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/hashgraph-online/awesome-codex-plugins --skill add-member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。
Coordinate agents via the AMQ CLI for file-based inter-agent messaging. Use this skill whenever you need to send messages to another agent (codex, claude, or any named handle), check your inbox, drain queued messages, set up co-op mode between agents, join a swarm team, route messages across projects, or diagnose delivery issues. Also use it when you receive a message and need to know how to reply, inspect receipts, or handle priority. Covers any multi-agent coordination task where agents need to talk to each other — review requests, questions, status updates, decision threads, wake notifications, and orchestrator integration (Symphony, Kanban). For collaborative spec/design workflows specifically, prefer the /amq-spec skill which provides structured phase-by-phase guidance. Not intended for distributed systems design (RabbitMQ, Kafka), CI/CD pipelines, or single-agent tasks with no partner.
Parallel-research-then-converge design workflow between two agents. Use this skill when the user wants two agents to independently think through a design problem before aligning on a solution — "spec X with codex", "design X together", "both agents think through X", "brainstorm architecture together", "parallel research then joint proposal", "think through separately then align", "careful thought from both sides before coding", or any variation where the user wants collaborative design rather than just splitting implementation work. Also use this when you receive a message labeled workflow:spec and need to know the correct receiver-side protocol. Not for sending simple messages or reviews (use /amq-cli), implementing completed designs, or creating document templates.
Use when defining new features, product behavior, UI/component design, architecture choices, contract changes, or ambiguous medium/high-complexity work before implementation, or when the user asks to grill or pressure-test a plan or design.
正在显示 SKILL.md
基于 SOC 职业分类
| name | add-member |
| description | Add a new team member. Describe what you need and ATeam infers the config. |
Add a new team member from a natural language description.
Check for .agenteam/config.yaml, .agenteam.team/config.yaml, or legacy
agenteam.yaml. If all are missing:
mkdir -p .agenteamcp <plugin-dir>/templates/agenteam.yaml.template .agenteam/config.yamlpython3 <runtime>/agenteam_rt.py generateExtract role details from the user's natural language request. Examples:
performance_engineer, focus: profiling + optimizationsecurity_auditor, focus: vulnerabilities + authdocs_writer, focus: documentation, write_scope: docs/**devops_engineer, focus: pipelines + deploymentInfer as much as possible from the description:
| Field | How to infer |
|---|---|
name | Snake_case from the role title |
description | From user's description |
responsibilities | 3-5 items inferred from the role's domain |
participates_in | Match to pipeline stages: research, strategy, design, plan, implement, test, review |
can_write | Yes if the role creates/modifies files; no if it only analyzes |
write_scope | Infer from what the role writes (docs, src, tests, configs) |
model | Omit to inherit platform default. If the user requests a pin, inspect codex debug models and choose only from the live catalog. Use evaluation evidence to justify a strong model for demanding analysis or a smaller model for bounded worker tasks. Model is a personal override — share-config strips it. |
reasoning_effort | high for analysis roles, medium for execution/writing roles |
system_instructions | Generate focused instructions from the role's domain |
Before confirming, count the current roles:
python3 <runtime>/agenteam_rt.py roles list
system_instructions instead. Proceed anyway? (yes / cancel)"Also check: if the team will have more than 6 roles, note:
"Codex defaults to 6 concurrent agent threads. To run more agents in
parallel, set agents.max_threads in your Codex config.toml."
Present the inferred role as a summary and ask for confirmation:
Here's your new team member:
Name: performance_engineer
Focus: Profiling, bottleneck analysis, optimization
Stages: review, implement
Writes to: src/** (optimization patches)
Model: inherited default (or `gpt-5.4` if you want to pin it)
System instructions:
You are the performance engineer on an AgenTeam. Your primary job is
to identify bottlenecks and optimize critical paths...
Add to team? (yes / adjust)
If the user says "adjust" or requests changes, update the fields and re-confirm. Do not ask field-by-field -- keep it conversational.
Read the current .agenteam/config.yaml (or legacy agenteam.yaml) and add the new role under roles:.
Write the full role block including:
descriptionresponsibilitiesparticipates_incan_write and write_scope (if applicable)model and reasoning_effortparallel_safe (true for read-only roles, false for writers unless scoped)system_instructionsIf the role participates in a pipeline stage, also add it to the
appropriate pipeline.stages[].roles list.
python3 <runtime>/agenteam_rt.py generate
Show the user:
.codex/agents/<name>.toml@ateam ask <name> to <task>$ateam:assign <name> "<task>".agenteam/config.yaml anytime to adjust this role."