Category: tool
Repository-specific skill engineering workflow for alicloud-skills.
skills/**.name and description in frontmatter).tests/**.apps/ with no skill changes.skills//// .alicloud-.SKILL.md frontmatter with name and description.skills/**/SKILL.md content must stay English-only.tests///-test/SKILL.md .output// only.scripts/update_skill_index.sh.skills/<domain>/<subdomain>/<skill-name>/
├── SKILL.md
├── agents/openai.yaml
├── references/
│ └── sources.md
└── scripts/ (optional)
tests/<domain>/<subdomain>/<skill-name>-test/
└── SKILL.md
1) Capture intent
2) Implement skill changes
SKILL.md + agents/openai.yaml.scripts/, references/, assets/).3) Add smoke test
tests/**/-test/SKILL.md .4) Validate locally
Run script compile validation for the skill:
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:
scripts/update_skill_index.sh
Confirm index presence:
rg -n "<skill-name>" 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.pyscripts/aggregate_benchmark.pyeval-viewer/generate_review.pyPrefer placing benchmark artifacts in a sibling workspace directory and keep per-iteration outputs.
output/.references/schemas.mdreferences/sources.md共 1 个版本