itself as: Turns AI coding tools into reliable software delivery systems. Drop a .gaai/ folder into any project — Discovery defines what to build, Delivery executes autonomously until…
When your agent uses it
- Tasks that involve LLM evaluation
Example prompts
- “/eval-run”
Requirements
- Compatibility (from SKILL.md): Works with any filesystem-based AI coding agent
Workflow steps
4 steps, taken from the step headings in SKILL.md.
- Load inputs
- Run code assertions
- Run llm-judge assertions
- Compile score report
What it can do on your machine
Read from SKILL.md and the folder at commit a26ea7a. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md (its code samples are markdown and yaml).
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Compatibility
Works with any filesystem-based AI coding agent
From compatibility in the SKILL.md frontmatter.
Context cost
Eval Run loads about 1.7k tokens when it runs, and up to ~3.9k if it reads all its reference files. Until then it costs about 74 tokens; SKILL.md has 633 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
Its licence (Custom licence) doesn't allow us to republish the file, so here is its outline and opening line. It has 633 words (~1,738 tokens).
- name
- eval-run
- compatibility
- Works with any filesystem-based AI coding agent
- license
- ELv2
- metadata.author
- gaai-framework
- metadata.version
- 1.0
- metadata.category
- cross
- metadata.track
- cross-cutting
- metadata.id
- SKILL-CRS-025
- metadata.updated_at
- 2026-03-15
- metadata.status
- experimental
- outputs
- score report (YAML or structured Markdown) with per-assertion pass/fail, total score, and failed assertion details
Files
SKILL.md and 1 other file (references) in .gaai/core/skills/cross/eval-run of digipulse-engineering/GAAI-framework.
- SKILL.md
- references/evals-format.md
Open the folder on GitHubat commit a26ea7a
Compare with similar skills
Eval Run next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Eval Run this skilldigipulse-engineering/GAAI-framework | 163 | — | ~1.7k | Automated safety check: Pass | Custom licence | |
| LLM Benchmarking with lm-evaluation-harnessOrchestra-Research/AI-Research-SKILLs | 13k | 8 repos | ~3k | Automated safety check: Pass | MIT | |
| Azure AI Projects Python SDKmicrosoft/skills | 3.1k | 6 repos | ~2.8k | Automated safety check: Pass | MIT | |
| Fine-Tuning ExpertJeffallan/claude-skills | 12k | 1 repos | ~1.7k | Automated safety check: Pass | MIT | |
| Looperksimback/looper | 710 | — | ~2.7k | Automated safety check: Notes | MIT | |
| Hugging Face Local Model Evalshuggingface/skills | 11k | 2 repos | ~1.6k | Automated safety check: Pass | Apache-2.0 |
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Fine-Tuning Expert
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Guides LLM fine-tuning with LoRA and QLoRA through Hugging Face PEFT, from dataset validation and training checks to adapter merging, quantization and deployment.
Looper
ksimback/looper
Scaffold a well-designed agent loop with best-practice coaching and a cross-model review council.
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Hugging Face Local Model Evals
huggingface/skills
Runs evaluations of Hugging Face Hub models on local hardware with inspect-ai or lighteval, and helps choose between vLLM, Transformers and accelerate backends.
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Builds agent evaluations in stages: inspect the repository and traces, agree a Task Spec with you, then build, audit and run a Harbor task with an independent verifier.
More from digipulse-engineering/GAAI-framework
All 55 skills in this repoPattern Transfer
digipulse-engineering/GAAI-framework
Discover structurally similar patterns across domains, assess transfer viability via structural invariant checking, and propose domain adaptations with risk gates.
Skill Optimize
digipulse-engineering/GAAI-framework
Run a structured evaluate-analyze-improve cycle on any GAAI skill to measure quality, detect regressions, and propose targeted improvements.
Abort Safe Handler
digipulse-engineering/GAAI-framework
Orchestrator-level Stage 4 entry gate for /gaai:bootstrap. An agent skill from digipulse-engineering/GAAI-framework.
Create Skill
digipulse-engineering/GAAI-framework
Guide creation of a new GAAI skill following the agentskills.io spec and GAAI best practices.
Discovery High Level Plan
digipulse-engineering/GAAI-framework
Transform vague or high-level human intent into a governed Discovery action plan.
Memory Compact
digipulse-engineering/GAAI-framework
Emergency single-pass memory compression when context window pressure is high mid-task.
Related
Categories
Questions about Eval Run
What does Eval Run do?
Evaluate any output file against a structured evals.yaml assertions file and produce a score report with per-assertion pass/fail results. Eval Run is an agent skill from digipulse-engineering/GAAI-framework.yaml assertions file and produce a score report with per-assertion pass/fail results.
When should I use Eval Run?
Eval Run fits situations like: tasks that involve LLM evaluation.
How do I install Eval Run in Claude Code?
Run `npx skills add digipulse-engineering/GAAI-framework --skill eval-run -a claude-code`. Or copy the skill folder (.gaai/core/skills/cross/eval-run in digipulse-engineering/GAAI-framework) into .claude/skills/eval-run in your project. Claude Code loads it when a task matches its description.
How do I install Eval Run in Codex?
Run `npx skills add digipulse-engineering/GAAI-framework --skill eval-run -a codex`. Or copy the skill folder (.gaai/core/skills/cross/eval-run in digipulse-engineering/GAAI-framework) into .agents/skills/eval-run in your project. Codex loads it when a task matches its description.
Can I use Eval Run in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add digipulse-engineering/GAAI-framework --skill eval-run -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/eval-run, .gemini/skills/eval-run, .github/skills/eval-run and .opencode/skills/eval-run in your project.
What does Eval Run need to run?
SKILL.md names no scripts, command-line tools or credentials: Eval Run is instructions for the agent only. Compatibility (from SKILL.md): Works with any filesystem-based AI coding agent.
Does Eval Run access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Eval Run safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Eval Run use?
Eval Run has a licence file (the repository's licence) that doesn't match a standard licence. Read it on GitHub before reusing the skill.
How many tokens does Eval Run use?
About 1.7k tokens (SKILL.md is roughly 7k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 2.1k tokens, read only when the agent opens those files.
What are the alternatives to Eval Run?
Skills that share tags, products or a category with Eval Run: LLM Benchmarking with lm-evaluation-harness (Orchestra-Research/AI-Research-SKILLs, 13k stars), Azure AI Projects Python SDK (microsoft/skills, 3.1k stars), Fine-Tuning Expert (Jeffallan/claude-skills, 12k stars) and Looper (ksimback/looper, 710 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Eval Run?
digipulse-engineering (a GitHub organization) maintains it in digipulse-engineering/GAAI-framework, which has 163 GitHub stars. The repository holds 55 skills in this directory. The repository was last updated on September 29, 2026.
Source: digipulse-engineering/GAAI-framework on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.