菜单
Hand off specific named data from one agent to the next, typed (load files, validate expected outputs, map outputs→inputs). Use when step B needs particular named inputs produced by step A, not just the raw text. `dovetail()`.
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。
基于 SOC 职业分类
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/sancovp/cave-teams --skill cave-dovetail命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
正在显示 SKILL.md
Drive the whole cave-teams library from one function, as DATA — build/run any team from a spec in any sequence, save a team to your proven library, reuse a proven team anywhere, and discover teams across projects. Use when you want to serialize a team, run a team from JSON/dict, save+reuse a proven team across repos ("golden"), or have a higher system / global app drive cave-teams. NOT how you normally program (use the cave-teams DSL for that). Triggers: "cave()", "metacontrol", "run from a spec", "team as data", "golden team", "reuse a proven team", "scan for .cave", "global cave".
Build and run programmable agent teams with cave-teams — the provider-agnostic version of Claude Code Teams. Use when the user wants to wire multiple agents together, run agents in sequence or parallel, build a multi-agent pipeline/workflow, make a "team" of coding agents (Claude Code, Codex, MiniMax, or any), loop an agent until a check passes, run a contest between agents, or compose agents with the >> / | DSL. Triggers: "agent team", "multi-agent", "orchestrate agents", "wire these agents", "run agents in parallel", "cave-teams", "agent pipeline", "agent workflow".
Program WHEN each agent may be messaged — conditions on messages, the GUARDRAILS of a leader-driven team run. Use to gate agents on runtime state: after these two respond, only when a flag holds, any predicate over the message log. THIS is what makes cave-teams programmable where Claude Code Teams' flow is fixed.
| name | cave-dovetail |
| description | Hand off specific named data from one agent to the next, typed (load files, validate expected outputs, map outputs→inputs). Use when step B needs particular named inputs produced by step A, not just the raw text. `dovetail()`. |
The typed joint between two steps: a >> dovetail(D) >> b applies a DovetailModel between them — declares the expected outputs of the previous step, loads files into the context, and maps them to the next step's named inputs. A missing expected output surfaces as a BLOCKED/ERROR Link.
from cave_teams import dovetail, DovetailModel
D = DovetailModel(expected_outputs=["spec"], file_inputs={"plan": "PLAN.md"})
piped = a >> dovetail(D) >> b
{"op": "dovetail", "a": <spec>, "b": <spec>,
"model": {"expected_outputs": ["spec"], "file_inputs": {"plan": "PLAN.md"}}}
If the spec is malformed, cave() returns {"status": "construction_error", "hint": "read the cave-dovetail skill"} — the error names this skill.
cave-sequential · cave-dag · cave-teams
Part of the cave-teams plugin — the language + full DSL is the cave-teams skill; drive any pattern from data with the cave skill.