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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.

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mims-harvard/AutoScientists
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28 de maio de 2026 às 05:00
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SKILL.md
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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`.
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