skill-upper

A guide for testing and improving Agent Skills with the skill-up command-line tool. An evaluation, or eval, is a repeatable test that checks whether a skill behaves as expected.

In plain words
What is it for?
Use it to create eval.yaml or case.yaml files, add test cases, run evaluations, inspect reports, and iterate on a skill's instructions.
Why use it?
It gives you a way to find regressions and weak cases instead of judging a skill from a few manual runs. It also provides a process for diagnosing failures and refining the skill.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/alibaba/skill-up/skill-upper
Any agent
npx skills add alibaba/skill-up --skill skill-upper
Clone the repo
git clone --depth 1 https://github.com/alibaba/skill-up

Made for: Claude Code, Codex.

Per session 130 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,154 The whole file, excluding the scripts and references it only reads on demand.
Security scan C 2 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00130 $0.03154
Opus 5 $0.00065 $0.01577
Sonnet 5 $0.00026 $0.00631
Haiku 4.5 $0.00013 $0.00315

Measured 2d ago against content hash a4d816d3d641, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade C, and why

skill-upper scanned grade C with 2 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

The scan reads SKILL.md. This mod also ships 1 executable file (evals/fixtures/scripts/assert-english-only-generated-cases.sh), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Downloads and executes remote codehighSupply chain

curl | sh runs whatever the server returns today, which is not necessarily what it returned when this was reviewed.

curl -fsSL https://raw.githubusercontent.com/alibaba/skill-up/main/install.sh | bash

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

curl -fsSL https://raw.githubusercontent.com/alibaba/skill-up/main/install.sh | bash
skills/skill-upper/SKILL.md · 243 lines

How it starts

The opening of the file, as written. The whole thing — 243 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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.

Read the full file on GitHub · 243 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2d ago First seen · 243 lines · 130 tokens per session scan C a4d816d3d641

Subscribe to this mod's changes

skill-upper is a skill published in the GitHub repository alibaba/skill-up (757 stars, last pushed 6d ago), licensed Apache-2.0. It adds 130 tokens to every session and 3,154 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it C with 2 findings (downloads and executes remote code, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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