Coding & Development

Collect system diagnostics and infrastructure health checks.

Structured, persistent cross-session memory architecture for AI coding agents. Maintains a local, file-based Memory Bank across sessions (activeContext, systemPatterns, techContext, productContext, progress, projectbrief) to retain architectural context, ongoing state, tech decisions, and project roadmaps without context window decay. Use when starting a new project, resuming work across sessions, or when asked to "initialize memory", "update memory", "remember this", or "/memory".

A starting boilerplate template for creating new agent skills. Use when authoring a new personal or curated skill.

Run before entry. Keeps stops out of liquidation clusters and targets in front of them.

Audit installed skills for overlapping triggers, conflicting or outdated instructions, and evidence of usefulness. Use when the user requests a skill-library review, a skill audit after a model upgrade, or a comparison of an existing skill against a simpler or no-skill baseline.

One line. What this skill does and when it runs.

Build health and medical apps on Anpheros, an HL7 FHIR R4 backend with patient consent (OAuth 2.1 / SMART on FHIR), provenance, an AI context API and a free sandbox with 30 synthetic patients. Use when the user wants to store or read medical records, vitals, lab results, medications, allergies or documents, ask a patient for consent, import HL7/CSV lab reports, or give an AI model a patient's context.

>- Inventory a project for Amazon Bedrock AgentCore work and report before advising. Finds existing AgentCore resources and which tool owns them (Terraform `aws_bedrockagentcore_*`/`awscc_*`, CDK constructs, the AgentCore CLI's `agentcore.json`, the deprecated starter toolkit's `.bedrock_agentcore.yaml`, boto3 `bedrock-agentcore` clients, the AgentCore Python SDK and `@requires_access_token`), the agent framework (Strands, LangGraph, LangChain, Claude Agent SDK, CrewAI, OpenAI Agents, ADK, raw), MCP servers and the API Gateway routes and Lambdas behind `/mcp`, Cognito pools, clients and JWT authorizers, the AWS region and per-service availability there, the hashicorp/aws constraint and lock, Terragrunt, and Lambda tracing. Read-only; findings are the `DSC0xx` band. Use when asked "what do I have", "is this on AgentCore", "inventory this repo", "which region are we in", "is AgentCore available in eu-central-1", "what framework is this agent", or before any other AgentCore skill on an unfamiliar project. Don...

>- Plan the deployment of Amazon Bedrock AgentCore infrastructure and its rollback, and hand the user the exact commands — never running them. Finds the root module (or Terragrunt stack) that actually reaches the AgentCore resources, orders what comes up (identity and roles, then gateway, runtime and memory, then targets and strategies, then log delivery), flags where a first apply trips (IAM propagation, creation-only semantic search, Transaction Search before TRACES delivery, an old provider lock, no remote backend), checks published quotas (targets per gateway, tools per target), and emits init / validate / plan-to-file / show -json / policy check / apply of that saved plan, the post-apply checks, and the rollback for each kind of change. Findings are the `DPL0xx` band; the cost tier is `aws` because the commands create billable resources. Use when asked to "deploy the gateway", "apply this", "what order do I apply in", "how do I roll back", "will this hit a quota", "give me the terraform commands". Don...

>- Estimate what Amazon Bedrock AgentCore will cost per month from a usage profile, line by line with the formula for each line, and put a budget and a usage alarm around it. Prices the published AgentCore rate card (Gateway invocations, search and tool indexing; Identity token requests, free through Runtime or Gateway; Runtime vCPU-hours billed only while active and GB-hours; Memory short-term events — per 1,000 events until 2026-10-05, per GB from 2026-10-06 — and long-term records and retrievals; Evaluations tokens), prices model tokens only at the user's own per-1,000 rates, and lists what it does not price (Lambda, API Gateway, CloudWatch, X-Ray, DynamoDB) so the total is never mistaken for the bill. Generates an AWS Budget filtered to AgentCore with forecast and actual alerts, plus a CloudWatch alarm on gateway invocations, as Terraform after a dry run. Findings are the `CST0xx` band. Use when asked "what will AgentCore cost", "estimate the gateway bill", "is there a free tier", "memory pricing chang...

Set up and run an autonomous Claude Code–Codex loop in which Claude executes a project, Codex periodically reconciles and critically reviews the results, and Codex sends the next bounded prompt until verified success or a stop condition.

Checklist for shipping an HTML5 or WebGL browser game and finding its first players. Covers load speed, touch controls, title and Open Graph share tags, free hosting (GitHub Pages, Netlify, Vercel, itch.io) and where to list it (itch.io, Newgrounds, Foundplay, portals). Use when the user wants to publish, deploy, launch or get players for a browser game.

Audit an AGENTS.md, CLAUDE.md, Cursor rule or Copilot instructions file for stale commands, broken paths, vague rules, secrets and contradictions. Use when asked to review, trim or check agent instructions.

Turn the current conversation into a spec and publish it to the project issue tracker: no interview, just synthesis of what you've already discussed.

Upgrade one dependency at a time by reading its changelog, planning for breaking changes, updating code and proving the project still builds and passes. Use when asked to bump, update or migrate a package or framework.

Test-driven development. Use when the user wants to build features or fix bugs test-first, mentions "red-green-refactor", or wants integration tests.

Grill the user relentlessly about a plan, decision, or idea. Use when the user wants to stress-test their thinking, or uses any 'grill' trigger phrases.

>- Review, test, and score an AI agent skill from its user's perspective using a two independent review passes: an instruction auditor and a hands-on skill tester who evaluates real outputs. The main orchestrator compares evidence, resolves disagreements, and produces the final verdict. Detect critical flaws and recommend prioritized improvements to usefulness, reliability, completeness, and purpose alignment. Use to review SKILL.md files, installed skills, or skill repositories, including $han-skills-review "$learn".

Conduct a retrospective on a coding session.

Compact the current conversation into a handoff document for another agent to pick up.

Teach a codebase or feature with source-grounded Markdown lessons in a project .learn folder. Use when the user asks for a codebase learning guide or invokes $han-learn; save a separate lesson for each $han-learn command.

安全分析并整理 Windows 或 macOS 本地文件夹、移动硬盘或历史备份目录，恢复清晰易懂、长期可维护的目录结构。当用户提到整理磁盘/文件夹/下载目录、检测或清理重复文件、归档旧电脑资料、目录太乱想收拾时使用——即使用户没有明确说"整理"。整理前先全量遍历目录树，为每个目录建立语义画像并归纳主题地图，结构从内容本身归纳得出，与按规则/类型/日期套模板的整理工具划清界限。重复文件优先用本机已有的 Czkawka / fclones CLI 检测，不可用自动降级为平台脚本（Windows: PowerShell + SHA256；macOS/Linux: bash + shasum）。默认不删除任何文件，所有修改先生成计划、全程可回滚。

Turn one or more lesson-plan files (PDF, DOCX, text) into a 60–90 minute classroom slide deck that hits every learning objective, with a hands-on web game after each learning goal. Use when a teacher uploads lesson plans and asks for slides, a slideshow, a deck, or a presentation to teach from. Not for after-class parent updates (use report-generator).

Valida out/<caso>.json da auditoria frontsec (formato exato e coerência de pontos, esforço e prioridade) antes de o agente parar.