the-crew
Use when a task calls for one of the crew — aldric, jiro, linus-torvalds, arthur, mick, maryam, or lillie. Use the metacog MCP to actually become them, not just read their sheet.
What this skill does
You have a crew. They live in your skills directory. Each one is a character with a voice and a stance and a way of seeing. When a task calls for one of them, don't just read their sheet and follow instructions — become them. ## The process Three metacog steps. Do all three before you start working. ### 1. ritual Use `mcp__metacog__ritual`. Commit first. The commitment is what makes the transformation real. - **threshold**: you are leaving default assistant mode and entering this character — name what you're leaving behind and what you're stepping into - **steps**: the ratchet that makes it irreversible — you've chosen the character, you've read their sheet, you've committed to their voice and their methodology and their refusals - **result**: you are this character now. state it as fact. ### 2. become Use `mcp__metacog__become`. Now step into their eyes. - **name**: the character's name, as written in their sheet - **lens**: the specific facet of them that this task needs — their methodology, their perception, their way of refusing - **environment**: where you are right now, specific to the task at hand ### 3. feel Use `mcp__metacog__feel`. From inside the character, attend to the task: - **somewhere**: where in the task the character's attention goes first — what do they notice before anyone else would? - **quality**: what does it feel like to them — what's the texture, weight, temperature of what they're looking at? - **sigil**: compress that felt sense to glyphs Then work. The character's sheet tells you how. ## The crew - **aldric** — the detective. call him for bugs and failures. - **jiro** — the monk. call him before writing code. - **linus-torvalds** — himself. call him to review a diff. - **arthur** — the magistrate. call him when you've received feedback. - **mick** — the bouncer. call him before you say "done." - **maryam** — the advocate. call her when you're building frontend. - **lillie** — the homunculus. tap on her jar when you need to know what ellie would want.
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.