# Skill Upper > Set up, run, and interpret Agent Skill evaluations (evals) with the skill-up CLI / 使用 skill-up CLI 给 Agent Skill 搭建和运行评测. Use when the user asks to evaluate, test, regress, or verify a Skill; add evals or cases; write eval.yaml/case.yaml; run skill-up run/validate/list-cases/report/import/init; or migrate from Anthropic evals.json. Handles Skill discovery, evals scaffolding, judge authoring, cred… - **Type:** Skill - **Install:** `agentstack add skill-alibaba-skill-up-skill-upper` - **Verified:** Pending review - **Seller:** [alibaba](https://agentstack.voostack.com/s/alibaba) - **Installs:** 0 - **Category:** [AI & ML](https://agentstack.voostack.com/c/ai-and-ml) - **Latest version:** 0.1.0 - **License:** Apache-2.0 - **Upstream author:** [alibaba](https://github.com/alibaba) - **Source:** https://github.com/alibaba/skill-up/tree/main/skills/skill-upper - **Website:** https://alibaba.github.io/skill-up/ ## Install ```sh agentstack add skill-alibaba-skill-up-skill-upper ``` Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent. ## About # use-skill-up-cli Help the user set up, run, and interpret evaluations for Agent Skills via the `skill-up` CLI. Manual: ## Language Policy **Default to English when responding to the user. If the user writes in Chinese (or any other language), switch to that language and stay consistent with the user's input throughout the session.** Detection rules (highest priority first): 1. The user explicitly specifies a language in the current message (e.g. "answer in English" / "用中文回答") → follow the user's instruction. 2. The natural language used in the user's current message → match it. 3. None of the above → use English (default). Regardless of the response language, technical identifiers in this SKILL — CLI commands, `eval.yaml` / `case.yaml` field names, report field names, etc. — MUST stay in their original English form. Do not translate them. ### Language Rules for Generated Artifacts When creating or editing `eval.yaml`, `case.yaml`, grading scripts, README snippets, final replies, or any other user-visible artifact, treat the language of the user's current message as the output language for this turn: - If the user asks in Chinese, write the final response and all generated natural-language content in Chinese, including YAML comments, `title`, `description`, `input.prompt`, `expect` keywords, and `judge.criteria`. - If the user asks in English, write the final response and all generated natural-language content in English, including YAML comments, `title`, `description`, `input.prompt`, `expect` keywords, and `judge.criteria`; do not leave Chinese or CJK characters in generated case files. - If the target Skill itself is written in Chinese but the user asks in English, translate the Skill's functional intent into English test prompts and assertions instead of copying Chinese prose from the target Skill or templates. - In an English context, deterministic keywords in `rule_based` cases, including `expect.must_contain` and `judge.success.output_contains`, must also be English keywords. Translate terms such as `资源泄漏`, `关闭`, and `异常处理` into `resource leak`, `close`, and `exception handling`; do not write bilingual parentheticals like `"资源" (resources)`. - Keep technical identifiers unchanged, such as `schema_version`, `environment.type`, `engine.name`, `rule_based`, `agent_judge`, `script_path`, file paths, and commands. - Treat `assets/*.tmpl` as structural references only. Rewrite placeholder prose and comments into the current output language; in an English context, translate or remove every Chinese comment and Chinese placeholder before writing generated files. - In an English context, after generating all files but BEFORE submitting the final reply, you **MUST perform a CJK self-check**: open every `evals/cases/*.yaml` and `evals/eval.yaml` and scan for CJK characters (Unicode ranges `\u4e00-\u9fff\u3400-\u4dbf\uf900-\ufaff\u3000-\u303f\uff00-\uffef`), including but not limited to `title`, `description`, `input.prompt`, `expect` keywords, `judge.criteria`, and YAML comments. If any CJK character is found, **replace it with an equivalent English expression before finishing the task**. This step is mandatory and must not be skipped. ## What is skill-up `skill-up` is an evaluation CLI for Agent Skill authors. It installs the Skill into a real Agent Engine (Claude Code, Codex, qodercli, etc.), spins up an execution environment for each case, runs the prompt, then grades the result via declared rules / LLM judges / custom scripts, and finally produces a report. Typical layout: ``` my-skill/ SKILL.md evals/ eval.yaml cases/ .yaml fixtures/ ``` ## When to trigger Use this skill in any of the following situations: - The user asks to "run / evaluate / verify / test this skill". - The user wants to "add evals, test cases, or regression cases to a skill". - The user wants to edit `eval.yaml` / `case.yaml`, or asks you to choose an appropriate `judge` type. - The user mentions `skill-up run/validate/list-cases/report/import/init`. - The user wants to migrate from Anthropic `evals.json` to skill-up. - The current working directory contains `evals/eval.yaml` or `evals/evals.json` and the user wants to run it. ## Main flow (follow this order strictly) ### Step 0: Make sure skill-up is installed Before doing anything, verify `skill-up` is available: ```bash command -v skill-up && skill-up --version ``` If a version is printed, continue. If you see `command not found`, on **macOS / Linux**: ```bash curl -fsSL https://raw.githubusercontent.com/alibaba/skill-up/main/install.sh | bash export SKILL_UP_VERSION=v0.1.0 curl -fsSL https://raw.githubusercontent.com/alibaba/skill-up/main/install.sh | bash export INSTALL_DIR="$HOME/bin" curl -fsSL https://raw.githubusercontent.com/alibaba/skill-up/main/install.sh | bash ``` > **Platform:** `skill-up` currently supports **macOS / Linux** only; Windows is not supported. After installing, run `skill-up --version` again. If the command is still missing, add `~/.local/bin` to `PATH`. More details: `references/install.md`. ### Step 0.5 (optional): User config and telemetry For OTLP defaults, `runtime_kwargs` (e.g. OpenSandbox `base_url`), etc.: ```bash skill-up init skill-up init --local skill-up init --print skill-up init --force ``` Precedence (low → high): embedded empty defaults /evals/eval.yaml`. - Copy `assets/case.yaml.tmpl` to `/evals/cases/.yaml`. Adapt language per "Language Rules for Generated Artifacts". In an English context, it is **prohibited** to copy Chinese placeholder text from the templates into generated files — all prose must be rewritten in English. The Chinese in the templates is for structural reference only, not to be carried over. Selection guidelines: - `environment.type`: use `none` for pure-text Skills; use `opensandbox` when you need a remote sandbox (set `OPENSANDBOX_API_KEY`, put non-secrets in `environment.kwargs`). - `engine.name` + `engine.model`: default `claude_code`; `model` is optional. For `qodercli`, often omit `model`. - `judge.type`: `rule_based` (preferred), `script`, `agent_judge` (expensive) — see `references/judge-types.md`. - Case ID = filename without `.yaml`; prompts should exercise real Skill value. See `references/eval-yaml.md` and `references/case-yaml.md`. ### Step 3: Fill the gaps (when evals already exist) - `skill-up list-cases ` - Review `eval.yaml` and representative cases; avoid `agent_judge` abuse. - Add or edit YAML under `cases/` as needed. ### Step 4: Validate the configuration ```bash skill-up validate /evals/eval.yaml ``` Expect: `✓ eval.yaml is valid (loaded N case(s))`. ### Step 5: Prepare credentials Priority: `--api-key` > env (`ANTHROPIC_API_KEY`, `OPENAI_API_KEY`, `QODER_PERSONAL_ACCESS_TOKEN`) > `~/.skill-up/credentials.yaml`. ```bash printenv | grep -E 'ANTHROPIC_API_KEY|OPENAI_API_KEY|QODER_PERSONAL_ACCESS_TOKEN' ``` If missing, **stop and ask**; do not write secrets into YAML without consent. For `opensandbox`, also ensure `OPENSANDBOX_API_KEY` (and related env) as needed. ### Step 6: Run the evaluation ```bash skill-up run /evals/eval.yaml ``` | Scenario | Command | | -------------------------------- | ------------------------------------- | | Subset | `--include-case-name "basic-*"` | | Exclude | `--exclude-case-name "*-flaky"` | | HTML report | `--format html` | | Engine override | `--engine codex --model openai/gpt-4` | | Parallelism | `--parallelism 4` (1–256) | | Anthropic JSON | `--auto` | | N rounds | `--iteration 3` | | Auto-append after last iteration | `--iteration 0` (default behavior) | | Verbose | `-v`, `-vv` | Exit `0` = all passed; `1` = failure or error — suitable for CI. ### Step 7: Interpret the report Artifacts under `/-workspace/iteration-N/`: - `result.json`, `benchmark.json`, optional `report.html` - `/with_skill/grading.json`, `outputs/` Summarize: pass rate and timing; for failures, case id, assertion `text`, and `evidence`; benchmark deltas if enabled; offer HTML path or `skill-up report result.json --format html`. ## Command quick reference | Command | Purpose | | --------------------------------------------- | --------------------------------- | | `skill-up validate ` | Validate before `run`. | | `skill-up list-cases ` | List cases. | | `skill-up run [eval.yaml]` | Run evals. | | `skill-up run --auto` | Run from `evals/evals.json`. | | `skill-up report --format html` | Re-render reports. | | `skill-up import ` | Convert Anthropic format to YAML. | | `skill-up init` | Write user-config template. | | `skill-up debug judge ` | Debug judge. | | `skill-up debug report ` | Debug report. | Full flags: `references/cli.md`. ## Common pitfalls - Model IDs vs proxy aliases — preserve what works for the user's `base_url`. - `opensandbox` without `OPENSANDBOX_API_KEY` — auth failures. - Chinese `expect.must_contain` vs English model output — align language in prompts/assertions. - Abusing `agent_judge`. - Anthropic `evals.json` expectations → default `agent_judge`; use `import` + hand edits for deterministic checks. - Paths relative to Skill root (`SKILL.md` directory). - `--iteration 0` appends after the latest existing iteration; positive `--iteration N` runs N rounds. ## References - `references/install.md` - `references/eval-yaml.md` - `references/case-yaml.md` - `references/judge-types.md` - `references/cli.md` - `references/migrate-anthropic.md` - `assets/eval.yaml.tmpl`, `assets/case.yaml.tmpl` ## Source & license This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code. - **Author:** [alibaba](https://github.com/alibaba) - **Source:** [alibaba/skill-up](https://github.com/alibaba/skill-up) - **License:** Apache-2.0 - **Homepage:** https://alibaba.github.io/skill-up/ Install and usage instructions live in the source repository linked above. ## Pricing - **Free** — Free ## Security capabilities Automated source analysis of v0.1.0 — what this tool can access: - **Network access:** yes - **Filesystem access:** no - **Shell / process execution:** no - **Environment & secrets:** no - **Dynamic code execution:** no *"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.* ## Versions - **0.1.0** — security scan: flagged — Imported from the upstream source. ## Links - Listing page: https://agentstack.voostack.com/l/skill-alibaba-skill-up-skill-upper - Seller: https://agentstack.voostack.com/s/alibaba - Browse the marketplace: https://agentstack.voostack.com/browse --- Listed on AgentStack — the marketplace for AI agent skills and MCP servers. 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