skill-upper

name

skill-upper

description

Create, run, diagnose, and iteratively improve Agent Skill evaluations (evals) with the skill-up CLI / 使用 skill-up CLI 创建、运行、诊断并持续改进 Agent Skill 评测. Use when the user asks to evaluate, test, regress, verify, fix, improve, iterate, or evolve a Skill; add or strengthen eval 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, eval scaffolding, judge authoring, validation, runs, reports, and evidence-based repair loops.

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 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:

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

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.:

skill-up init
skill-up init --local
skill-up init --print
skill-up init --force

Precedence (low → high): embedded empty defaults < user config < project .skill-up.yaml < --config. SKILL_UP_CONFIG can point at the user config file (env var name is historical). See the upstream README "User config".

Step 1: Locate the target Skill

1. Identify the root directory of the target Skill (the directory containing SKILL.md). Search in this priority: user path → nearest SKILL.md upward from CWD → recently viewed files.
2. Read the target SKILL.md for scope, triggers, and dependencies. If the Skill is Chinese but the user writes in English, translate capabilities into English for prompts and assertions.
3. Check evals/:
   • evals/eval.yaml exists → Step 4 (optionally Step 3).
   • Only evals/evals.json → references/migrate-anthropic.md (skill-up run --auto or skill-up import).
   • Nothing → Step 2.

Step 2: Scaffold the evals (only when none exist)

• Copy assets/eval.yaml.tmpl to <skill-root>/evals/eval.yaml.
• Copy assets/case.yaml.tmpl to <skill-root>/evals/cases/<case-id>.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. Preserve short field-leading comments in generated YAML. In Chinese context, rewrite those comments into Chinese while keeping field names and enum values in English.

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 <path>
• Review eval.yaml and representative cases; avoid agent_judge abuse.
• Add or edit YAML under cases/ as needed.

Step 4: Validate the configuration

skill-up validate <skill-root>/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.

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

skill-up run <skill-root>/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
Stability/flakiness sampling	--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. When an explicit positive --iteration N runs more than one sample, inspect the terminal's simple current-command summary for lines like case_a: 3 trials, 2 PASS, 1 FAIL -> flaky.

Step 7: Interpret the report

Artifacts under <skill-root>/<skill-name>-workspace/iteration-N/:

• result.json, benchmark.json, optional report.html
• <case-id>/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.

Step 8: Evolve the Skill when requested

Only enter this loop when the user asks to fix, improve, iterate, or evolve the target Skill. If the user only asks to evaluate or report results, stop after Step 7 without modifying it.

1. Diagnose failures from result.json, grading.json, and output evidence.
2. Fix SKILL.md or supporting files when the Skill behavior is incorrect.
3. Add or refine eval cases when coverage is missing.
4. Do not weaken valid assertions merely to make a failure pass.
5. Rerun failed cases first, then run the full eval suite.
6. Continue until the evals pass or clearly report what remains blocked.

Command quick reference

Command	Purpose
skill-up validate <eval.yaml>	Validate before run.
skill-up list-cases <eval.yaml>	List cases.
skill-up run [eval.yaml]	Run evals.
skill-up run --auto	Run from evals/evals.json.
skill-up report <result.json> --format html	Re-render reports.
skill-up import <evals.json>	Convert Anthropic format to YAML.
skill-up init	Write user-config template.
skill-up debug judge <input.json>	Debug judge.
skill-up debug report <input.json>	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 one run after the latest existing iteration without summarizing history; positive --iteration N runs N samples of the selected cases and, when N > 1, prints a simple stability/flakiness summary covering only samples from the current command.

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