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Installer avec Codex ou Claude Copiez ce prompt, collez-le dans Codex, Claude ou un autre assistant, puis laissez-le vérifier la page du skill et l'installer pour vous.
Basé sur la classification professionnelle SOC
Build production-ready AWS Bedrock AgentCore Harness agents end to end — declarative model + system prompt, managed or BYO Memory, built-in Browser and Code Interpreter, Gateway/MCP tools, inline functions, Skills, versioning + endpoints (prod rollout/rollback), advanced config (truncation, limits, lifecycle, network, inbound auth), Observability (log delivery + tracing), Evaluations, Optimizations, Identity (outbound auth, Token Vault, credential providers), Policy guardrails, Payments, and the Agent Registry. Use whenever the user wants to create, configure, deploy, version, wire, harden, invoke, or troubleshoot an AgentCore Harness — or asks about AgentCore best practices, harness.json, CreateHarness/UpdateHarness/InvokeHarness, harness endpoints/qualifiers, attaching Memory, wiring browser/code-interpreter, adding skills, observability/log delivery, or A/B-testing prompts. Trigger even when the user describes a managed, declarative Bedrock agent with tools/memory/skills without saying "harness".
Build, extend, or debug GitLab CI/CD pipelines (.gitlab-ci.yml) that run Kiro CLI in headless mode for AI-powered merge-request review, security scanning, test-failure analysis, config-drift detection, code-duplication sync checks, and change-impact/risk analysis — including integrating the customer's MCP servers (SonarQube SAST, Jira, internal tools) into CI jobs. Use this skill whenever the user mentions GitLab CI, .gitlab-ci.yml, GitLab pipelines, merge-request automation, AI code review in CI, Kiro CLI in CI/CD, kiro-cli headless or non-interactive mode, MCP servers in pipelines, or wants agentic/LLM checks in a pipeline — even if they don't say "Kiro" or "GitLab CI" explicitly (e.g. "add an AI reviewer to my MRs", "hook SonarQube into the AI review", "make the pipeline catch config drift").
Maps code, architecture, and infrastructure changes to specific control IDs in financial services compliance frameworks - PCI-DSS v4.0 for payment card data and MAS TRM for Singapore-regulated institutions - producing an audit-traceable findings report with per-control remediation. Use this instead of a general security review whenever a compliance framework (PCI-DSS, MAS TRM), regulator, audit, or cardholder-data scope is mentioned, even if the request is phrased as a code review or a yes/no compliance question. Triggers on: "PCI-DSS check", "MAS TRM", "is this compliant", "compliance review", "audit this change for banking regulations", "does logging this violate PCI", "cardholder data handling review".
Reviews AWS architectures, IaC, and design docs against the AWS Well-Architected Framework's six pillars, producing a findings report with pillar-mapped risks (High/Medium) and concrete remediation. Loads only the pillars relevant to the change. Use for AWS architecture reviews, not generic code review. Triggers on: "well-architected review", "review this AWS architecture", "WAR review", "check this design against AWS best practices", "review my Terraform/CDK for AWS pitfalls", "is this architecture production-ready".
Infers a codebase's implicit coding conventions (naming, structure, imports, comments, error handling) by analyzing the existing code, then makes them explicit: generates linter/formatter configs that match current reality, a conventions document, and agent steering rules so AI-written code blends in. Use when adopting AI coding tools on an existing codebase or onboarding to an unfamiliar team style. Triggers on: "match our coding style", "infer our conventions", "generate lint config from this codebase", "make the AI write code like our team", "extract our code standards", "set up steering rules from existing code".
Adds tests to legacy code that has none, safely: characterization tests that pin current behavior (including bugs) before any refactoring, seam identification for breaking untestable dependencies, and a risk-ranked coverage strategy. Use for untested or inherited codebases. Triggers on: "add tests to legacy code", "this code has no tests", "characterization tests", "make this testable", "safe to refactor?", "pin down current behavior", "test this old module before we change it".
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| description | Replace with a clear description of what this skill does and when the AI assistant should use it. Include trigger phrases like "when user says X" to help with activation. |
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Step-by-step instructions the AI assistant should follow when this skill is triggered:
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references/ # Reference docs, style guides, or specs
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