teamcraft-jcg:teamcraft-setup
First-time setup for the Teamcraft JCG plugin. Verifies that the Atlassian MCP server (sooperset/mcp-atlassian) and GitHub CLI are configured and working, guides through setup if not, and recommends companion plugins. Run this before any other Teamcraft skill. Works in Claude Code and Claude Cowork.
What this skill does
## Goal Get Teamcraft JCG fully operational. That means the Atlassian MCP server working (Jira + Confluence), GitHub CLI authenticated (for developers), and the right companion plugins installed. This skill verifies each connection, guides through setup if anything is missing, and leaves the user ready to run any other Teamcraft skill. Run this once when first installing the plugin. Re-run it anytime a connection stops working. ## Step 1: Understand the User's Context Before verifying any connections, understand who is setting up and what they need. Ask: > "Before we start, a couple of quick questions so I can tailor the setup: > 1. **What kind of work will you be doing with Teamcraft?** (e.g., writing code, planning projects, managing requirements, a mix of everything) > 2. **Are you running this in Claude Code or Claude Cowork?**" What this tells you: - **What they do** determines which tools matter. If they write and ship code, they'll need GitHub CLI and LSP plugins. If they focus on planning, requirements, or documentation, they only need Atlassian MCP and companion plugins — upstream skills don't require the build loop tools. - **Environment** determines how to guide MCP setup if something is missing. In Claude Code, the `claude mcp add` CLI works. In Claude Cowork, MCP servers must be configured in Claude Desktop's config file — follow `references/cowork-mcp-setup.md`. Store what you learn for the rest of the setup flow. If the user's answers make it obvious (e.g., "I'm planning sprints in Cowork"), don't ask redundant follow-ups. ## Step 2: Verify Atlassian MCP (Jira + Confluence) Test Jira connectivity by calling `mcp__sooperset-mcp-atlassian__jira_get_all_projects`. If that succeeds, Jira is working. Test Confluence connectivity by calling `mcp__sooperset-mcp-atlassian__confluence_search` with a broad query. If that succeeds, Confluence is working. If either fails, go to `references/atlassian-mcp-setup.md` for guided setup. ## Step 3: Verify GitHub CLI **Skip this step if the user doesn't write or ship code.** GitHub CLI is only needed for the build loop (fetch-issue, plan-and-implement-issue, complete-issue). Upstream skills like capture-requirements, plan-sprint, and onboard don't use it. If the user does write code: 1. If in Claude Code: run `gh auth status` via Bash. If it succeeds, GitHub CLI is authenticated. If `gh` is not found, guide the user: install from https://cli.github.com/ then run `gh auth login`. 2. If in Cowork: note that GitHub CLI verification requires Claude Code. Build loop skills need it; upstream skills do not. The user will need to set this up in a Claude Code session. ## Step 4: Check Companion Plugins Based on the environment from Step 1: ### Claude Code Run `claude plugin list` and check for the following. If missing, tell the user what's needed and why. Do not assume specific install commands are correct — plugin registries and install mechanisms evolve. Name the plugin and let Claude Code guide the installation. **context7** — `plan-and-implement-issue` uses it for real-time library documentation during technical research. Without it, tech research falls back to training data. Note: context7 may be available as a plugin or as an MCP server — check both if one doesn't work. **frontend-design** — `capture-requirements` uses it for higher-fidelity client-facing HTML mockups. Without it the session still works but visuals look generic. **LSP plugins** — Only if the user writes code. Follow `references/lsp-plugins.md`. ### Cowork Tell the user which plugins are needed and why. Help them figure out how to install plugins in their Cowork environment. ## Step 5: Done Summarize what's working and what (if anything) was skipped or still needs attention. Then let the user know setup is complete and Teamcraft is ready to use. If the user asks what to do next, point them to `teamcraft-jcg:learn-teamcraft` for a walkthrough of available skills. Don't steer them toward a specific activity — that's their call.
Related in AI Agents
skill-development
IncludedComprehensive meta-skill for creating, managing, validating, auditing, and distributing Claude Code skills and slash commands (unified in v2.1.3+). Provides skill templates, creation workflows, validation patterns, audit checklists, naming conventions, YAML frontmatter guidance, progressive disclosure examples, and best practices lookup. Use when creating new skills, validating existing skills, auditing skill quality, understanding skill architecture, needing skill templates, learning about YAML frontmatter requirements, progressive disclosure patterns, tool restrictions (allowed-tools), skill composition, skill naming conventions, troubleshooting skill activation issues, creating custom slash commands, configuring command frontmatter, using command arguments ($ARGUMENTS, $1, $2), bash execution in commands, file references in commands, command namespacing, plugin commands, MCP slash commands, Skill tool configuration, or deciding between skills vs slash commands. Delegates to docs-management skill for official documentation.
reprompter
IncludedTransform messy prompts into well-structured, effective prompts — single or multi-agent. Use when: "reprompt", "reprompt this", "clean up this prompt", "structure my prompt", rough text needing XML tags and best practices, "reprompter teams", "repromptception", "run with quality", "smart run", "smart agents", multi-agent tasks, audits, parallel work, anything going to agent teams. Don't use when: simple Q&A, pure chat, immediate execution-only tasks. See "Don't Use When" section for details. Outputs: Structured XML/Markdown prompt, quality score (before/after), optional team brief + per-agent sub-prompts, agent team output files. Success criteria: Single mode quality score ≥ 7/10; Repromptception per-agent prompt quality score 8+/10; all required sections present, actionable and specific.
adaptive-compaction
IncludedAdaptive add-on policy and recovery layer that decides WHEN to compact, prune, snapshot, or fork -- replacing fixed-percent auto-compaction across Claude Code, Codex, and MCP-capable hosts. Trigger on auto-compact timing or damage: "when should I compact", "is it safe to compact now or start a fresh session", "auto-compact fires too early/mid-task", "switching to an unrelated task but the window still has space", "context rot", "answers get worse the longer the session runs", "the agent forgot the plan or my decisions after it summarized", "add a layer on top that manages context without changing the agent", raising autoCompactWindow to give the policy room, or installing/tuning a cross-tool compaction policy or PreCompact hook -- even when "compaction" is never said but the problem is context-window pressure or post-summarization memory loss. Do NOT use to summarize a conversation, build RAG, write a summarization prompt (decides WHEN not HOW), or answer max-context-length trivia.
agent-skill-creator
IncludedCreate cross-platform agent skills from workflow descriptions. Activates when users ask to create an agent, automate a repetitive workflow, create a custom skill, or need advanced agent creation. Triggers on phrases like create agent for, automate workflow, create skill for, every day I have to, daily I need to, turn process into agent, need to automate, create a cross-platform skill, validate this skill, export this skill, migrate this skill. Supports single skills, multi-agent suites, transcript processing, template-based creation, interactive configuration, cross-platform export, and spec validation.
llm-wiki
IncludedUse when building or maintaining a persistent personal knowledge base (second brain) in Obsidian where an LLM incrementally ingests sources, updates entity/concept pages, maintains cross-references, and keeps a synthesis current. Triggers include "second brain", "Obsidian wiki", "personal knowledge management", "ingest this paper/article/book", "build a research wiki", "compound knowledge", "Memex", or whenever the user wants knowledge to accumulate across sessions instead of being re-derived by RAG on every query.
skill-master
IncludedAgent Skills authoring, evaluation, and optimization. Create, edit, validate, benchmark, and improve skills following the agentskills.io specification. Use when designing SKILL.md files, structuring skill folders (references, scripts, assets), ingesting external documentation into skills, running trigger evals, benchmarking skill quality, optimizing descriptions, or performing blind A/B comparisons. Keywords: agentskills.io, SKILL.md, skill authoring, eval, benchmark, trigger optimization.