Results for “atlassian”

Writes user-focused acceptance criteria for Jira stories using Given/When/Then scenarios, and updates the ticket via the Atlassian MCP server.

Routes project management requests to one of eight specialized skills: senior PM, scrum master, Jira, Confluence, Atlassian admin, templates, meeting analyzer, or team communications.

Search, read, create, and update Confluence pages and spaces using the confluence-cli command-line tool.

Creates structured Jira tickets from incident root cause analysis findings, including severity classification and remediation steps.

Interact with Atlassian Jira via REST API, JQL, workflow transitions, sprints, and webhooks for issue tracking and project management.

Retrieve, analyze, and update Jira tickets directly from your AI coding workflow using MCP or REST API.

Retrieves Jira tickets, analyzes requirements, updates ticket status, adds comments, and transitions issues via MCP or direct REST API.

Routes product-discovery work into the right stage — opportunity brief, research, PRD, tickets, or outcome review — and hands off to the matching sub-skill.

6 project management agent skills and plugins for Claude Code, Codex, Gemini CLI, Cursor, OpenClaw. Senior PM, scrum master, Jira expert (JQL), Confluence expert, Atlassian admin, template creator. MCP integration for live Jira/Confluence automation.

Atlassian Jira expert for creating and managing projects, planning, product discovery, JQL queries, workflows, custom fields, automation, reporting, and all Jira features. Use for Jira project setup, configuration, advanced search, dashboard creation, workflow design, and technical Jira operations.

DashScope (Alibaba Cloud Bailian / 阿里云百炼) integration — image generation (qwen-image-2.0-pro), text-to-speech (qwen3-tts-flash), and ASR with word-level timestamps (qwen3-asr-flash-filetrans). Use when generating images via Qwen-Image, narrating via Qwen-TTS, or transcribing with word-level timestamps via Qwen-ASR.

Atlas turns your STATED NEED into a real systems atlas by SCANNING your actual sources (Azure via `az`, git repos, local dirs) and process/data mining them, THEN enriching against the Atlas knowledge graph. Use this skill when asked to inventory/map your real systems, scan your cloud + repos + directories, mine the real processes or data they contain, or collect their real constraints/gotchas. (atlas, scan my systems, inventory our azure account, map my repos, real systems atlas, process mining, data mining, collect nuances, system discovery)

Fuzz Python code and C extensions with coverage guidance and AddressSanitizer support using a libFuzzer-based fuzzer.

Publishes a local Markdown file to a user-specified Confluence location, creating a new page or updating an existing one through the Atlassian MCP server. Use when the user wants to post, publish, push, or sync a Markdown file to a Confluence space or page. Requires a configured Atlassian MCP server. Does not write or generate the Markdown itself — point it at an existing file, or use project-documentation-to-confluence for the document-then-publish flow, or plan-a-feature-to-confluence for the plan-then-publish flow. Does not publish to Jira — use work-items-to-jira.

Guides setting up and using Atheris for coverage-guided fuzzing of Python code and C extensions, including Docker setup, harness writing, and corpus management.

Interact with Atlassian Jira from the terminal: search issues, view details, create issues, add comments, list projects, and transition status. Use when the user mentions Jira, a ticket key (e.g. PROJ-123), or asks about issues, bugs, tasks, projects, or sprint work.

Stand up a self-hostable, typo-tolerant search environment with Typesense — the open-source Algolia / ElasticSearch alternative (single C++ binary, <50ms instant search, no runtime deps). One routing-first skill: pick a server mode (binary download, official Docker image, or managed Typesense Cloud), install an API client (Python/JS/PHP/Ruby official; Go/Dart/C# community), design a collection schema, index documents, and run searches with typo tolerance, faceting/filtering, geo-search, sorting, grouping, synonyms, curation, scoped API keys, and federated multi-search — then wire an InstantSearch.js UI and a Raft-based HA cluster for production. Use when the user wants to build or operate an installable search backend, add site/app/product search, or migrate off Algolia/Elasticsearch. Triggers on: typesense, search engine, typo-tolerant search, algolia alternative, elasticsearch alternative, instantsearch, faceted search, geo search, vector search, self-hosted search, site search, product search.

Astropy is the core Python package for astronomy, providing essential functionality for astronomical research and data analysis.

看相分析技能。通过用户上传的人体部位图片，识别图片类型（面相/手相/体相/骨相），调用相应的相术规则知识库，结合图片内容进行专业分析。支持面相（五官、三庭、十二宫）、手相（掌线、指形、掌丘）、骨相（头骨、体骨、九骨）、体相（体型、体态、气质）的综合分析。基于《麻衣神相》《柳庄相法》《神相全编》《水镜神相》《冰鉴》等经典相书整理。

Skill for discovering and researching autonomous AI agents, tools, and ecosystems using the AgentFolio directory.

Build production-ready AI agents with type-safe tool use, structured outputs, dependency injection, and multi-model support using PydanticAI.

Astropy is the core Python package for astronomy, providing essential functionality for astronomical research and data analysis.

通用关系抽象接口。当定义实体间关系、关系网络或关系推理系统时调用此技能。

Use when asked to set up AI agent templates with coordinator mode, implement persistent agent memory, compress context for long-running agents, build multi-agent workflows with skills/agents/workflows structure, or use the .agents/ folder convention for AI-native editors. Triggers on: 'ag-kit', 'antigravity kit', 'coordinator mode agent', 'persistent agent memory', 'context compression agent', 'agent workflow template', 'multi-agent coordinator', '.agents folder', 'agent skills workflows', 'ag kit init', 'template đa agent', 'bộ nhớ agent lâu dài'.

Use for writing, reviewing, debugging, or testing Python analysis of Bayesian inference results with ArviZ, including 1.x DataTree groups, legacy InferenceData inputs, xarray dimensions and coordinates, conversion, summaries, R-hat/ESS/MCSE diagnostics, posterior predictive checks, PSIS-LOO, Pareto-k, and model comparison. Trigger on chain/draw shape errors, mislabeled groups, flattened samples, missing log likelihood, or misleading diagnostic claims. Do not use to construct or sample PyMC, NumPyro, or Bambi models, for generic plotting, or for deterministic statistics without Bayesian draws.

SQLAlchemy Python SQL toolkit and ORM with powerful query builder, relationship mapping, and database migrations via Alembic

Astropy is the core Python package for astronomy, providing essential functionality for astronomical research and data analysis.

ASCII art: pyfiglet, cowsay, boxes, image-to-ascii.

Skill for discovering and researching autonomous AI agents, tools, and ecosystems using the AgentFolio directory.

Guides authorized Active Directory security research covering Kerberos attacks, AD CS vulnerabilities, BloodHound path analysis, NTLM relay, and domain privilege escalation techniques.