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alicloud-data-lake-dlf▌

cinience/alicloud-skills · updated Apr 8, 2026

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$npx skills add https://github.com/cinience/alicloud-skills --skill alicloud-data-lake-dlf
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summary

Category: service

skill.md

Category: service

Data Lake Formation

Use Alibaba Cloud OpenAPI (RPC) with official SDKs or OpenAPI Explorer to manage resources for Data Lake Formation.

Workflow

  1. Confirm region, resource identifiers, and desired action.
  2. Discover API list and required parameters (see references).
  3. Call API with SDK or OpenAPI Explorer.
  4. Verify results with describe/list APIs.

AccessKey priority (must follow)

  1. Environment variables: ALICLOUD_ACCESS_KEY_ID / ALICLOUD_ACCESS_KEY_SECRET / ALICLOUD_REGION_ID Region policy: ALICLOUD_REGION_ID is an optional default. If unset, decide the most reasonable region for the task; if unclear, ask the user.
  2. Shared config file: ~/.alibabacloud/credentials

API discovery

  • Product code: DataLake
  • Default API version: 2020-07-10
  • Use OpenAPI metadata endpoints to list APIs and get schemas (see references).

High-frequency operation patterns

  1. Inventory/list: prefer List* / Describe* APIs to get current resources.
  2. Change/configure: prefer Create* / Update* / Modify* / Set* APIs for mutations.
  3. Status/troubleshoot: prefer Get* / Query* / Describe*Status APIs for diagnosis.

Minimal executable quickstart

Use metadata-first discovery before calling business APIs:

python scripts/list_openapi_meta_apis.py

Optional overrides:

python scripts/list_openapi_meta_apis.py --product-code <ProductCode> --version <Version>

The script writes API inventory artifacts under the skill output directory.

Output policy

If you need to save responses or generated artifacts, write them under: output/alicloud-data-lake-dlf/

Validation

mkdir -p output/alicloud-data-lake-dlf
for f in skills/data-lake/alicloud-data-lake-dlf/scripts/*.py; do
  python3 -m py_compile "$f"
done
echo "py_compile_ok" > output/alicloud-data-lake-dlf/validate.txt

Pass criteria: command exits 0 and output/alicloud-data-lake-dlf/validate.txt is generated.

Output And Evidence

  • Save artifacts, command outputs, and API response summaries under output/alicloud-data-lake-dlf/.
  • Include key parameters (region/resource id/time range) in evidence files for reproducibility.

Prerequisites

  • Configure least-privilege Alibaba Cloud credentials before execution.
  • Prefer environment variables: ALICLOUD_ACCESS_KEY_ID, ALICLOUD_ACCESS_KEY_SECRET, optional ALICLOUD_REGION_ID.
  • If region is unclear, ask the user before running mutating operations.

References

  • Sources: references/sources.md
how to use alicloud-data-lake-dlf

How to use alicloud-data-lake-dlf on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • ›Cursor installed and configured on your development machine
  • ›Node.js version 16.0+ with npm package manager (verify with node --version)
  • ›Active project directory or workspace where you want to add alicloud-data-lake-dlf
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/cinience/alicloud-skills --skill alicloud-data-lake-dlf

The skills CLI fetches alicloud-data-lake-dlf from GitHub repository cinience/alicloud-skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/alicloud-data-lake-dlf

Reload or restart Cursor to activate alicloud-data-lake-dlf. Access the skill through slash commands (e.g., /alicloud-data-lake-dlf) or your agent's skill management interface.

⚠

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

Additional Resources

›View source code on GitHub›Skills CLI documentation›Learn more about Cursor›What are agent skills?

List & Monetize Your Skill

Submit your Claude Code skill and start earning

GET_STARTED →

Use Cases▌

Task Automation & Efficiency

Automate repetitive workflows and reduce manual effort

Example

Generate reports, summarize documents, draft communications

✓

Save 3-5 hours per week on routine tasks

Knowledge Enhancement

Learn new skills, understand complex topics, get expert guidance

Example

Explain concepts, provide examples, suggest learning resources

✓

Accelerate learning and skill development by 2x

Quality Improvement

Enhance output quality through reviews, suggestions, and refinements

Example

Review drafts, suggest improvements, catch errors

✓

Improve work quality by 30-40% with less effort

Implementation Guide▌

Prerequisites

  • ›Claude Desktop or compatible AI client with skill support
  • ›Clear understanding of task or problem to solve
  • ›Willingness to iterate and refine outputs

Time Estimate

15-45 minutes depending on use case complexity

Installation Steps

  1. 1.Install skill using provided installation command
  2. 2.Test with simple use case relevant to your work
  3. 3.Evaluate output quality and relevance
  4. 4.Iterate on prompts to improve results
  5. 5.Integrate into regular workflow if valuable

Common Pitfalls

  • ⚠Expecting perfect results without iteration
  • ⚠Not providing enough context in prompts
  • ⚠Using skill for tasks outside its intended scope
  • ⚠Accepting outputs without review and validation

Best Practices▌

✓ Do

  • +Start with clear, specific prompts
  • +Provide relevant context and constraints
  • +Review and refine all outputs before using
  • +Iterate to improve output quality
  • +Document successful prompt patterns

✗ Don't

  • −Don't use without understanding skill limitations
  • −Don't skip validation of outputs
  • −Don't share sensitive information in prompts
  • −Don't expect skill to replace human judgment

💡 Pro Tips

  • ★Be specific about desired format and style
  • ★Ask for multiple options to choose from
  • ★Request explanations to understand reasoning
  • ★Combine AI efficiency with human expertise

When to Use This▌

✓ Use When

Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.

✗ Avoid When

Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.

Learning Path▌

  1. 1Familiarize yourself with skill capabilities and limitations
  2. 2Start with low-risk, non-critical tasks
  3. 3Progress to more complex and valuable use cases
  4. 4Build expertise through regular use and experimentation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.6★★★★★28 reviews
  • ★★★★★Meera Brown· Dec 28, 2024

    alicloud-data-lake-dlf fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • ★★★★★Diya Jackson· Dec 4, 2024

    alicloud-data-lake-dlf is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • ★★★★★Yash Thakker· Nov 27, 2024

    I recommend alicloud-data-lake-dlf for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • ★★★★★Hassan Reddy· Nov 23, 2024

    Solid pick for teams standardizing on skills: alicloud-data-lake-dlf is focused, and the summary matches what you get after install.

  • ★★★★★Fatima Khanna· Nov 19, 2024

    Registry listing for alicloud-data-lake-dlf matched our evaluation — installs cleanly and behaves as described in the markdown.

  • ★★★★★Nikhil Desai· Nov 15, 2024

    Useful defaults in alicloud-data-lake-dlf — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • ★★★★★Dhruvi Jain· Oct 18, 2024

    Useful defaults in alicloud-data-lake-dlf — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • ★★★★★Diya Patel· Oct 14, 2024

    alicloud-data-lake-dlf has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • ★★★★★Naina Park· Oct 10, 2024

    alicloud-data-lake-dlf reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • ★★★★★Nia Garcia· Oct 6, 2024

    I recommend alicloud-data-lake-dlf for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

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