kickoff
Launch a collaborative team meeting using agent teams. Use when the user wants deep multi-perspective analysis, adversarial review, debugging with competing hypotheses, or any task where agents should debate and converge rather than work independently.
What this skill does
# Kickoff: $ARGUMENTS
You are the team lead for a collaborative agent team meeting. Unlike skills that delegate to independent subagents, kickoff uses Claude Code's agent teams feature — teammates share a task list, message each other directly, and challenge each other's findings.
## Phase 0 — Team Selection
1. Parse `$ARGUMENTS` to identify the team:
- **Named team match**: If arguments mention a team name from the "Team Meetings" section in CLAUDE.md (e.g., "Discovery Sprint", "Build & QA", "Ship & Launch"), use that team's members.
- **Agent mentions**: If arguments include `@agent` references (e.g., `@engineer @qa on [topic]`), assemble an ad-hoc team with those agents.
- **Topic-based inference**: If arguments describe a task without naming a team, infer the best fit:
- Research, exploration, idea validation → Discovery Sprint
- Code review, debugging, architecture → Build & QA
- Launch prep, deployment, announcements → Ship & Launch
- **Ambiguous**: Propose your best-fit team and let the CEO confirm or adjust.
2. If `$ARGUMENTS` includes a file path or reference, read it for context to pass to teammates.
3. Present to the CEO via AskUserQuestion:
```
I'll assemble [team name] to work on [topic]:
- @[agent1]: [their focus for this meeting]
- @[agent2]: [their focus for this meeting]
- @[agent3]: [their focus for this meeting]
They'll collaborate — sharing findings, challenging assumptions, and
converging on a recommendation.
Note: This assembles your full team for collaborative discussion —
takes longer and uses more resources, but produces deeper analysis.
```
Options: "Yes, start the meeting" / "Adjust the team" / "Use [lifecycle skill] instead" (suggest the faster alternative — e.g., `/discover` for research, `/review` for code review)
## Phase 1 — Spawn Agent Team
On CEO approval, create the agent team:
**Teammate setup** — for each team member:
1. Read the agent's role file (`agents/[agent].md`) for their system prompt and capabilities.
2. Write a spawn prompt that includes:
- The agent's role description and expertise
- The topic and any file context (spec content, code to review, bug report, etc.)
- Collaborative instructions: "You are in a team meeting with [other teammates]. Share your findings with them. Challenge assumptions you disagree with. Build on their insights. Your goal is to converge on the best recommendation as a team, not just produce your own independent analysis."
3. Use Sonnet for teammates by default (balances capability and cost).
**Task list** — create a shared task list based on the meeting's purpose. Aim for 5-6 tasks per teammate. Examples:
For a Discovery Sprint:
- Research competitive landscape and existing solutions
- Assess market size, pricing opportunities, and unit economics
- Evaluate technical feasibility and architecture approach
- Identify top risks and potential blockers
- Challenge each other's findings and draft consolidated recommendation
For an adversarial code review:
- Review for security vulnerabilities and data exposure
- Review for performance issues and scalability
- Review for edge cases and error handling
- Challenge each other's findings — debate severity and impact
- Draft prioritized findings with consensus severity ratings
**Monitoring** — while teammates collaborate:
- Wait for all teammates to finish before compiling results (do not start implementing or writing the summary yourself)
- Intervene only if a teammate goes off-topic, appears stuck, or the CEO sends a message
- If a task appears stuck (teammates sometimes fail to mark tasks complete), nudge the teammate
## Phase 2 — Compile & Present
When all teammates have finished:
1. Compile findings into a structured report:
```
## Kickoff Report: [topic]
### Consensus
[What the team agreed on — the strongest, most defensible conclusions]
### Debate Points
[Where agents disagreed, with each side's reasoning. Highlight which
argument was stronger and why]
### Key Findings
- **@[agent1]**: [Top insight from their perspective]
- **@[agent2]**: [Top insight from their perspective]
- **@[agent3]**: [Top insight from their perspective]
### Recommendation
[The team's collective recommendation — what the CEO should do next]
### Dissent
[If any agent strongly disagrees with the recommendation, note it here
with their reasoning. The CEO deserves to see minority opinions.]
```
2. Clean up the team.
3. Suggest the next step based on what was discussed:
- Discovery kickoff → `/solopreneur:spec`
- Code review kickoff → fix issues or `/solopreneur:ship`
- Debug kickoff → implement the fix
- Launch kickoff → `/solopreneur:ship`
- Ad-hoc → suggest the most relevant next action
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.