teamcraft-glgd:create-issue
Create a single well-formed GitLab issue at any time — bugs found in production, features requested by stakeholders, technical debt noticed during development, chores that need doing. Follows the same issue standards as plan-sprint, always. Works in Claude Cowork and Claude Code.
What this skill does
## Goal Create one well-formed GitLab issue that gives a developer everything they need to pick it up cold. The same standards apply regardless of when or why the issue is created. One issue. Explicit confirmation. No exceptions. ## Identify the Project Use `mcp__gitlab__list_projects` to see what is visible, surface the results, and ask the user which project this issue belongs to. Never ask the user to supply a namespace string. Confirm once identified. ## Resolve Drive Account Call `mcp__google-drive__list_accounts` before any other Drive operation: - **No accounts** — Drive is not configured for this user. Tell them and skip Drive operations. - **One account** — Use it. Pass `account_email` explicitly on every Drive tool call this session. - **Multiple accounts** — Present the list, ask which account to use for this session. Pass that `account_email` on every Drive tool call. If any Drive call returns a permission error, surface it: the active account may not have access to that file or folder. Offer to try another account if one is available. If a Drive file operation fails with a path error, read the error message to identify a valid accessible host path and retry with it. ## Load Context Ask the user if they can point at the tech decisions and conventions documents in Google Drive. If they can, use `mcp__google-drive__download_file` to load them directly. If they cannot, ask whether to search Drive. These documents inform the technical guidance section of the issue. If nothing is available, proceed without — the user's description carries sufficient context. ## Understand the Issue Use `$ARGUMENTS` as the starting point if provided. Determine issue type — feature, bug, or chore. Ask if not clear from the description. ## Draft and Confirm Before drafting, read the reference file matching the issue type — `references/example-feature-issue.md`, `references/example-bug-issue.md`, or `references/example-chore-issue.md`. These define the required structure. Every issue must include all sections from the reference: Background & Goal, Acceptance Criteria, Technical Guidance, and Testing Requirements (for features and bugs) or Verification (for chores). An issue missing any of these sections is incomplete. Show the draft. Get explicit confirmation before creating. Never create without approval. ## Create Create the issue in GitLab. If open milestones exist, offer to assign it to one. Report the IID and URL. ## PRD Integrity Check After creating the issue, assess whether it represents a capability or requirement not currently reflected in the PRD. If it does — a new feature, a constraint discovered in implementation, a stakeholder request that extends original scope — flag it explicitly: > "This issue adds capability X that is not in the current PRD. If this reflects a real change in scope, the PRD should be updated so all agents and team members work from accurate requirements. Would you like to update it now?" If the user says yes, ask them to point at the PRD in Drive and update the relevant section. One atomic action — the issue and the PRD stay in sync at the moment scope changes, not weeks later.
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.