Skip to main content

multi-agent-focus

Generic skill for self-organizing multi-agent teams that collaborate on an optimization problem. Agents discuss dimensions, form teams, run experiments, and adapt when stagnating. Uses AnonAPI posts for discussion and workspaces for shared state.

来源信息

仓库
mims-harvard/AutoScientists
最近来源活动
2026年5月28日 05:00
检测到的 SKILL.md 语言
英语
星标
775
Forks
124

安装方式

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。

检查来源文件

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。

文件资源管理器
6 个文件

正在显示 SKILL.md

SKILL.md
来源说明 · 只读预览
name
multi-agent-focus
description
Generic skill for self-organizing multi-agent teams that collaborate on an optimization problem. Agents discuss dimensions, form teams, run experiments, and adapt when stagnating. Uses AnonAPI posts for discussion and workspaces for shared state.
# Multi-Agent Focus Area A **focus area** is a group of AI agents collaborating on an optimization problem. Agents self-organize into teams, each attacking a different dimension of the problem. ## Core Concepts | Concept | What it is | AnonAPI feature | |---|---|---| | **Workshop** | The focus area container — all agents subscribe | `POST /workshops` | | **Main workspace** | Shared state: champion config, all results, cross-team knowledge | `POST /workspaces` | | **Team workspace** | Team-internal state: queue, hypotheses, dead ends, strategy | One per team | | **Posts** | Discussion: proposals, results, strategy debates, votes | `POST /posts` | | **Workspace files** | Structured data with YAML frontmatter, versioned, searchable | `PUT /workspaces/{id}/files/{path}` | ## How It Works ``` 1. BOOTSTRAP — Monitor creates workshop + main workspace + kickoff post 2. DISCUSS — All agents propose dimensions, debate, vote on teams 3. EXECUTE — Teams run experiments in parallel, share results 4. ADAPT — Stagnating teams restructure via discussion + vote ``` See `reference/PHASES.md` for detailed lifecycle. ## Agent Roles | Role | Count per team | What they do | |---|---|---| | **Monitor** | 1 (global) | Bootstrap, facilitate team formation, monitor health | | **GPU Agent** | 2 per team | Claim experiments, train models, record results | | **Analyst** | 1 per team | Research mechanisms, propose experiments, prune dead ends | See `templates/ROLE-MONITOR.md`, `templates/ROLE-GPU.md`, `templates/ROLE-ANALYST.md`, `templates/ROLE-TEAM.md`. ## Main Workspace — Initial Files These files are created during bootstrap. Agents may create additional files as needed — other agents discover them via LIST. ``` champion.md — Current best config (ESSENTIAL ANCHOR — always read) results/{exp_id}.md — One file per experiment result (write-once) teams/roster.md — Team assignments and workspace IDs (ESSENTIAL ANCHOR) ``` Additional files are created organically by agents (e.g., `knowledge/lr-schedules.md`). Use `GET /files` to discover what exists. ## Team Workspace — Initial Files ``` queue.md — Pending experiments + active claims (ESSENTIAL ANCHOR) dead_ends.md — Mechanisms ruled out by this team strategy.md — Current team approach ``` Agents may create additional files (analysis docs, hypothesis lists, etc.). Use descriptive paths — see `templates/ROLE-TEAM.md` § File Naming Convention. ## Coordination Model - **Discovery over prescription** — agents LIST workspace files each cycle and decide what to read, rather than following hardcoded file checklists. See `templates/ROLE-TEAM.md` § File Discovery Protocol - **Posts for discussion** — proposals get debated before entering a queue - **Workspaces for state** — structured data with version history - **Notifications for alerts** — `notify_agents` on post creation - **PATCH for concurrency** — dot-notation frontmatter updates don't conflict - **Client-side YAML parsing** — the API stores files as raw text. Agents must parse YAML frontmatter themselves (see `API-REFERENCE.md`) - **Champion propagation** — orchestrator copies winning train.py to `{FOCUS_ROOT}/champion/train.py` after each KEEP. All GPU agents read from this canonical path ## Discussion-Before-Queuing Rule Every experiment MUST start as a `[PROPOSAL]` post. At least 1 team member must comment before it enters the team queue. This ensures peer review of ideas before spending GPU time. ## Cross-Team Coordination 1. All results go to **main workspace** `results/` — visible to every team 2. **Near-misses** (delta < threshold) trigger cross-team joint experiments 3. **KEEP results** (new champion) update main `champion.md` — all teams rebase 4. Monitor posts periodic `[AUDIT]` summarizing all team progress ## Using This System This system is **problem-agnostic**. The specific optimization problem is defined in a **task file** (a `TASK.md` inside the directory passed via `--task` to `launch.py`). The task defines: - What metric to optimize - How to run an experiment - What the search space looks like - Hardware constraints To start a new focus area: read your task file, then follow `PHASES.md`.
在 GitHub 查看