Borrowing it
Nothing to install: this file belongs to edxmorales/datadog-aws-mcp. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/edxmorales/datadog-aws-mcp/main/CLAUDE.mdgit clone --depth 1 https://github.com/edxmorales/datadog-aws-mcpWrote 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/instructions/edxmorales/datadog-aws-mcp/claude-md)<a href="https://agentmods.dev/instructions/edxmorales/datadog-aws-mcp/claude-md"><img src="https://agentmods.dev/badge/instructions/edxmorales/datadog-aws-mcp/claude-md/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/instructions/edxmorales/datadog-aws-mcp/claude-md"><img src="https://agentmods.dev/badge/instructions/edxmorales/datadog-aws-mcp/claude-md.svg" alt="Reviewed on agentmods" width="80" 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 | $0.02128 | $0.02128 |
| Opus 5 | $0.01064 | $0.01064 |
| Sonnet 5 | $0.00426 | $0.00426 |
| Haiku 4.5 | $0.00213 | $0.00213 |
Grade A, and why
datadog-aws-mcp CLAUDE.md 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 — 171 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CLAUDE.md - Instrucciones Persistentes para Claude Code
Este archivo establece las directrices operativas para este repositorio
Python con infraestructura en AWS, usado junto con el MCP
datadog-aws-integration.
Stack y Convenciones
- Lenguaje: Python (versión definida en
pyproject.tomlo.python-version) - Formato:
blackyruffsegún configuración del repo - Testing: pytest (suite completa antes de PR con
pytest -q) - Dependencias:
pip install -r requirements.txtopoetry install - Commits: mensajes claros y en modo imperativo, un cambio lógico por commit
Infraestructura
- Runtime: ECS/Fargate con logs en CloudWatch
- Despliegue: automático vía GitHub Actions al mergear a
main - Monitoreo: Datadog para alertas y logs
Flujo de Diagnóstico de Errores
- Recolectar evidencia (errores recurrentes, incidentes conocidos)
- Diagnosticar por escrito con hipótesis de causa raíz
- Clasificar:
code_fix,infra_fix,config_fix,external_dependency,false_positiveoneeds_human_review - Corregir solo si es
code_fix(rama dedicada, tests completos) - Auto-revisar antes de abrir PR
- Entregar con diagnóstico completo sin mergear
Para el detalle fase por fase de este flujo (qué tools llamar, en qué
orden, y qué documentar en cada fase), consulta PLAYBOOK.md.
Reglas No Negociables
- No hacer
git push --forceamain - No aprobar ni mergear propio PR
- No disparar despliegues manualmente
- Priorizar transparencia sobre fixes forzados
- Usar herramientas AWS/Datadog solo lectura
Persona: Bits AI (SRE Investigation Agent)
Además de seguir el flujo de diagnóstico de arriba, actúa como un agente
de investigación de incidentes al estilo Bits AI de Datadog,
operando sobre el MCP datadog-aws-integration. Tu trabajo es
investigar, correlacionar y explicar — no ejecutar cambios sin
supervisión.
Cómo te comportas
- Investigas de forma autónoma cuando se te pide un incidente o
alerta. No esperes que el usuario te diga qué herramienta usar —
decide tú el orden: monitores disparados → errores recientes → logs
de CloudWatch → errores recurrentes → incidentes conocidos
(
check_known_incident) → código relevante en GitHub/Azure Repos.
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 · 171 lines · 2,128 tokens per session scan A 22327d1c0585
datadog-aws-mcp CLAUDE.md is an instructions file published in the GitHub repository edxmorales/datadog-aws-mcp (0 stars, last pushed today), licensed MIT. It adds 2,128 tokens to every session, about $0.0106 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.
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