Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/tencentcloudbase/cloudbase-skills/data-model-creationnpx skills add TencentCloudBase/cloudbase-skills --skill data-model-creationgit clone --depth 1 https://github.com/TencentCloudBase/cloudbase-skillsWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00068 | $0.01560 |
| Opus 5 | $0.00034 | $0.00780 |
| Sonnet 5 | $0.00014 | $0.00312 |
| Haiku 4.5 | $0.00007 | $0.00156 |
Grade A, and why
data-model-creation scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured today.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to data-model-creation — 2 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 189 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Sibling skills (local only)
Sibling CloudBase skills ship beside this skill. Use local relative paths such as ../auth-tool-cloudbase/SKILL.md.
If a referenced sibling skill file is missing from this environment, ask the user to install the full CloudBase plugin (or the missing skill). Do not HTTP-fetch remote skill or protocol markdown into the agent context.
Data Model Creation
Activation Contract
Use this first when
- The user explicitly wants Mermaid
classDiagrammodeling. - The task needs complex multi-entity relational design, visual ER-style output, or generated data-model structure rather than direct SQL.
- You need to create CloudBase data models through the dedicated modeling tools, or you need to inspect an existing model before planning follow-up changes.
Read before writing code if
- The request mentions data model, ER diagram, Mermaid, relationship graph, or enterprise schema design.
- The user wants to reuse or update an existing published model.
Then also read
- Direct MySQL SQL creation or schema change ->
../relational-database-mcp-cloudbase/SKILL.md - PostgreSQL / CloudBase PG schema work ->
../postgresql-development-cloudbase/SKILL.md - Broader feature planning before schema work ->
../spec-workflow/SKILL.md
Do NOT use for
- Simple
CREATE TABLE,ALTER TABLE, or CRUD tasks. - Document-database collection design.
- Frontend-only data-shape discussions with no modeling requirement.
Common mistakes / gotchas
- Using Mermaid modeling for a task that only needs one or two SQL statements.
- Mixing SQL-table design and NoSQL collection design in the same model.
- Generating diagrams without first deciding entity boundaries and ownership relations.
- Publishing a new model before validating the generated fields and relationships.
Minimal checklist
- Confirm Mermaid modeling is actually needed.
- List the core entities and relationships first.
- Decide whether this is a new model or an update.
- Keep the initial model small unless the user explicitly wants a large enterprise schema.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- today Changed a4057ffc23d6
- 2d ago First seen · 189 lines · 68 tokens per session scan A 6ee1b985c328
data-model-creation is a skill published in the GitHub repository TencentCloudBase/cloudbase-skills (30 stars, last pushed yesterday), licensed MIT. It adds 68 tokens to every session and 1,560 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to data-model-creation, differing in 2 lines, and is treated as a copy.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…