reinvent-stack-toolkit
Skill8 starsChanged 41 days agoApache-2.0View on GitHub
Technical patterns and known-only-by-live-invocation pitfalls for the reinvent agent's fixed stack. Covers how to wire services correctly; says nothing about what to build. Read this BEFORE writing any code for a new project on this stack. Only fall back to live AWS docs search when a value here looks stale or the project needs something this skill doesn't cover.
In this repository17 other files it gives its agents.
- AGENTSAGENTS.md
- amazon-bedrockSkill
- amazon-location-serviceSkill
- amazon-polly-generativeSkill
- aws-architecture-diagramSkill
- aws-diagram-htmlSkill
- aws-diagram-pngSkill
- aws-serverless-patternsSkill
- cdk-infrastructure-patternsSkill
- cognito-email-migrationSkill
- cognito-passkey-authSkill
- deploy-on-awsSkill
- email-template-renderingSkill
- mcp-tool-discoverySkill
- personal-rules-managementSkill
- react-frontend-patternsSkill
- testing-patternsSkill
The file
We split this skill into the rules it gives an agent
1 rule. About 0 are common, 0 are distinctive, and 1 is very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈0
Distinctive · estimate
This author's own choices. Worth a look.
1
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 9
Trust policy: every ARN, model ID, and pattern below was verified against this account's real infrastructure as of the verified date above.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
reinvent Stack Toolkit
Trust policy: every ARN, model ID, and pattern below was verified against this
account's real infrastructure as of the verified date above. Treat them as correct
without re-querying live AWS for roughly 90 days from that date. Only re-verify a
specific value live if it actually fails at build or deploy time (a stale ARN, a
rejected model ID, a changed API shape). If something here turns out stale, fix it here
and bump the date, so the next build doesn't re-pay the same research cost.
Observability is not covered here. The build prompt specifies it per project.
Verified ARNs & versions (quick reference)
Pinned values confirmed against live AWS docs/account state as of the verified date
above. Use these directly without re-querying; only re-verify if one fails at build or
deploy time, per the trust policy.
| Value | Pinned | Scope / caveat |
| ADOT Lambda layer for Python | arn:aws:lambda:<region>:901920570463:layer:aws-otel-python-<arch>-ver-1-32-0:7 (<arch> = amd64 or arm64) | Python 3.8-3.13 only. This stack's Lambdas run python3.14 -- the layer is NOT compatible, confirmed by live failure (No module named 'aws_xray_sdk' at cold start). Keep this ARN on file for the day this stack's Python version drops to 3.13 or earlier, or for a sibling project pinned to an older runtime. For python3.14 today, skip ADOT entirely -- see references/pitfalls.md item 4 for the Powertools+aws-xray-sdk alternative this stack actually uses. |
amazon.nova-lite-v1:0 | bare model ID (no inference profile needed) | Confirmed invokable on-demand directly via Converse in this account/region (us-east-1) as of 2026-07-14. Correction: amazon.nova-lite-v2:0 does not exist in this account/region and fails Converse with ValidationException: The provided model identifier is invalid. The newer amazon.nova-2-lite-v1:0 exists but is INFERENCE_PROFILE-only (needs us.amazon.nova-2-lite-v1:0 or global.amazon.nova-2-lite-v1:0) -- amazon.nova-lite-v1:0 is the correct bare on-demand ID. If a ValidationException about invalid model identifier appears again, re-run aws bedrock list-foundation-models --query "modelSummaries[?contains(modelId,'nova')].modelId" before trusting any pinned Nova ID here. |
What's here
references/pitfalls.md-- read this in full before the first deploy, not after the first failure. Every entry passed static validation and a clean deploy on a prior build and only broke on real invocation.- Technical pattern files:
references/bedrock-structured-extraction-pattern.py-- Converse with forced tool-use for structured output from an image, document, or text input
references/frontend-toolkit-notes.md-- Vite + Tailwind v4 + shadcn/ui setup gotchas (including the stray-@-directory bug onshadcn init) and the icon-generation snippet.
The pitfalls that cost the most time historically
Full detail and fixes in references/pitfalls.md -- this is just the index:
sam sync --codecan desync a function's separately-managed dependency layer without any error. Never use it mid-build; always redeploy with the fullsam deploy --expresspath.- Bedrock Converse: a specific model may (a) require an inference profile ID instead
of the bare model ID, and (b) reject
inferenceConfigfields liketemperatureas deprecated for that model generation. Both fail only on real invocation, never on deploy. OmitinferenceConfigunless a field is confirmed supported.
Relationship to the shared skills
This skill is attached only to the reinvent agent. aws-serverless-patterns.md is
also attached and remains the authority for general Lambda/API Gateway/DynamoDB/
EventBridge patterns shared with the CDK-based agents (DynamoDB Decimal requirement,
wait_for_callback raw-string parsing, EventBridge envelope unwrapping and the Lambda
permission a rule target needs).
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.