CloudBase AI Toolkit is an integration layer that lets AI coding tools use Tencent CloudBase as an application backend, including databases, authentication, functions, storage, and deployments. Developers use it to have coding agents build and operate CloudBase-backed applications through skills, plugins, commands, and chat-accessible tools. The catalogue entries are the toolkit’s own skills, agents, hooks, plugins, rules, instructions, and MCP tools.
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-ai-toolkit/doc-freshness-reviewnpx skills add TencentCloudBase/CloudBase-AI-Toolkit --skill doc-freshness-reviewgit clone --depth 1 https://github.com/TencentCloudBase/CloudBase-AI-ToolkitWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/tencentcloudbase/cloudbase-ai-toolkit/doc-freshness-review)<a href="https://agentmods.dev/skills/tencentcloudbase/cloudbase-ai-toolkit/doc-freshness-review"><img src="https://agentmods.dev/badge/skills/tencentcloudbase/cloudbase-ai-toolkit/doc-freshness-review.svg" alt="Measured on agentmods" height="20"></a>What 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 | $0.00083 | $0.00913 |
| Opus 5 | $0.00042 | $0.00456 |
| Sonnet 5 | $0.00017 | $0.00183 |
| Haiku 4.5 | $0.00008 | $0.00091 |
Grade A, and why
doc-freshness-review 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 5d ago.
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.
How it starts
The opening of the file, as written. The whole thing — 94 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Documentation Freshness Review
Review whether repository documentation still matches the current code, generated assets, and published entry points.
When to use this skill
Use this skill when you need to:
- Audit published documentation under
doc/for stale or wrong statements - Check whether
README.md,README.zh-CN.md,mcp/README.md, ormcp/README.zh-CN.mddrifted away from current behavior - Verify that linked scripts, files, commands, and workflows still exist and still match the repo
- Run a periodic docs health check before opening corrective PRs
- Separate mechanical doc drift from product ambiguity that still needs human confirmation
Do NOT use for:
- Marketing copy polish without a freshness or correctness question
- API contract review that depends on CloudBase control-plane docs first
- Feature planning or architecture design
- Rewriting large docs from scratch when the main need is a targeted drift audit
Workflow
Phase 1 — Scope the surfaces
- Read
references/review-scope.mdfirst. - Build the audit surface in two groups:
- published documentation under
doc/ - README surfaces such as
README.md,README.zh-CN.md, andmcp/README.md
- published documentation under
- Note which code, scripts, generated files, or workflows those docs claim to describe.
Phase 2 — Evidence-based comparison
- Verify whether referenced files, scripts, commands, and workflow names still exist.
- Compare the docs against the current implementation, generated outputs, and source-of-truth files.
- Mark each mismatch as one of:
- stale statement
- missing or stale reference
- outdated command
- drift between published docs and README surfaces
- drift between docs and actual implementation
Phase 3 — Severity and action
- Prefer concrete findings with exact file paths and the smallest useful rewrite direction.
- If the fix is mechanical and low-risk, prepare the doc updates for a focused PR.
- If the docs may be wrong because product behavior is unclear, write a report and issue instead of guessing.
- If the drift originates from a CloudBase API contract problem, hand off to
api-contract-reviewbefore updating the prose.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 5d ago First seen · 94 lines · 83 tokens per session scan A 6c90ee5a01bb
doc-freshness-review is a skill published in the GitHub repository TencentCloudBase/CloudBase-AI-Toolkit (1,092 stars, last pushed today), licensed MIT. It adds 83 tokens to every session and 913 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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