Aliyun Skill Creator

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name: aliyun-skill-creator description: Use when creating, migrating, or optimizing skills for this alicloud-skills repository. Use whenever users ask to add a new skill, import an external skill, refactor skill structure, improve trigger descriptions, add smoke tests under tests/**, or benchmark skill quality before merge. version: 1.0.0

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Category: tool

Alibaba Cloud Skill Creator

Repository-specific skill engineering workflow for alicloud-skills.

Use this skill when

Creating a new skill under skills/**.

Importing an external skill and adapting it to this repository.

Updating skill trigger quality (name and description in frontmatter).

Adding or fixing smoke tests under tests/**.

Running structured benchmark loops before merge.

Do not use this skill when

The user only needs to execute an existing product skill.

The task is purely application code under apps/ with no skill changes.

Repository constraints (must enforce)

Skills live under skills////.

Skill folder names use kebab-case and should start with alicloud-.

Every skill must include SKILL.md frontmatter with name and description.

skills/**/SKILL.md content must stay English-only.

Smoke tests must be in tests///-test/SKILL.md.

Generated evidence goes to output// only.

If skill inventory changes, refresh README index with scripts/update_skill_index.sh.

Standard deliverable layout

skills////
├── SKILL.md
├── agents/openai.yaml
├── references/
│   └── sources.md
└── scripts/ (optional)
tests///-test/
└── SKILL.md

Workflow

1) Capture intent

Confirm domain/subdomain and target skill name.

Confirm whether this is new creation, migration, or refactor.

Confirm expected outputs and success criteria.

2) Implement skill changes

For new skills: scaffold structure and draft SKILL.md + agents/openai.yaml.

For migration from external repo: copy full source tree first, then adapt.

Keep adaptation minimal but explicit:

- Replace environment-specific instructions that do not match this repo. - Add repository validation and output discipline sections. - Keep reusable bundled resources (scripts/, references/, assets/).

3) Add smoke test

Create or update tests/**/-test/SKILL.md.

Keep it minimal, reproducible, and low-risk.

Include exact pass criteria and evidence location.

4) Validate locally

Run script compile validation for the skill:

python3 tests/common/compile_skill_scripts.py \
  --skill-path skills/// \
  --output output/-test/compile-check.json

Refresh skill index when inventory changed:

scripts/update_skill_index.sh

Confirm index presence:

rg -n "" README.md README.zh-CN.md README.zh-TW.md

Optional broader checks:

make test
make build-cli

5) Benchmark loop (optional, for major skills)

If the user asks for quantitative skill evaluation, reuse bundled tooling:

scripts/run_eval.py

scripts/aggregate_benchmark.py

eval-viewer/generate_review.py

Prefer placing benchmark artifacts in a sibling workspace directory and keep per-iteration outputs.

Definition of done

Skill path and naming follow repository conventions.

Frontmatter is complete and trigger description is explicit.

Test skill exists and has objective pass criteria.

Validation artifacts are saved under output/.

README skill index is refreshed if inventory changed.

References

references/schemas.md

references/sources.md