team-coordination
Coordinate agent teams for adversarial review (paired skeptic/verifier per facet, challenge round with disposition vocabulary, consolidated findings with confidence) or parallel implementation (task sizing 5-6 per teammate, non-overlapping files). Enforces independent analysis before shared conclusions. Reference only (`disable-model-invocation: true`); loaded only when `agentTeams: true` in settings.
What this skill does
# Team Coordination
Domain skill for orchestrating agent teams. Only relevant when `agentTeams: true` in settings.
Requires: `CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS` environment variable.
## Iron Law
**INDEPENDENT ANALYSIS BEFORE SHARED CONCLUSIONS. Teammates who see each other's findings are contaminated reviewers.**
The value of a team is independent perspectives. Sharing findings before independent analysis destroys that value.
## When to Spawn Teams
| Scenario | Recommendation | Rationale |
|----------|---------------|-----------|
| Review with 3+ facets | Spawn team | Independent analysis enables adversarial protocol |
| Large feature (>5 acceptance criteria, independent modules) | Suggest team | Parallel implementation across modules |
| Debugging with competing hypotheses | Spawn team | Each teammate tests a different hypothesis |
| Small feature (<3 criteria) | Single session | Team overhead exceeds benefit |
| Sequential dependencies | Single session | Can't parallelize sequential work |
## Task Sizing
- **5-6 tasks per teammate** — enough work to stay busy, not so much they lose focus
- Each task should be completable without coordination
- Tasks must have clear acceptance criteria
- Overlapping file sets → same teammate (avoid merge conflicts)
## Adversarial Review Protocol
When spawning a review team, each facet runs as a **paired reviewer** (skeptic + verifier) with a **disposition-only challenge round** between them. The protocol is the contract frozen in `.decisions/issue-86.md` and emitted by `commands/review.md` Path A.
### Cost expectation
Per `/flow:review` run with default 6-facet fan-out:
| Phase | LLM calls |
|-------|-----------|
| Phase 1 — Independent Analysis (5 agent facets × 2 + 2 holdout-validation Skill calls) | 12 |
| Phase 2 — Share findings (lead-only orchestration; no LLM call) | 0 |
| Phase 3 — Challenge (each Agent reviewer challenges the other's findings, 5 × 2; holdout-validation excluded — see review.md A.1 note) | 10 |
| Phase 4 — Synthesize (main agent, 1 consolidation pass) | 1 |
| Phase 5 — Emit consolidated output (lead-only; no LLM call) | 0 |
| **Total** | **≈23 calls** (≈3.8× single-session baseline of 6) |
Wall-clock: ≈1.5–2× single-session via parallel dispatch within each phase. The cost is opt-in (`agentTeams: false` by default) and gated behind `CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS`.
### Phase 1: Independent Analysis (paired reviewers per facet)
For each facet, dispatch **two** subagents with orthogonal prompt lenses. Both run in parallel and never see each other's findings during this phase.
| Variant | System-prompt lens |
|---------|---------------------|
| **skeptic** | "Assume the diff is broken until proven otherwise. Flag every behavior you cannot prove correct from the code as written." |
| **verifier** | "Assume the diff is correct as a baseline. Look only for missed edge cases, undocumented contract assumptions, or invariants that aren't enforced." |
Default 6-facet topology (12 invocations in one parallel dispatch — 5 Agent pairs and 2 Skill calls):
```
Agent(security-reviewer-skeptic) | Agent(security-reviewer-verifier)
Agent(code-reviewer-skeptic) | Agent(code-reviewer-verifier)
Agent(convention-checker-skeptic) | Agent(convention-checker-verifier)
Agent(test-runner-skeptic) | Agent(test-runner-verifier)
Agent(error-handler-inspector-skeptic) | Agent(error-handler-inspector-verifier)
Skill(holdout-validation) [skeptic lens] | Skill(holdout-validation) [verifier lens]
```
The holdout-validation pair is dispatched as Skills (not Agents) because the project does not define a `holdout-validation` agent — the skill IS the contract. The holdout-validation pair contributes findings to A.2 auto-consensus matching but is **excluded from the Phase 3 challenge round** by design, not because Skills lack a challenger prompt pattern.
The principled rationale: adversarial challenge (AGREE/DISAGREE/REFINE) exists for findings where reviewers can hold legitimately different subjective opinions about priority, severity, or category. Holdout findings are categorically different — they are objective claim-verification (file state vs self-reported claim). The file state is the arbiter, so DISAGREE is not a meaningful disposition. Including holdout in challenge would produce vacuous AGREE responses (re-check confirms what we already established) or confuse the protocol (DISAGREE based on what?). See `commands/review.md` A.1 for the full rationale.
Holdout findings carry their own confidence model: `consensus` when both lenses raised the same finding independently, `unchallenged` when only one lens raised it (signal: the lenses parsed the same claim differently or weighted scenario priority differently). They NEVER carry `validated` / `refined` / `kept` — those dispositions are challenge-round outputs.
Each returns P1/P2/P3 findings with `file:line` citations and a category. **No challenge information is included in this phase** — outputs are independent.
### Phase 2: Share Findings
Lead collects all 12 finding sets (10 Agent + 2 Skill). No LLM call. Indexes findings by facet for the per-facet challenge round. Holdout-validation findings are indexed for A.2 auto-consensus matching only and bypass Phase 3.
### Phase 3: Challenge (disposition-only, no diff re-read)
For each facet, dispatch each variant to challenge the OTHER variant's findings. **The challenger does NOT re-read the diff.** The challenger labels each of the other's findings with one of three dispositions:
| Disposition | Meaning |
|-------------|---------|
| `AGREE` | Challenger also flagged this OR confirms it as a real issue |
| `DISAGREE` | Challenger believes this is not a real issue (must give a one-line reason) |
| `REFINE` | Real issue, but priority/category differs (challenger states the corrected priority/category) |
Challenge prompt (issued per facet, both directions in parallel):
```
You are reviewer-{A|B} for facet {facet}. Reviewer-{B|A} raised the following
findings on the same diff you reviewed independently. For each finding, respond
with exactly one line:
{finding-id} AGREE
{finding-id} DISAGREE: {one-line reason}
{finding-id} REFINE: priority={P1|P2|P3} category={text}
Do NOT re-read the diff. Decide based on your prior independent analysis only.
Findings to challenge:
{list of the OTHER reviewer's findings: ID, file:line, priority, category}
```
10 challenge prompts run in parallel (5 agent facets × 2 directions; the holdout-validation Skill pair is excluded by design — see A.1 note for the principled rationale: holdout is objective claim-verification, not subjective judgment, so AGREE/DISAGREE/REFINE doesn't apply).
### Phase 4: Synthesize (consolidation rules)
Lead applies the consolidation table to each finding:
| Origin | Other reviewer's disposition | Consolidated confidence | Disposition vocab |
|--------|------------------------------|-------------------------|-------------------|
| Both raised independently (file ±2 lines, priority ±1) | n/a | **HIGH** | `consensus` |
| One raised, other AGREE | AGREE | **HIGH** | `validated` |
| One raised, other REFINE | REFINE | **MEDIUM** | `refined` (priority/category from REFINE) |
| One raised, other DISAGREE | DISAGREE | **LOW** | `kept` |
| One raised, other timed out / errored | none | **MEDIUM** | `unchallenged` |
| Both raised, both DISAGREE'd in challenge | n/a | **DROPPED** | excluded; logged in journal |
**Independence-match window** (hard-coded for v1): same facet AND same file AND lines within ±2 AND priority within ±1 (P1↔P2 counts; P1↔P3 does not).
The disposition vocabulary `consensus|validated|refined|kept|unchallenged` is the controlled set emitted into the `FLOW_REVIEW_CYCLE` marker — see `references/finding-ledger-parser.md` for the marker schema.
### Phase 5: Emit consolidated output
Lead writes the per-priority finding tables, folding confidence and disposition into the Finding cell as a trailing `_(confidence ·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.