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.
Best practices for creating expectations and grader files to evaluate guidance quality.
$ npx skills add GoogleChrome/modern-web-guidance-src --skill project-evals -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install GoogleChrome/modern-web-guidance-src project-evals --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/GoogleChrome/modern-web-guidance-src.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/project-evals .claude/skills/project-evals && 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 "project-evals" agent skill from https://github.com/GoogleChrome/modern-web-guidance-src/tree/main/.agents/skills/project-evals into .claude/skills/project-evals/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "project-evals", 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/GoogleChrome/modern-web-guidance-src/tree/main/.agents/skills/project-evalsType 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 GoogleChrome/modern-web-guidance-src --skill project-evals -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install GoogleChrome/modern-web-guidance-src project-evals --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/GoogleChrome/modern-web-guidance-src.git skills-src && mkdir -p .agents/skills && cp -r skills-src/.agents/skills/project-evals .agents/skills/project-evals && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "project-evals" agent skill from https://github.com/GoogleChrome/modern-web-guidance-src/tree/main/.agents/skills/project-evals into .agents/skills/project-evals/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "project-evals", 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 GoogleChrome/modern-web-guidance-src --skill project-evals -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install GoogleChrome/modern-web-guidance-src project-evals --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/GoogleChrome/modern-web-guidance-src.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/.agents/skills/project-evals .cursor/skills/project-evals && 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 "project-evals" agent skill from https://github.com/GoogleChrome/modern-web-guidance-src/tree/main/.agents/skills/project-evals into .cursor/skills/project-evals/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "project-evals", 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/GoogleChrome/modern-web-guidance-src.git --path .agents/skills/project-evals--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 GoogleChrome/modern-web-guidance-src --skill project-evals -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install GoogleChrome/modern-web-guidance-src project-evals --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/GoogleChrome/modern-web-guidance-src.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/.agents/skills/project-evals .gemini/skills/project-evals && 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 "project-evals" agent skill from https://github.com/GoogleChrome/modern-web-guidance-src/tree/main/.agents/skills/project-evals into .gemini/skills/project-evals/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "project-evals", 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 GoogleChrome/modern-web-guidance-src project-evalsInstalls 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 GoogleChrome/modern-web-guidance-src --skill project-evals -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/GoogleChrome/modern-web-guidance-src.git skills-src && mkdir -p .github/skills && cp -r skills-src/.agents/skills/project-evals .github/skills/project-evals && 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 "project-evals" agent skill from https://github.com/GoogleChrome/modern-web-guidance-src/tree/main/.agents/skills/project-evals into .github/skills/project-evals/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "project-evals", 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 GoogleChrome/modern-web-guidance-src --skill project-evals -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install GoogleChrome/modern-web-guidance-src project-evals --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/GoogleChrome/modern-web-guidance-src.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/.agents/skills/project-evals .opencode/skills/project-evals && 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 "project-evals" agent skill from https://github.com/GoogleChrome/modern-web-guidance-src/tree/main/.agents/skills/project-evals into .opencode/skills/project-evals/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "project-evals", 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.
project-evalsBest practices for creating expectations and grader files to evaluate guidance quality.
Project Evals is an agent skill from GoogleChrome/modern-web-guidance-src. Best practices for creating expectations and grader files to evaluate guidance quality. Use this skill any time you're writing or reviewing an expectations.md or grader.ts file.
Its SKILL.md is about 2.3k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in AI & LLM Engineering, covering LLM evaluation. The licence is Apache-2.0.
3 steps, taken from the first numbered list in SKILL.md.
Read from SKILL.md and the folder at commit c312847. It shows what the files ask for, not the result of running them.
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.
Shell commands in SKILL.md call:
nodeFrom the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Project Evals loads about 2.3k tokens when it runs. Until then it costs about 49 tokens; SKILL.md has 1,311 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.
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.
The full file from GoogleChrome/modern-web-guidance-src at commit c312847, republished under its Apache-2.0 licence (© GoogleChrome). 1,311 words, ~2,325 tokens.
.claude/skills/project-evals/SKILL.md (or your agent's skills folder).This is the third of three stages in creating guidance:
Real-world coding agents see only guide.md — retrieved automatically via the RAG skills system when a developer asks for help. Every other file in a use case directory is eval infrastructure.
The eval harness runs a separate coding agent in a controlled environment to test whether the guidance works. This eval agent receives the first prompt from tasks/task.md and has access to guide.md via the same RAG system. The harness then runs grader.ts against the eval agent's output.
None of the following are ever seen by real-world coding agents:
| File | Role in eval pipeline |
|---|---|
tasks/task.md | Simulated developer prompts and base application name fed to the eval agent by the harness |
demo.html | Reference implementation — grader runs against it to confirm tests pass on correct code |
negative-demo.html | Anti-example — grader runs against it to confirm tests fail on incorrect code |
expectations.md | Spec used to generate grader.ts |
grader.ts | Playwright tests run against the eval agent's output |
tasks/task.md, expectations.md, and grader.ts form a tightly coupled pipeline:
tasks/task.md — Simulated developer prompts used only by the eval harness. It must start with a YAML frontmatter specifying the base_app, followed by a list of prompts. Each prompt should sound like a real developer request, without naming specific APIs or best practices — the eval agent is expected to discover those by reading guide.md via RAG. The first prompt is the most important: it is used as the default task.
expectations.md — The ground truth for what a correct implementation looks like. Each bullet becomes exactly one test in grader.ts. Write expectations assuming the eval agent read guide.md and implemented it faithfully; they describe the observable output, not the implementation approach.
grader.ts — A Playwright test file generated 1:1 from expectations.md. Every bullet maps to one test() block. If an expectation cannot be translated into a Playwright assertion (static file check or browser automation), it does not belong in expectations.md.
expectations.mdWrite a natural language, bulleted list of assertions that must be true if an agent implements the guide.md correctly (e.g., "The input element is styled with a red border only AFTER a blur event").
MUST, MUST NOT, DO, and DO NOT are a guide.md convention for steering coding agents; expectations.md is consumed only by the internal grader generator, so the The implementation MUST… boilerplate adds nothing and should be omitted.demo.html. Expectations that aren't covered by the demo lead to unreliable grader calibration.grader.ts) live within their respective guide folders. These are Playwright test files.str.includes() on fs.readFileSync) to test CSS or HTML syntax whenever possible. These are extremely brittle and will fail if the agent uses a different class name, semantic element, or formatting.element.evaluate((el) => window.getComputedStyle(el).propertyName) to robustly verify that the browser is rendering the feature correctly, regardless of how the agent authored the code..ts file if the generator struggles to get it perfectly tailored.Once a guide has its guide.md, demo.html, and expectations.md completely written, it is ready for the evaluation pipeline.
To generate the eval graders, use the gd dev tool.
Run the following command:
node ./bin/gd.ts dev <path-to-guide-directory>This command will automatically:
negative-demo.html based on the guidance.grader.ts Playwright test that asserts your expectations.md against both demo.html (should pass) and negative-demo.html (should fail).tasks/task.mdtasks/task.md contains realistic developer prompts used to run AI agents end-to-end against the guide's grader, prefixed by a YAML frontmatter specifying the base application.
Format:
---
base_app: daily-grind
---
- make my images load faster on the page
- Optimize the priority of my LCP image 'hero.jpg' and deprioritize the gallery images below the fold.Critical: The first prompt is the most important. It is used as the default task for the harness, and it must be specific enough to produce a grader-testable result.
Rules:
hero.jpg, /api/analytics).[!IMPORTANT] Functional Locators vs. Technical Solutions It is completely acceptable (and sometimes necessary) to mandate specific DOM IDs or CSS classes (e.g.,
"add a .fan-card class") if the grader requires them to locate elements. What is strictly banned is mandating the underlying implementation technology (e.g., commanding the model to"use sibling-index()"or"use the Temporal API").
Quantity: 1–4 prompts is typical. A single highly specific prompt is fine for technical use cases. Multiple prompts are useful for use cases with multiple valid entry points (e.g., "accordion", "tabs", "drawer" all exercising the same feature).
Test your prompts: Before finalizing, ask yourself: would an agent reading this prompt understand what they need to build? Vague phrases like "I should be able to search" may not convey browser-native "Find in page" behavior to a model. If the prompt is ambiguous, rewrite it to make the intent explicit.
Consistency: If writing multiple prompts, consider starting them with the same verb or structure (e.g., all starting with "Create a...") to make the list scannable and consistent.
If gd dev fails to calibrate the grader:
expectations.md so the generated grader is more accurate, or simply run gd dev again (it attempts to fix itself using failure context).© GoogleChrome, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Just SKILL.md in .agents/skills/project-evals of GoogleChrome/modern-web-guidance-src.
Open the folder on GitHubat commit c312847
Project Evals 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 |
|---|---|---|---|---|---|---|
| Project Evals this skillGoogleChrome/modern-web-guidance-src | 1.1k | — | ~2.3k | Automated safety check: Pass | Apache-2.0 | |
| 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 |
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.
microsoft/skills
Reference for building on Microsoft Foundry with the azure-ai-projects Python SDK: project clients, versioned agents, evaluations, connections, datasets and indexes.
Jeffallan/claude-skills
Guides LLM fine-tuning with LoRA and QLoRA through Hugging Face PEFT, from dataset validation and training checks to adapter merging, quantization and deployment.
ksimback/looper
Scaffold a well-designed agent loop with best-practice coaching and a cross-model review council.
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.
langchain-ai/langchain-skills
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.
GoogleChrome/modern-web-guidance-src
Downloads and analyzes the latest three distinct nightly evaluation runs (Claude Code, Codex CLI, and Jetski CLI) from the GCS remote dashboard to identify and flag unhealthy or low-performing tasks…
GoogleChrome/modern-web-guidance-src
Build and publish Chrome Extensions using Manifest V3 best practices.
GoogleChrome/modern-web-guidance-src
Run a document coherence, link integrity, and git repository status audit across repository markdown files using a dedicated subagent.
GoogleChrome/modern-web-guidance-src
Action-oriented guidelines for privacy by design, data minimization, third-party audits, and modern browser privacy APIs.
GoogleChrome/modern-web-guidance-src
Coding style, architectural conventions, and PR review standards for the modern-web-guidance-src (guidance) repository.
GoogleChrome/modern-web-guidance-src
Workflow for refactoring discipline-level guides (e.g., JavaScript, CSS) to remove "Common Knowledge" by generating and comparing against model-specific "Knowledge Mirrors".
Categories
Best practices for creating expectations and grader files to evaluate guidance quality. Project Evals is an agent skill from GoogleChrome/modern-web-guidance-src. Best practices for creating expectations and grader files to evaluate guidance quality.
Project Evals fits situations like: tasks that involve LLM evaluation.
Run `npx skills add GoogleChrome/modern-web-guidance-src --skill project-evals -a claude-code`. Or copy the skill folder (.agents/skills/project-evals in GoogleChrome/modern-web-guidance-src) into .claude/skills/project-evals in your project. Claude Code loads it when a task matches its description.
Run `npx skills add GoogleChrome/modern-web-guidance-src --skill project-evals -a codex`. Or copy the skill folder (.agents/skills/project-evals in GoogleChrome/modern-web-guidance-src) into .agents/skills/project-evals in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add GoogleChrome/modern-web-guidance-src --skill project-evals -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/project-evals, .gemini/skills/project-evals, .github/skills/project-evals and .opencode/skills/project-evals in your project.
Going by SKILL.md and its folder, Project Evals needs the command-line tools its instructions call (node).
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.
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.
Project Evals is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 2.3k tokens (SKILL.md is roughly 9.3k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Project Evals: 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.
GoogleChrome (a GitHub organization) maintains it in GoogleChrome/modern-web-guidance-src, which has 1,134 GitHub stars. The repository holds 14 skills in this directory. The repository was last updated on October 7, 2026.
Source: GoogleChrome/modern-web-guidance-src on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.