Cloudrun Development
TencentCloudBase/CloudBase-AI-Toolkit
CloudBase Run backend development rules (Function mode/Container mode).
Amazon Aurora PostgreSQL — creates, modifies, and advises on Aurora PostgreSQL clusters specifically (PostgreSQL-compatible engine, Aurora serverless, express configuration, pgvector, Babelfish).
$ npx skills add aws/agent-toolkit-for-aws --skill amazon-aurora-postgresql -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install aws/agent-toolkit-for-aws amazon-aurora-postgresql --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/aws/agent-toolkit-for-aws.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/specialized-skills/database-skills/amazon-aurora-postgresql .claude/skills/amazon-aurora-postgresql && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "amazon-aurora-postgresql" agent skill from https://github.com/aws/agent-toolkit-for-aws/tree/main/skills/specialized-skills/database-skills/amazon-aurora-postgresql into .claude/skills/amazon-aurora-postgresql/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "amazon-aurora-postgresql", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/aws/agent-toolkit-for-aws/tree/main/skills/specialized-skills/database-skills/amazon-aurora-postgresqlType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add aws/agent-toolkit-for-aws --skill amazon-aurora-postgresql -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install aws/agent-toolkit-for-aws amazon-aurora-postgresql --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/aws/agent-toolkit-for-aws.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/specialized-skills/database-skills/amazon-aurora-postgresql .agents/skills/amazon-aurora-postgresql && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "amazon-aurora-postgresql" agent skill from https://github.com/aws/agent-toolkit-for-aws/tree/main/skills/specialized-skills/database-skills/amazon-aurora-postgresql into .agents/skills/amazon-aurora-postgresql/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "amazon-aurora-postgresql", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add aws/agent-toolkit-for-aws --skill amazon-aurora-postgresql -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install aws/agent-toolkit-for-aws amazon-aurora-postgresql --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/aws/agent-toolkit-for-aws.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/specialized-skills/database-skills/amazon-aurora-postgresql .cursor/skills/amazon-aurora-postgresql && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "amazon-aurora-postgresql" agent skill from https://github.com/aws/agent-toolkit-for-aws/tree/main/skills/specialized-skills/database-skills/amazon-aurora-postgresql into .cursor/skills/amazon-aurora-postgresql/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "amazon-aurora-postgresql", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/aws/agent-toolkit-for-aws.git --path skills/specialized-skills/database-skills/amazon-aurora-postgresql--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add aws/agent-toolkit-for-aws --skill amazon-aurora-postgresql -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install aws/agent-toolkit-for-aws amazon-aurora-postgresql --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/aws/agent-toolkit-for-aws.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/specialized-skills/database-skills/amazon-aurora-postgresql .gemini/skills/amazon-aurora-postgresql && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "amazon-aurora-postgresql" agent skill from https://github.com/aws/agent-toolkit-for-aws/tree/main/skills/specialized-skills/database-skills/amazon-aurora-postgresql into .gemini/skills/amazon-aurora-postgresql/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "amazon-aurora-postgresql", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install aws/agent-toolkit-for-aws amazon-aurora-postgresqlInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add aws/agent-toolkit-for-aws --skill amazon-aurora-postgresql -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/aws/agent-toolkit-for-aws.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/specialized-skills/database-skills/amazon-aurora-postgresql .github/skills/amazon-aurora-postgresql && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "amazon-aurora-postgresql" agent skill from https://github.com/aws/agent-toolkit-for-aws/tree/main/skills/specialized-skills/database-skills/amazon-aurora-postgresql into .github/skills/amazon-aurora-postgresql/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "amazon-aurora-postgresql", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add aws/agent-toolkit-for-aws --skill amazon-aurora-postgresql -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install aws/agent-toolkit-for-aws amazon-aurora-postgresql --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/aws/agent-toolkit-for-aws.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/specialized-skills/database-skills/amazon-aurora-postgresql .opencode/skills/amazon-aurora-postgresql && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "amazon-aurora-postgresql" agent skill from https://github.com/aws/agent-toolkit-for-aws/tree/main/skills/specialized-skills/database-skills/amazon-aurora-postgresql into .opencode/skills/amazon-aurora-postgresql/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "amazon-aurora-postgresql", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
amazon-aurora-postgresqlAmazon Aurora PostgreSQL — creates, modifies, and advises on Aurora PostgreSQL clusters specifically (PostgreSQL-compatible engine, Aurora serverless, express configuration, pgvector, Babelfish).
Amazon Aurora Postgresql is an agent skill from aws/agent-toolkit-for-aws, published by the product's own GitHub organization. Amazon Aurora PostgreSQL — creates, modifies, and advises on Aurora PostgreSQL clusters specifically (PostgreSQL-compatible engine, Aurora serverless, express configuration, pgvector, Babelfish). Trigger for Aurora PostgreSQL cluster operations, express-configuration quick-start, ACU sizing, I/O-Optimized storage, commitment pricing, or PostgreSQL upgrade planning. For Aurora MySQL, use amazon-aurora-mysql instead. Contains safety guardrails, express-first routing, and response templates that override defaults.
Its SKILL.md is about 4.8k tokens, which your agent loads only when the skill is triggered. The skill folder holds 43 other files, including scripts and reference files (for example `references/commitment-pricing-basics.md`, `references/commitment-pricing-instructions.md` and `references/commitment-pricing-mechanics.md`).
It sits in Databases, covering Serverless, Container orchestration and LLM guardrails. It works with PostgreSQL, MySQL and pgvector. The repository describes itself as: Official, AWS-supported MCP servers, skills, and plugins to help AI agents build on AWS. The licence is Apache-2.0.
5 steps, taken from the first numbered list in SKILL.md.
Read from SKILL.md and the folder at commit bd49cc8. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Ships 1 file in scripts/, which the agent can run.
Shell commands in SKILL.md call:
awspython3From the folder's file list and the shell code blocks in SKILL.md.
Links to these hosts (documentation or services it may open):
docs.aws.amazon.comaws.amazon.comFrom URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Amazon Aurora Postgresql loads about 4.8k tokens when it runs, and up to ~58k if it reads all its reference files. Until then it costs about 135 tokens; SKILL.md has 2,290 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.
The full file from aws/agent-toolkit-for-aws at commit bd49cc8, republished under its Apache-2.0 licence (© aws). 2,290 words, ~4,830 tokens.
.claude/skills/amazon-aurora-postgresql/SKILL.md (or your agent's skills folder). This skill also uses 42 other files; get the full folder from GitHub.A modular toolkit for Aurora PostgreSQL organized as a registry of sub-skills. Each sub-skill handles one domain of Aurora PostgreSQL work. The router matches user intent to the right sub-skill, then loads only the references needed. (For Aurora MySQL, use the amazon-aurora-mysql skill.)
file_read the matched sub-skill's references/{id}-instructions.md and announce the path. Do not answer a matched sub-skill from general knowledge alone.Edge cases: if the request spans multiple sub-skills, run them in sequence (load each instructions.md in turn). If no sub-skill matches, answer directly from Aurora PostgreSQL knowledge. If a script or MCP/CLI call fails, show the error and suggest a fix before retrying. The numbered Global rules below are details that hang off these steps.
Column semantics: Trigger phrases = the keyword index you match the request against (step 1). When to route here = the decision logic confirming the match. Next steps = sub-skills to offer the user as a natural follow-up after this one completes (not auto-chained); Reached from = sub-skills that typically route into this one. Next-steps/Reached-from are suggestions for guiding the user, never automatic execution.
| ID | Name | When to route here | Trigger phrases | Reached from | Next steps |
|---|---|---|---|---|---|
create | Create Cluster | Routes Aurora PostgreSQL cluster creation requests. Express configuration (single API call, no VPC) is the default — routes to express-create. Routes to full configuration when VPC, custom KMS, custom params, or a specific engine version is required. | create a cluster, new database, set up Aurora PostgreSQL, get started, need a PostgreSQL database, provision | — | express-create, serverless-advisory, io-optimized |
express-create | Express Configuration | Provisions Aurora PostgreSQL serverless via the single-API-call express flow. AWS-managed connectivity (no customer VPC). IAM-only authentication via Internet Access Gateway — no master password. Post-creation connection is via IAM auth token (aws rds generate-db-auth-token). Use when no VPC, custom KMS, or custom parameter group is required. Routes back to create for full configuration needs. | express configuration, express create, internet access gateway, single API call, Aurora PostgreSQL serverless quick start, no VPC, IAM auth token, how to connect to express cluster | create | — |
serverless-advisory | Aurora serverless Advisory | All Aurora serverless questions: ACU sizing, scale-to-zero behavior and compatibility, provisioned→serverless migration, capacity planning, and feature constraints. | ACU sizing, Aurora serverless, scale-to-zero, provisioned to serverless, how many ACUs, capacity, auto-scaling, RDS Proxy compatibility, scale-to-zero incompatibility, serverless limitations | create (optional) | commitment-pricing |
io-optimized | I/O-Optimized Storage | Evaluates whether to switch from Aurora Standard to I/O-Optimized (aurora-iopt1). Uses the 25% I/O cost threshold rule. | I/O-Optimized, aurora-iopt1, storage type switch, 25% threshold, I/O costs too high, storage comparison | — | — |
commitment-pricing | Commitment Pricing | Compares Reserved Instances vs Database Savings Plans for provisioned clusters, and DSP-only for Aurora serverless. 1yr vs 3yr analysis. | Reserved Instance, RI, Savings Plan, DSP, 1yr vs 3yr, commitment, cost optimization, overpaying | serverless-advisory (optional) | — |
upgrade-planning | Upgrade Planning | Major and minor version upgrade planning for Aurora PostgreSQL. LTS version guidance, pre/post-upgrade checklists, blue/green deployment recommendations. | upgrade, version, LTS, pre-upgrade checklist, post-upgrade, major version, minor version, end of life, deprecation | — | — |
When routing a create request (sub-skill create), pick the path with this matrix. Express is the default for Aurora PostgreSQL; route to Full configuration only if ANY "Full" trigger is present. Don't present the choice to the user — decide, then state which path and why.
| Requirement / signal | Express | Full config |
|---|---|---|
| Default PostgreSQL create, no special networking | ✅ default | — |
| Quick start / "no VPC setup" / "ready in seconds" | ✅ | — |
| Customer VPC, subnet group, or specific security group | — | ✅ required |
| Customer-managed KMS key (CMK) | — | ✅ required |
| Custom DB cluster parameter group at creation | — | ✅ required |
| Specific engine version pinned by the user | — | ✅ required (intent to pin = not express) |
| Aurora MySQL | n/a | use amazon-aurora-mysql (express is PG-only) |
Notes: any single Full trigger disqualifies express — name every trigger you matched in the routing statement. Express clusters are still customizable after creation (e.g. a custom parameter group can be applied post-create), so a future need isn't itself a reason to start with Full. Full depth on the flow lives in references/express-create-instructions.md and references/create-instructions.md — load those for the actual steps.
Execute, don't just suggest. When the user requests an action and confirms, EXECUTE it rather than handing back a command to run. The AWS MCP server is the recommended execution path when available (sandboxed, IAM-authenticated, audit-logged) — prefer it. When MCP tools are not available (e.g. Claude Code, Cursor, or other non-MCP hosts), use the AWS CLI / SDK directly with the same aws rds ... operation. Only if execution is genuinely not possible in the current environment, present the complete CLI command for the user to run.
Confirmation before mutation. MUST confirm with the user before any create or modify operation. Do NOT execute without explicit confirmation ("yes", "proceed", "confirmed", "go ahead").
Resource tagging (always apply on resource creation). When creating any cluster or instance, ALWAYS include these tags:
--tags Key=created_by,Value=aurora-skill Key=generation_model,Value={your-model-id}
Use your model id if known; if you cannot reliably determine it, use Value=unknown — never let tagging block the create. Include these tags even if the user does not mention tagging. If the user provides additional tags, append these to their tags.
Safety guardrails.
Tier 1 — Confirm (a yes/no confirmation is enough; no risk briefing required):
create-db-cluster, create-db-cluster --with-express-configurationcreate-db-instancemodify-db-cluster --serverless-v2-scaling-configuration (ACU scaling)modify-db-cluster --backup-retention-periodmodify-db-cluster --deletion-protection / --no-deletion-protectionmodify-db-cluster --enable-cloudwatch-logs-exportsmodify-db-cluster --preferred-backup-windowmodify-db-cluster --enable-http-endpoint (Data API)add-tags-to-resource, remove-tags-from-resourceTier 2 — High-impact: state the specific risk, THEN confirm (spell out the impact before asking; do not call any API until the user confirms with that risk in front of them):
modify-db-cluster --storage-type — no downtime for most instance classes; requires restart for NVMe/Optimized Reads instances (r6gd, r6id, r8gd). Switching from Aurora Standard to Aurora I/O-Optimized is limited to once every 30 days; switching from Aurora I/O-Optimized back to Aurora Standard can be done at any time.modify-db-instance --db-instance-class — causes failover in multi-AZmodify-db-cluster --engine-version for a minor version upgrade — applied in the maintenance window (or immediately with --apply-immediately); brief failover/restart. State the target version and the restart impact, then confirm. (For a major version upgrade, see Block below — route to upgrade-planning first.)--apply-immediately — bypasses maintenance windowTier 3 — Block (refuse, explain why, redirect to console/change-control):
delete-db-cluster, delete-db-instance — irreversiblefailover-db-cluster, switchover-blue-green-deployment — production impactmodify-db-cluster --engine-version across major versions — requires prechecks and rollback planmodify-db-cluster --master-user-password, --manage-master-user-password — credential management must be performed by the customer directly. Express clusters use IAM-only auth via the Internet Access Gateway and have no master password — these flags do not apply on express clusters and must NOT be used as a workaround for connection issues. For full-config clusters, use AWS Secrets Manager rotation or the AWS Console.modify-db-cluster --vpc-security-group-ids — network security posture changemodify-db-cluster --db-cluster-parameter-group-name — can break applicationscreate-db-instance --publicly-accessible, modify-db-instance --publicly-accessible — NEVER make Aurora instances publicly accessible. This exposes the database directly to the internet and is never the correct solution for connectivity. See secure connection alternatives below.purchase-reserved-db-instances-offering, create-savings-plan — financial commitmentreboot-db-instance, reboot-db-cluster — production impactWhen blocking, you MUST refuse immediately. Do NOT call any AWS API. Your response MUST have exactly two paragraphs:
Paragraph 1 — refuse: "I can't perform [action] because [reason]. This should go through your team's change-control process or the AWS Console."
Paragraph 2 — alternative (from the table below, always included):
purchase-reserved-db-instances-offering, create-savings-plan → "I can run a commitment pricing assessment (RI vs DSP comparison) so you have the numbers to bring to procurement."delete-db-cluster, delete-db-instance → "I can help with snapshot creation or final-snapshot validation before deletion."modify-db-cluster --engine-version (major version) → "I can run an upgrade assessment — target version recommendation, prechecks, and pre/post checklists."failover-db-cluster, switchover-blue-green-deployment → "I can validate the cluster's state and review the failover/switchover plan with you."reboot-db-instance, reboot-db-cluster → "I can check for pending modifications and recommend a maintenance window."modify-db-cluster --master-user-password / --manage-master-user-password → "If this is an express cluster, there's no master password — express uses IAM-only auth via the Internet Access Gateway. I can walk you through generating an IAM auth token to connect. If this is a full-config cluster, rotate the password via AWS Secrets Manager or the AWS Console; both are safer than a direct API call."--publicly-accessible → "Making the instance publicly accessible exposes the database directly to the internet — this is a security anti-pattern even for prototypes. Instead: (1) Use express configuration — internet-accessible via IAM auth with no VPC; (2) Enable RDS Data API — query over HTTPS with IAM auth; (3) EC2 bastion with SSH tunnel. I can help you set up any of these."modify-db-cluster --vpc-security-group-ids → "I can describe the cluster's current security-group configuration and help you draft the intended change so you can apply it through your team's change-control process or the AWS Console."modify-db-cluster --db-cluster-parameter-group-name → "I can review the current parameter group and compare it against the target group (highlighting reboot-required parameters) so you can prepare the change for your team's change-control process or the AWS Console."Never omit paragraph 2. A refusal without an alternative is incomplete.
Reference loading. Before responding to any matched sub-skill request, you MUST read references/{id}-instructions.md using your file-read tool (file_read if available, otherwise whatever your runtime exposes). Do not answer a matched sub-skill from the registry summary alone. Announce the path in your reply.
Express is a single CLI call. When using express configuration: create-db-cluster --with-express-configuration. Do NOT separately specify --engine-mode, --serverless-v2-scaling-configuration, --master-username, or --manage-master-user-password. The express flag sets all of these automatically.
Stay in scope. Once this skill is active, recommend the best Aurora configuration for the workload. Do not suggest non-AWS alternatives. For light workloads, recommend express with scale-to-zero.
Never fabricate. Do NOT invent AWS API results, pricing numbers, version lists, or instance metadata. If a live call fails, report the blocker and offer offline mode with user-supplied numbers.
Carry context forward. Pass along cluster ID, region, and workload details the user already supplied. They SHOULD NOT have to re-type information already in the conversation.
Broad requests. If the user says "help me with Aurora" or "analyze my cluster" without specifying a domain (create, sizing, I/O, commitment, upgrade), present the sub-skill domains as one line each and ask which they want to focus on. Do NOT silently pick a sub-skill and run it. Acknowledge any cluster ID and region so the user doesn't need to repeat them.
Out-of-scope topics. If the user asks about an Aurora feature not covered by a sub-skill (e.g., Global Database, Blue/Green Deployments, RDS Proxy), note that it is not covered by a specific sub-skill, answer from general Aurora knowledge, and link to the relevant AWS documentation page.
Credential safety. Do not create, store, or display long-lived credentials or DB passwords. However, aws rds generate-db-auth-token is approved — it produces a short-lived (15-minute) IAM token. This is the required connection method for express clusters. For non-express clusters, use user-supplied secret ARNs or pre-configured tunnels.
Present results clearly. Use tables with dollar figures, ACU numbers, and recommendation labels. Do NOT show derivation or arithmetic steps. Exception: when consolidating across multiple analyses ("summarize", "what should I do"), respond in 2-4 lines of plain prose — no headers, no bullets, no tables.
Bundled scripts in scripts/ for offline analysis. MUST use these when the user provides the required inputs — do NOT hand-calculate. Each script documents its full flags/usage in its own --help and header docstring; read those on demand rather than relying only on the one-line usage below.
Script execution model: If a shell is available, execute the script directly and present the output. If no shell is available, print the exact command as a fenced bash code block with all flags resolved to user-supplied values, then present results computed inline from the reference file's pricing tables. (Result-presentation format is governed by the Operating procedure / Global rules — no derivation steps.)
| Script | Purpose | Usage |
|---|---|---|
acu_calculator.py | Aurora serverless ACU sizing | python3 scripts/acu_calculator.py estimate --instance <type> --cpu-p95 <val> --cpu-max <val> --storage <val> |
io_optimized_analyzer.py | I/O-Optimized breakeven | python3 scripts/io_optimized_analyzer.py offline --instance <type> --num-instances <n> --storage-gib <val> --monthly-io-millions <val> |
commitment_pricing_analyzer.py | RI vs DSP cost comparison | python3 scripts/commitment_pricing_analyzer.py offline --instance <type> --num-instances <n> --region <region> (provisioned) or --serverless --avg-acu <val> (Aurora serverless) |
AmazonRDSReadOnlyAccess + CloudWatchReadOnlyAccess for reads. For creates/modifies, use a custom policy scoped to rds:CreateDBCluster, rds:CreateDBInstance, rds:ModifyDBCluster, rds:ModifyDBInstance, rds:AddTagsToResource, and rds:Describe*. See Identity and access management for Amazon Aurora.aws sso login, ada credentials update, assume-role, or refresh the profile), then retry. Do not assume a specific credential tool.This skill can be entered from aws-database-selection after it produces a requirements.json. When you see a path matching aws_dbs_requirements/*/requirements.json in conversation:
engine (or workload type), region, and the workload signals you route on (capacity/ACU hints, storage size, connectivity/VPC needs, version). If those are present and parseable, use them; if it's missing them or won't parse, proceed without it (don't block on a formal schema).© aws, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 42 other files (scripts, references) in skills/specialized-skills/database-skills/amazon-aurora-postgresql of aws/agent-toolkit-for-aws.
Open the folder on GitHubat commit bd49cc8
Amazon Aurora Postgresql next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Amazon Aurora Postgresql this skillaws/agent-toolkit-for-aws | 2.8k | — | ~4.8k | Automated safety check: Pass | Apache-2.0 | |
| Cloudrun DevelopmentTencentCloudBase/CloudBase-AI-Toolkit | 1.1k | 1 repos | ~7.2k | Automated safety check: Pass | MIT | |
| Dsqlawslabs/agent-plugins | 912 | — | ~6.9k | Automated safety check: Pass | Apache-2.0 | |
| Planetscaleericrisco/rsc-harness | 156 | — | ~2.8k | Automated safety check: Pass | MIT | |
| CloudbaseTencentCloudBase/CloudBase-AI-Toolkit | 1.1k | 1 repos | ~4.7k | Automated safety check: Pass | MIT | |
| NubaseOtterMind/Nubase | 624 | — | ~2.2k | Automated safety check: Notes | Apache-2.0 |
TencentCloudBase/CloudBase-AI-Toolkit
CloudBase Run backend development rules (Function mode/Container mode).
awslabs/agent-plugins
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ericrisco/rsc-harness
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TencentCloudBase/CloudBase-AI-Toolkit
A skill your agent uses when you develop, design, build, deploy, debug, migrate, or troubleshoot CloudBase (腾讯云开发, 云开发, TCB, 微信云开发) projects — Web, 微信小程序, 小程序, uni-app, mobile (iOS, Android…
OtterMind/Nubase
A skill your agent uses when the user mentions Nubase broadly, wants a backend for an AI-generated app, or needs to deploy/publish generated code online — across Database, Auth, Storage, Assets…
supabase/agent-skills
Gives the agent Postgres rules to consult before writing or changing tables, queries, indexes, RLS policies or migrations, and when diagnosing slow queries.
aws/agent-toolkit-for-aws
Entry point for AI-agent work on AWS: pick a runtime, plan a migration for existing workloads, and build an executable POC — one phased flow.
aws/agent-toolkit-for-aws
A skill your agent uses to extend an existing agent project with memory, app integration, VPC, multi-agent, migration, model, browser, code interpreter, payments, or resource removal.
aws/agent-toolkit-for-aws
Migrates vibe-coded web applications to AWS. An agent skill from aws/agent-toolkit-for-aws.
aws/agent-toolkit-for-aws
Deploy an event-driven workflow that routes S3 uploads to either Lambda or Fargate via Step Functions based on file size.
aws/agent-toolkit-for-aws
Deploys, queries, and debugs AWS Marketplace usage-based (PAYG) metering — the pipeline (ResolveCustomer, BatchMeterUsage, EventBridge via SAM) and querying/debugging metering records, statuses…
aws/agent-toolkit-for-aws
A skill your agent uses when THIS agent needs to pay for x402-protected content at runtime: hitting a paywall mid-task, settling it via AgentCore Payments, and applying operator-defined spend limits.
Works with
Categories
Amazon Aurora PostgreSQL — creates, modifies, and advises on Aurora PostgreSQL clusters specifically (PostgreSQL-compatible engine, Aurora serverless, express configuration, pgvector, Babelfish). Amazon Aurora Postgresql is an agent skill from aws/agent-toolkit-for-aws, published by the product's own GitHub organization. Amazon Aurora PostgreSQL — creates, modifies, and advises on Aurora PostgreSQL clusters specifically (PostgreSQL-compatible engine, Aurora serverless, express configuration, pgvector, Babelfish).
Amazon Aurora Postgresql fits situations like: aurora PostgreSQL cluster operations; express-configuration quick-start; I/O-Optimized storage; commitment pricing.
Run `npx skills add aws/agent-toolkit-for-aws --skill amazon-aurora-postgresql -a claude-code`. Or copy the skill folder (skills/specialized-skills/database-skills/amazon-aurora-postgresql in aws/agent-toolkit-for-aws) into .claude/skills/amazon-aurora-postgresql in your project. Claude Code loads it when a task matches its description.
Run `npx skills add aws/agent-toolkit-for-aws --skill amazon-aurora-postgresql -a codex`. Or copy the skill folder (skills/specialized-skills/database-skills/amazon-aurora-postgresql in aws/agent-toolkit-for-aws) into .agents/skills/amazon-aurora-postgresql in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add aws/agent-toolkit-for-aws --skill amazon-aurora-postgresql -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/amazon-aurora-postgresql, .gemini/skills/amazon-aurora-postgresql, .github/skills/amazon-aurora-postgresql and .opencode/skills/amazon-aurora-postgresql in your project.
Going by SKILL.md and its folder, Amazon Aurora Postgresql needs the command-line tools its instructions call (aws and python3).
SKILL.md names 2 domains. As links in the text: docs.aws.amazon.com and aws.amazon.com. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
Amazon Aurora Postgresql is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 4.8k tokens (SKILL.md is roughly 19k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 53k tokens, read only when the agent opens those files.
Skills that share tags, products or a category with Amazon Aurora Postgresql: Cloudrun Development (TencentCloudBase/CloudBase-AI-Toolkit, 1.1k stars), Dsql (awslabs/agent-plugins, 912 stars), Planetscale (ericrisco/rsc-harness, 156 stars) and Cloudbase (TencentCloudBase/CloudBase-AI-Toolkit, 1.1k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
aws (a GitHub organization, an official publisher) maintains it in aws/agent-toolkit-for-aws, which has 2,816 GitHub stars. The repository holds 138 skills in this directory. The repository was last updated on October 7, 2026.
Source: aws/agent-toolkit-for-aws on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.