Agent Builder
shareAI-lab/learn-claude-code
Design and build AI agents for any domain. An agent skill from shareAI-lab/learn-claude-code.
Write Harbor task verifiers using Reward Kit. An agent skill from Arize-ai/phoenix.
Install Rewardkit
$ npx skills add Arize-ai/phoenix --skill rewardkit -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install Arize-ai/phoenix rewardkit --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/Arize-ai/phoenix.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/rewardkit .claude/skills/rewardkit && 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 "rewardkit" agent skill from https://github.com/Arize-ai/phoenix/tree/main/.agents/skills/rewardkit into .claude/skills/rewardkit/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "rewardkit", 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/Arize-ai/phoenix/tree/main/.agents/skills/rewardkitType 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 Arize-ai/phoenix --skill rewardkit -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install Arize-ai/phoenix rewardkit --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Arize-ai/phoenix.git skills-src && mkdir -p .agents/skills && cp -r skills-src/.agents/skills/rewardkit .agents/skills/rewardkit && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "rewardkit" agent skill from https://github.com/Arize-ai/phoenix/tree/main/.agents/skills/rewardkit into .agents/skills/rewardkit/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "rewardkit", 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 Arize-ai/phoenix --skill rewardkit -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install Arize-ai/phoenix rewardkit --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Arize-ai/phoenix.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/.agents/skills/rewardkit .cursor/skills/rewardkit && 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 "rewardkit" agent skill from https://github.com/Arize-ai/phoenix/tree/main/.agents/skills/rewardkit into .cursor/skills/rewardkit/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "rewardkit", 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/Arize-ai/phoenix.git --path .agents/skills/rewardkit--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 Arize-ai/phoenix --skill rewardkit -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install Arize-ai/phoenix rewardkit --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Arize-ai/phoenix.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/.agents/skills/rewardkit .gemini/skills/rewardkit && 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 "rewardkit" agent skill from https://github.com/Arize-ai/phoenix/tree/main/.agents/skills/rewardkit into .gemini/skills/rewardkit/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "rewardkit", 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 Arize-ai/phoenix rewardkitInstalls 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 Arize-ai/phoenix --skill rewardkit -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/Arize-ai/phoenix.git skills-src && mkdir -p .github/skills && cp -r skills-src/.agents/skills/rewardkit .github/skills/rewardkit && 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 "rewardkit" agent skill from https://github.com/Arize-ai/phoenix/tree/main/.agents/skills/rewardkit into .github/skills/rewardkit/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "rewardkit", 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 Arize-ai/phoenix --skill rewardkit -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install Arize-ai/phoenix rewardkit --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Arize-ai/phoenix.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/.agents/skills/rewardkit .opencode/skills/rewardkit && 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 "rewardkit" agent skill from https://github.com/Arize-ai/phoenix/tree/main/.agents/skills/rewardkit into .opencode/skills/rewardkit/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "rewardkit", 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
rewardkit- GitHub stars
- 12k
- Token cost
- ~3k tokens
- SKILL.md length
- 1,127 words
- Files
- 1
- Skills in repo
- 39
- Repo updated
- First seen
- Licence
- Custom licence
At a glance
Write Harbor task verifiers using Reward Kit. An agent skill from Arize-ai/phoenix.
- Editing a tasks tests/ directory
- SKILL.md covers Setup in a Harbor task, Programmatic criteria, Custom criteria and Judge criteria (LLM or…, plus 5 more sections
- Calls uvx and uv; needs TYPESAFE_API_KEY and ANTHROPIC_API_KEY
- Adding grading criteria
What it does
Rewardkit is an agent skill from Arize-ai/phoenix. Write Harbor task verifiers using Reward Kit. Use when creating or editing a task's tests/ directory, adding grading criteria, setting up LLM/agent judges, or designing verifiers that produce a reward score.
Its SKILL.md is about 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. The repository describes itself as: AI Observability & Evaluation.
When your agent uses it
- Editing a tasks tests/ directory
- Adding grading criteria
- Setting up LLM/agent judges
- Designing verifiers that produce a reward score
Example prompts
- “/rewardkit”
Requirements
- Python 3
- A credential in ANTHROPIC_API_KEY
- A credential in TYPESAFE_API_KEY
What it can do on your machine
Read from SKILL.md and the folder at commit 52f76fc. 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
Shell commands in SKILL.md call:
uvxuv
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md. Its commands use uvx and uv, which can reach the network depending on how they are called.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names these keys or tokens, usually read from environment variables:
TYPESAFE_API_KEYANTHROPIC_API_KEY
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Rewardkit loads about 3k tokens when it runs. Until then it costs about 54 tokens; SKILL.md has 1,127 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 1,127 words (~2,960 tokens).
“Help the user write task verifiers with Reward Kit. Reward Kit is a lightweight Python package that turns a directory of criteria files into a reward score. Each criterion is a Python function call or a TOML judge file; folders…”
- name
- rewardkit
Files
Just SKILL.md in .agents/skills/rewardkit of Arize-ai/phoenix.
Open the folder on GitHubat commit 52f76fc
Compare with similar skills
Rewardkit 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 |
|---|---|---|---|---|---|---|
| Rewardkit this skillArize-ai/phoenix | 12k | — | ~3k | Automated safety check: Pass | Custom licence | |
| Agent BuildershareAI-lab/learn-claude-code | 78k | 5 repos | ~1.2k | Automated safety check: Pass | MIT | |
| Add Uint Supportpytorch/pytorch | 104k | 2 repos | ~2.3k | 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 | |
| Segment Anything Model GuideOrchestra-Research/AI-Research-SKILLs | 13k | 8 repos | ~3.3k | Automated safety check: Pass | MIT | |
| 1passwordtrpc-group/trpc-agent-go | 1.9k | 14 repos | ~656 | Automated safety check: Pass | Apache-2.0 |
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Guide to using Meta's Segment Anything Model for zero-shot image segmentation with point, box or mask prompts, or automatic mask generation.
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Related
Categories
Questions about Rewardkit
What does Rewardkit do?
Write Harbor task verifiers using Reward Kit. An agent skill from Arize-ai/phoenix. Rewardkit is an agent skill from Arize-ai/phoenix. Write Harbor task verifiers using Reward Kit.
When should I use Rewardkit?
Rewardkit fits situations like: editing a tasks tests/ directory; adding grading criteria; setting up LLM/agent judges; designing verifiers that produce a reward score.
How do I install Rewardkit in Claude Code?
Run `npx skills add Arize-ai/phoenix --skill rewardkit -a claude-code`. Or copy the skill folder (.agents/skills/rewardkit in Arize-ai/phoenix) into .claude/skills/rewardkit in your project. Claude Code loads it when a task matches its description.
How do I install Rewardkit in Codex?
Run `npx skills add Arize-ai/phoenix --skill rewardkit -a codex`. Or copy the skill folder (.agents/skills/rewardkit in Arize-ai/phoenix) into .agents/skills/rewardkit in your project. Codex loads it when a task matches its description.
Can I use Rewardkit 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 Arize-ai/phoenix --skill rewardkit -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/rewardkit, .gemini/skills/rewardkit, .github/skills/rewardkit and .opencode/skills/rewardkit in your project.
What does Rewardkit need to run?
Going by SKILL.md and its folder, Rewardkit needs the command-line tools its instructions call (uvx and uv) and credentials named TYPESAFE_API_KEY and ANTHROPIC_API_KEY. Our summary lists: Python 3; A credential in ANTHROPIC_API_KEY; A credential in TYPESAFE_API_KEY.
Does Rewardkit access the network?
SKILL.md contains no URLs. Its commands use uvx and uv, which can reach the network depending on how they are called. This is read from the text; nothing was executed.
Is Rewardkit 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 Rewardkit use?
Rewardkit 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 Rewardkit use?
About 3k tokens (SKILL.md is roughly 12k 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 Rewardkit?
Skills that share tags, products or a category with Rewardkit: Agent Builder (shareAI-lab/learn-claude-code, 78k stars), Add Uint Support (pytorch/pytorch, 104k stars), LLM Benchmarking with lm-evaluation-harness (Orchestra-Research/AI-Research-SKILLs, 13k stars) and Segment Anything Model Guide (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 Rewardkit?
Arize-ai (a GitHub organization) maintains it in Arize-ai/phoenix, which has 11,764 GitHub stars. The repository holds 39 skills in this directory. The repository was last updated on October 9, 2026.
Source: Arize-ai/phoenix on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.