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 eliecer2000/kiro-bootstrap --skill aws-dynamodbgit clone --depth 1 https://github.com/eliecer2000/kiro-bootstrapWrote 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/eliecer2000/kiro-bootstrap/aws-dynamodb)<a href="https://agentmods.dev/skills/eliecer2000/kiro-bootstrap/aws-dynamodb"><img src="https://agentmods.dev/badge/skills/eliecer2000/kiro-bootstrap/aws-dynamodb.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 5.1 | $0.00038 | $0.01820 |
| Opus 5 | $0.00019 | $0.00910 |
| Sonnet 5 | $0.00008 | $0.00364 |
| Haiku 4.5 | $0.00004 | $0.00182 |
Grade A, and why
aws-dynamodb 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 8d 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 — 188 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AWS DynamoDB
Skill para modelado de datos DynamoDB, access patterns, diseño de PK/SK, GSIs, LSIs, single-table design, transacciones, costos, DynamoDB Streams y mejores prácticas de rendimiento.
Principios fundamentales
- Modelar datos por access patterns, no por entidades. Primero definir las queries, luego diseñar la tabla.
- Single-table design cuando los access patterns están bien definidos y el equipo tiene experiencia. Multi-table cuando la simplicidad es prioridad.
- Nunca hacer Scan como patrón principal de lectura. Si necesitas Scan frecuente, el modelo de datos está mal diseñado.
- Partition key debe distribuir la carga uniformemente. Evitar hot partitions.
- Diseñar para el caso de uso más frecuente, optimizar los demás con GSIs.
Proceso de modelado de datos
- Listar todas las entidades del dominio.
- Definir todos los access patterns (queries que la aplicación necesita).
- Diseñar PK/SK para cubrir los access patterns principales.
- Agregar GSIs para access patterns secundarios.
- Documentar el modelo en una tabla de access patterns.
Tabla de access patterns (ejemplo e-commerce)
| Access Pattern | PK | SK | GSI |
|---|---|---|---|
| Obtener usuario por ID | USER#<userId> |
PROFILE |
- |
| Listar pedidos de usuario | USER#<userId> |
ORDER#<timestamp> |
- |
| Obtener pedido por ID | ORDER#<orderId> |
METADATA |
- |
| Listar items de pedido | ORDER#<orderId> |
ITEM#<itemId> |
- |
| Buscar pedidos por estado | ORDER#<orderId> |
METADATA |
GSI1: PK=STATUS#<status>, SK=<timestamp> |
| Obtener producto por SKU | PRODUCT#<sku> |
METADATA |
- |
Patrones de PK/SK
Prefijos de tipo (recomendado)
PK: USER#12345 SK: PROFILE
PK: USER#12345 SK: ORDER#2024-01-15T10:30:00Z
PK: ORDER#abc-def SK: METADATA
PK: ORDER#abc-def SK: ITEM#001
Composite sort key para queries flexibles
PK: TENANT#acme SK: USER#active#2024-01-15
→ begins_with(SK, "USER#active") → usuarios activos
→ begins_with(SK, "USER#") → todos los usuarios
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.
- 8d ago First seen · 188 lines · 38 tokens per session scan A c9d1bcc0bcf3
aws-dynamodb is a skill published in the GitHub repository eliecer2000/kiro-bootstrap (9 stars, last pushed 5mo ago), licensed MIT. It adds 38 tokens to every session and 1,820 once invoked, about $0.0002 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 2026-08-31.
Other skills, from other repositories
cognito-passkey-auth
Amazon Cognito — Custom UI with Passkeys, Social Login & Face ID. Reference skill (loaded via skill:// from the ios agent).
amazon-location-service
Amazon Location Service. Reference skill (loaded via skill:// from the ios agent).
amazon-polly-generative
Amazon Polly Generative Voices. Reference skill (loaded via skill:// from the ios agent).
amazon aurora dsql
Build with Aurora DSQL — manage schemas, execute queries, handle migrations, diagnose query plans, and develop applications with a serverless, distributed SQL database. Covers IAM auth, multi-tenant patterns, MySQL-to-DSQL migration, DDL operations, query plan explainability, and SQL compatibility validation. Triggers…
aurora dsql
Build with Aurora DSQL — manage schemas, execute queries, handle migrations, diagnose query plans, and develop applications with a serverless, distributed SQL database. Covers IAM auth, multi-tenant patterns, MySQL-to-DSQL migration, DDL operations, query plan explainability, and SQL compatibility validation. Triggers…
aws dsql
Build with Aurora DSQL — manage schemas, execute queries, handle migrations, diagnose query plans, and develop applications with a serverless, distributed SQL database. Covers IAM auth, multi-tenant patterns, MySQL-to-DSQL migration, DDL operations, query plan explainability, and SQL compatibility validation. Triggers…