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/<domain>/<subdomain>/<skill-name>/.
• 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/<domain>/<subdomain>/<skill-name>-test/SKILL.md.
• Generated evidence goes to output/<skill-or-test-skill>/ only.
• If skill inventory changes, refresh README index with scripts/update_skill_index.sh.

Standard deliverable layout#

text

skills/<domain>/<subdomain>/<skill-name>/
├── SKILL.md
├── agents/openai.yaml
├── references/
│   └── sources.md
└── scripts/ (optional)

tests/<domain>/<subdomain>/<skill-name>-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.

1. 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/).

1. Add smoke test

• Create or update tests/**/<skill-name>-test/SKILL.md.
• Keep it minimal, reproducible, and low-risk.
• Include exact pass criteria and evidence location.

1. Validate locally

Run script compile validation for the skill:

bash

python3 tests/common/compile_skill_scripts.py \
  --skill-path skills/<domain>/<subdomain>/<skill-name> \
  --output output/<skill-name>-test/compile-check.json

Refresh skill index when inventory changed:

bash

scripts/update_skill_index.sh

Confirm index presence:

bash

rg -n "<skill-name>" README.md README.zh-CN.md README.zh-TW.md

Optional broader checks:

bash

make test
make build-cli

1. 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