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 skills add TencentCloudBase/CloudBase-AI-Toolkit --skill pr-review-fixgit 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/pr-review-fix)<a href="https://agentmods.dev/skills/tencentcloudbase/cloudbase-ai-toolkit/pr-review-fix"><img src="https://agentmods.dev/badge/skills/tencentcloudbase/cloudbase-ai-toolkit/pr-review-fix/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/tencentcloudbase/cloudbase-ai-toolkit/pr-review-fix"><img src="https://agentmods.dev/badge/skills/tencentcloudbase/cloudbase-ai-toolkit/pr-review-fix.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 5.1 | $0.00066 | $0.01031 |
| Opus 5 | $0.00033 | $0.00515 |
| Sonnet 5 | $0.00013 | $0.00206 |
| Haiku 4.5 | $0.00007 | $0.00103 |
Grade A, and why
pr-review-fix 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 9d 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 — 112 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PR Review & Fix
Systematically analyze open pull requests for CI failures, code review feedback, and code quality issues — then fix them efficiently.
When to use this skill
Use this skill when you need to:
- Check the status of all open PRs (CI, reviews, conflicts)
- Triage and fix CI build/test failures on PR branches
- Address code review feedback (reviewer comments, requested changes)
- Run a scheduled health check across all open PRs
- Fix multiple PRs in a single session without losing context
Do NOT use for:
- Creating new PRs or new features
- Merging PRs (that's a manual decision)
- General code refactoring unrelated to PR feedback
- Reviewing code as a reviewer (this skill is for responding to reviews)
Workflow
Phase 1 — Discovery
- Read
references/discovery.mdfor the full discovery procedure. - Fetch the list of open PRs from GitHub:
gh pr list --state open --json number,title,headRefName,statusCheckRollup,reviewDecision,mergeable --limit 30 - For each PR, classify its health status:
- 🔴 CI Failed — at least one required check failed
- 🟡 Changes Requested — reviewer left requested changes
- 🟢 Healthy — CI passing + approved or no review yet
- ⚪ Conflict — merge conflicts detected
Phase 2 — Triage
- Read
references/triage.mdfor prioritization rules. - Prioritize by severity: CI failures > review changes > conflicts.
- For each failing PR, identify root cause category:
- Build error — TypeScript/webpack compilation failure
- Test failure — vitest/jest test assertion or timeout
- Lint/type error — ESLint, type-check, or format issues
- Review feedback — code style, logic, security, or design concerns
- Present a summary table to the user before proceeding to fixes.
Phase 3 — Fix
- Read
references/fix-workflow.mdfor the fix procedure. - For each PR to fix (in priority order):
a. Stash current work:
git stashb. Check out the PR branch:git checkout -B <branch> github/<branch>c. Reproduce the issue locally (build, test, or lint) d. Apply the fix e. Verify locally: build → test → lint f. Commit with conventional-changelog format:fix(<scope>): 🔧 <description>g. Push:git push github <branch>h. Return to original branch:git checkout <original> && git stash pop - After all fixes, present a completion summary.
What ships with it
5 files 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.
- 9d ago First seen · 112 lines · 66 tokens per session scan A bbc873140b79
pr-review-fix is a skill published in the GitHub repository TencentCloudBase/CloudBase-AI-Toolkit (1,103 stars, last pushed yesterday), licensed MIT. It adds 66 tokens to every session and 1,031 once invoked, about $0.0003 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 2026-09-03.
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