Azure AI Projects Python SDK
microsoft/skills
Reference for building on Microsoft Foundry with the azure-ai-projects Python SDK: project clients, versioned agents, evaluations, connections, datasets and indexes.
Run EvalView regression checks against golden baselines to detect regressions in AI agent behavior after code, prompt, or model changes.
Install Run Eval
$ npx skills add hidai25/eval-view --skill run-eval -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install hidai25/eval-view run-eval --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/hidai25/eval-view.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/run-eval .claude/skills/run-eval && 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 "run-eval" agent skill from https://github.com/hidai25/eval-view/tree/main/skills/run-eval into .claude/skills/run-eval/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "run-eval", 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/hidai25/eval-view/tree/main/skills/run-evalType 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 hidai25/eval-view --skill run-eval -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install hidai25/eval-view run-eval --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/hidai25/eval-view.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/run-eval .agents/skills/run-eval && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "run-eval" agent skill from https://github.com/hidai25/eval-view/tree/main/skills/run-eval into .agents/skills/run-eval/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "run-eval", 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 hidai25/eval-view --skill run-eval -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install hidai25/eval-view run-eval --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/hidai25/eval-view.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/run-eval .cursor/skills/run-eval && 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 "run-eval" agent skill from https://github.com/hidai25/eval-view/tree/main/skills/run-eval into .cursor/skills/run-eval/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "run-eval", 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/hidai25/eval-view.git --path skills/run-eval--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 hidai25/eval-view --skill run-eval -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install hidai25/eval-view run-eval --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/hidai25/eval-view.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/run-eval .gemini/skills/run-eval && 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 "run-eval" agent skill from https://github.com/hidai25/eval-view/tree/main/skills/run-eval into .gemini/skills/run-eval/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "run-eval", 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 hidai25/eval-view run-evalInstalls 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 hidai25/eval-view --skill run-eval -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/hidai25/eval-view.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/run-eval .github/skills/run-eval && 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 "run-eval" agent skill from https://github.com/hidai25/eval-view/tree/main/skills/run-eval into .github/skills/run-eval/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "run-eval", 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 hidai25/eval-view --skill run-eval -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install hidai25/eval-view run-eval --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/hidai25/eval-view.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/run-eval .opencode/skills/run-eval && 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 "run-eval" agent skill from https://github.com/hidai25/eval-view/tree/main/skills/run-eval into .opencode/skills/run-eval/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "run-eval", 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
run-eval- GitHub stars
- 137
- Token cost
- ~557 tokens
- SKILL.md length
- 272 words
- Files
- 1
- Skills in repo
- 4
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Run EvalView regression checks against golden baselines to detect regressions in AI agent behavior after code, prompt, or model changes.
- Works in 5 steps: Locate the test directory. Look for… → Run a regression check using the… → Interpret results → …
- Tasks that involve Building AI agents
- SKILL.md covers What this does, Steps, CLI equivalent and Tips
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Run Eval is an agent skill from hidai25/eval-view. Run EvalView regression checks against golden baselines to detect regressions in AI agent behavior after code, prompt, or model changes.
Its SKILL.md is about 560 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 Building AI agents. It works with Python. The repository describes itself as: Regression testing for AI agents. Snapshot behavior,diff tool calls,catch regressions in CI. Works with LangGraph, CrewAI, OpenAI, Anthropic. The licence is Apache-2.0.
When your agent uses it
- Tasks that involve Building AI agents
Example prompts
- “/run-eval”
Requirements
- Python 3
Workflow steps
5 steps, taken from the first numbered list in SKILL.md.
- Locate the test directory. Look for tests/evalview/ in the project. If it exists, use that. Otherwise check for a tests/ directory with…
- Run a regression check using the run_check MCP tool
- Interpret results
- If changes are intentional, offer to update the baseline by calling run_snapshot with an explanatory notes parameter.
- Generate a visual report (optional) by calling generate_visual_report for a detailed HTML breakdown of traces, diffs, scores, and timelines.
What it can do on your machine
Read from SKILL.md and the folder at commit 394f7d7. 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.
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.
Context cost
Run Eval loads about 557 tokens when it runs. Until then it costs about 36 tokens; SKILL.md has 272 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
The full file from hidai25/eval-view at commit 394f7d7, republished under its Apache-2.0 licence (© hidai25). 272 words, ~557 tokens.
Download SKILL.mdSave it as
.claude/skills/run-eval/SKILL.md (or your agent's skills folder).- name
- run-eval
- description
- Run EvalView regression checks against golden baselines to detect regressions in AI agent behavior after code, prompt, or model changes.
Run Eval
Use this skill after making changes to an AI agent (prompt edits, model swaps, tool changes, code refactors) to verify nothing broke.
What this does
EvalView compares current agent behavior against saved golden baselines. It runs your test cases, evaluates the outputs, and reports a diff status for each test:
- PASSED — behavior matches the baseline
- OUTPUT_CHANGED — output shifted but may be intentional
- TOOLS_CHANGED — different tools were called
- REGRESSION — score dropped significantly (blocking failure)
Steps
Locate the test directory. Look for
tests/evalview/in the project. If it exists, use that. Otherwise check for atests/directory with.yamltest files.Run a regression check using the
run_checkMCP tool:- If checking all tests: call
run_checkwith the detectedtest_path - If checking a specific test: also pass the
testparameter with the test name
- If checking all tests: call
Interpret results:
- If all tests pass, confirm to the user that no regressions were found
- If REGRESSION is reported, show the diff (score delta, tool changes, output similarity) and offer to help fix it
- If OUTPUT_CHANGED or TOOLS_CHANGED, flag it as a warning — the user should decide if the change is intentional
If changes are intentional, offer to update the baseline by calling
run_snapshotwith an explanatorynotesparameter.Generate a visual report (optional) by calling
generate_visual_reportfor a detailed HTML breakdown of traces, diffs, scores, and timelines.
CLI equivalent
evalview check tests/evalview/
evalview check tests/evalview/ --test "my-test"
evalview snapshot tests/evalview/ --notes "updated after prompt refactor"Tips
- Use
run_checkfrequently — it calls the Python API directly with no subprocess overhead. - A score delta near zero with TOOLS_CHANGED often means the agent found an equivalent path.
- Always snapshot after confirming intentional changes so future checks compare against the new baseline.
© hidai25, 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
Files
Just SKILL.md in skills/run-eval of hidai25/eval-view.
Open the folder on GitHubat commit 394f7d7
Compare with similar skills
Run Eval 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 |
|---|---|---|---|---|---|---|
| Run Eval this skillhidai25/eval-view | 137 | — | ~557 | Automated safety check: Pass | Apache-2.0 | |
| Azure AI Projects Python SDKmicrosoft/skills | 3.1k | 6 repos | ~2.8k | Automated safety check: Pass | MIT | |
| Senior Prompt Engineermaslennikov-ig/claude-code-orchestrator-kit | 260 | 4 repos | ~1.4k | Automated safety check: Pass | Custom licence | |
| Google Agents CLI Adk Codepifferologo/cloud-agents-cli | 129 | 1 repos | ~768 | Automated safety check: Pass | Apache-2.0 | |
| DSPy Language Model ProgrammingOrchestra-Research/AI-Research-SKILLs | 13k | 10 repos | ~3.8k | Automated safety check: Pass | MIT | |
| E2b Code Interpreteragent-sandbox/agent-sandbox | 218 | — | ~2.3k | Automated safety check: Pass | Apache-2.0 |
Similar skills
- Official
Senior Prompt Engineer
maslennikov-ig/claude-code-orchestrator-kit
Provides reference guides and Python scripts for prompt optimization, RAG evaluation, and agent orchestration when building or tuning LLM systems.
Google Agents CLI Adk Code
pifferologo/cloud-agents-cli
This skill should be used when the user wants to "write agent code", "build an agent with ADK", "add a tool", "create a callback", "define an agent", "use state management", or needs ADK (Agent…
DSPy Language Model Programming
Orchestra-Research/AI-Research-SKILLs
Teaches an agent to build LM pipelines, RAG systems and agents in DSPy using signatures, modules and optimizers instead of hand-tuned prompts.
E2b Code Interpreter
agent-sandbox/agent-sandbox
Execute code in E2B sandboxes and integrate with LLMs for tool calling.
- Official
Code Snippets
MicrosoftDocs/semantic-kernel-docs
How to reference code from sample repos in Agent Framework docs pages using :::code directives, snippet tags, zone pivots, and highlight attributes.
More from hidai25/eval-view
Generate Tests
hidai25/eval-view
Generate EvalView test cases — either from a SKILL.md file using LLM-powered generation, or by capturing real agent interactions through a proxy.
Procrastination Buster
hidai25/eval-view
Beat procrastination with task breakdown, 2-minute starts, and accountability tracking
Watch
hidai25/eval-view
Start EvalView watch mode to automatically re-run regression checks whenever project files change.
Related
Works with
Categories
Questions about Run Eval
What does Run Eval do?
Run EvalView regression checks against golden baselines to detect regressions in AI agent behavior after code, prompt, or model changes. Run Eval is an agent skill from hidai25/eval-view. Run EvalView regression checks against golden baselines to detect regressions in AI agent behavior after code, prompt, or model changes.
When should I use Run Eval?
Run Eval fits situations like: tasks that involve Building AI agents.
How do I install Run Eval in Claude Code?
Run `npx skills add hidai25/eval-view --skill run-eval -a claude-code`. Or copy the skill folder (skills/run-eval in hidai25/eval-view) into .claude/skills/run-eval in your project. Claude Code loads it when a task matches its description.
How do I install Run Eval in Codex?
Run `npx skills add hidai25/eval-view --skill run-eval -a codex`. Or copy the skill folder (skills/run-eval in hidai25/eval-view) into .agents/skills/run-eval in your project. Codex loads it when a task matches its description.
Can I use Run Eval 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 hidai25/eval-view --skill run-eval -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/run-eval, .gemini/skills/run-eval, .github/skills/run-eval and .opencode/skills/run-eval in your project.
What does Run Eval need to run?
SKILL.md names no scripts, command-line tools or credentials: Run Eval is instructions for the agent only. Our summary lists: Python 3.
Does Run Eval 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 Run Eval 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 Run Eval use?
Run Eval 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.
How many tokens does Run Eval use?
About 557 tokens (SKILL.md is roughly 2.2k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Run Eval?
Skills that share tags, products or a category with Run Eval: Azure AI Projects Python SDK (microsoft/skills, 3.1k stars), Senior Prompt Engineer (maslennikov-ig/claude-code-orchestrator-kit, 260 stars), Google Agents CLI Adk Code (pifferologo/cloud-agents-cli, 129 stars) and DSPy Language Model Programming (Orchestra-Research/AI-Research-SKILLs, 13k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Run Eval?
hidai25 (a GitHub user) maintains it in hidai25/eval-view, which has 137 GitHub stars. The repository holds 4 skills in this directory. The repository was last updated on September 5, 2026.
Source: hidai25/eval-view on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.