Skill Upper

use-skill-up-cli

Help the user evaluate and evolve Agent Skills through the skill-up CLI.

Manual: <https://alibaba.github.io/skill-up/>

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.
• Generated YAML comments must use field-leading comments. Keep each comment short: one line for field meaning, plus one line for options only when useful.
• When listing options in comments, keep enum values unchanged, such as none | opensandbox | docker and rule_based | agent_judge | script.
• 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.
• skill-up import uses the CLI conversion path and does not preserve template comments; do not promise commented YAML for import-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/
      <case-id>.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 asks to "fix / improve / iterate / evolve this skill" from eval failures.
• The user wants to "add evals, test cases, or regression cases to a skill".
• The user asks to record explicitly attributed Skill usage, evidence, or feedback.
• The user wants to review captured Skill observations or turn approved feedback into a regression case.
• 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)

Observation capture mode (optional)

Use this mode when the user asks to record the current Skill interaction or feedback and observation tools are available. It requires either a current Codex release with the codex-skill-up plugin and lifecycle-hook support, or DSH with the skill-up plugin's observer explicitly enabled. Do not retrofit observation capture onto the pinned Codex 0.80.0 evaluation adapter.

If the user asks how to install either host plugin, or the required observation tools are missing and the user asks to enable them, read references/install.md under "安装 observation host plugin（可选）". Do not install or enable a plugin unless the user asks.

Claude Code, qodercli, Qwen Code, and other Agent Engines are not supported for this observation workflow. This restriction does not apply to normal skill-up evaluation runs, which remain multi-engine.

1. Attribute the interaction only when the user explicitly references a Skill (for example, $my-skill) or the responsible Skill is otherwise known with certainty.
2. In Codex, if explicit attribution is absent but certain, call mark_skill_invocation once with the Skill name and optional version. DSH derives exact attribution from its durable /skill injection or successful skill tool result; do not add inferred attribution.
3. In Codex, use attach_skill_evidence only for references that help reproduce or evaluate the behavior. Prefer paths and short summaries over copied file contents.
4. In Codex, use record_skill_feedback during the observed turn. In DSH, wait until the completed turn has produced an observation, then use record_observation_feedback with its ID. Never invent sentiment or comments.

Both adapters redact common credential shapes before local persistence, but still avoid sending secrets to marker tools. If the request is capture-only, stop after recording it; do not edit, evaluate, commit, or publish a Skill unless the user also asks for that work.

Observation input mode (optional)

When the request starts from a captured Skill observation, read references/observations.md first. Use the available observation tools to inspect and preview the observation, require explicit approval for the exact candidate, write it only after approval, and then continue at Step 4. If the request does not start from an observation, use the normal flow below.

Step 0: Make sure skill-up is installed

Before doing anything, verify skill-up is available:

command -v skill-up && skill-up --version

If a version is printed, continue. If you see command not found, on macOS / Linux:

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