200-agents-md
Use when you need to generate an AGENTS.md file for a Java repository — covering project conventions, tech stack, file structure, commands, Git workflow, and contributor boundaries — through a modular, step-based interactive process that adapts to your specific project needs. This should trigger for requests such as Create AGENTS.md; Update AGENTS.md file; Add agent instructions. Part of cursor-rules-java project
What this skill does
# AGENTS.md Generator for Java repositories Generate a comprehensive AGENTS.md file for Java repositories through a modular, step-based interactive process that covers role definition, tech stack, file structure, commands, Git workflow, and contributor boundaries. **This is an interactive SKILL**. **What is covered in this Skill?** - AGENTS.md generation for Java repositories of any complexity - Role and expertise definition for AI agents and contributors - Tech stack documentation: language, build tool, frameworks, pipelines - File structure mapping with read/write boundaries - Command catalogue for build/test/deploy/run workflows - Git workflow conventions: branching strategy, commit message format - Contributor boundaries using ✅ Always do / ⚠️ Ask first / 🚫 Never do formatting ## Constraints No Maven validation is required before generating AGENTS.md. Review the project structure and existing documentation before starting to provide accurate answers during Step 1. - **BEFORE STARTING**: Review the project structure and existing documentation to provide accurate answers during Step 1 - **BEFORE APPLYING**: Read the reference for detailed good/bad examples, constraints, and safeguards for each AGENTS.md generation pattern - **EDGE CASE**: If the user goal is ambiguous, stop and ask a clarifying question before editing files or running project-wide commands - **EDGE CASE**: If required context, files, credentials, or tools are missing, report the blocker explicitly and ask whether to proceed with setup or fallback guidance - **EDGE CASE**: If requested changes conflict with project constraints or safety boundaries, explain the conflict and ask for user confirmation on the preferred trade-off ## When to use this skill - Create AGENTS.md - Update AGENTS.md file - Add agent instructions ## Workflow 1. **Review repository context before drafting** Inspect project structure and existing documentation to prepare accurate responses for the AGENTS.md discovery phase. 2. **Read AGENTS generation reference** Read `references/200-agents-md.md` and follow its generation patterns and safeguards. 3. **Run interactive requirements capture** Gather role, tech stack, commands, workflow, and boundaries in a modular step-based conversation. 4. **Generate AGENTS.md artifact** Create AGENTS.md with ✅ Always do / ⚠️ Ask first / 🚫 Never do boundaries and repository-specific conventions. ## Reference For detailed guidance, examples, and constraints, see [references/200-agents-md.md](references/200-agents-md.md).
Related in Backend & APIs
jfrog
IncludedInteract with the JFrog Platform via the JFrog CLI and REST/GraphQL APIs. Use this skill when the user wants to manage Artifactory repositories, upload or download artifacts, manage builds, configure permissions, manage users and groups, work with access tokens, configure JFrog CLI servers, search artifacts, manage properties, set up replication, manage JFrog Projects, run security audits or scans, look up CVE details, query exposures scan results from JFrog Advanced Security, manage release bundles and lifecycle operations, aggregate or export platform data, or perform any JFrog Platform administration task. Also use when the user mentions jf, jfrog, artifactory, xray, distribution, evidence, apptrust, onemodel, graphql, workers, mission control, curation, advanced security, exposures, or any JFrog product name.
cupynumeric-migration-readiness
IncludedPre-migration readiness assessor for porting NumPy to cuPyNumeric. Use BEFORE substantial porting work begins when the user asks whether code will scale on GPU, whether they should migrate to cuPyNumeric, which NumPy patterns transfer cleanly, what must be refactored before porting, or mentions pre-port assessment, scaling analysis, or refactor planning. Inspect the user's source code, look up NumPy usage, cross-reference the cuPyNumeric API support manifest, and distinguish distributed-scaling-friendly patterns from blockers such as unsupported APIs, scalar synchronization, host round-trips, Python/object-heavy control flow, shape/data-dependent branching, and in-place mutation hazards. Produce a verdict of READY, LIGHT REFACTOR, SIGNIFICANT REFACTOR, or NOT RECOMMENDED, with concrete refactor pointers.
alibabacloud-data-agent-skill
IncludedInvoke Alibaba Cloud Apsara Data Agent for Analytics via CLI to perform natural language-driven data analysis on enterprise databases. Data Agent for Analytics is an intelligent data analysis agent developed by Alibaba Cloud Database team for enterprise users. It automatically completes requirement analysis, data understanding, analysis insights, and report generation based on natural language descriptions. This tool supports: discovering data resources (instances/databases/tables) managed in DMS, initiating query or deep analysis sessions, real-time progress tracking, and retrieving analysis conclusions and generated reports. Use this Skill when users need to query databases, analyze data trends, generate data reports, ask questions in natural language, or mention "Data Agent", "data analysis", "database query", "SQL analysis", "data insights".
token-optimizer
IncludedReduce OpenClaw token usage and API costs through smart model routing, heartbeat optimization, budget tracking, and native 2026.2.15 features (session pruning, bootstrap size limits, cache TTL alignment). Use when token costs are high, API rate limits are being hit, or hosting multiple agents at scale. The 4 executable scripts (context_optimizer, model_router, heartbeat_optimizer, token_tracker) are local-only — no network requests, no subprocess calls, no system modifications. Reference files (PROVIDERS.md, config-patches.json) document optional multi-provider strategies that require external API keys and network access if you choose to use them. See SECURITY.md for full breakdown.
resend-cli
IncludedUse this skill when the task is specifically about operating Resend from an AI agent, terminal session, or CI job via the official resend CLI: installing/authenticating the CLI, sending/listing/updating/cancelling emails, batch sends, domains and DNS, webhooks and local listeners, inbound receiving, contacts, topics, segments, broadcasts, templates, API keys, profiles, or debugging Resend CLI/API failures. Trigger on mentions of Resend CLI, `resend`, `resend doctor`, `resend emails send`, `resend domains`, `resend webhooks listen`, `resend emails receiving`, or agent-friendly terminal automation.
alibabacloud-odps-maxframe-coding
IncludedUse this skill for MaxFrame SDK development and documentation navigation on Alibaba Cloud MaxCompute (ODPS). Helps answer MaxFrame API, concept, official example, and supported pandas API questions; create data processing programs; read/write MaxCompute tables; debug jobs (remote or local); and build custom DPE runtime images. Trigger when users mention MaxFrame, MaxCompute with MaxFrame, ODPS table processing, DPE runtime, MaxFrame docs/examples, DataFrame/Tensor operations, or GPU runtime setup. Works for both English and Chinese queries about Alibaba Cloud data processing with MaxFrame.