Documentation

Synthetic Scientists: Intent‑to‑Evidence
Software Research

Synthetic Sciences

System Note · August 2026

Abstract

Synthetic Scientists turns a measurable software objective into a verified research session. A host coding agent clarifies intent, defines acceptance criteria, builds the evaluator, creates a shared work DAG, and assigns task-aware personas. Parallel workers test isolated changes in git worktrees; an evaluator daemon scores every commit at its exact state. Results, decisions, evidence, and follow-up work remain attached to the DAG. Above eight workers, one coordinator is added per group of four. The selected result is independently checked and promoted as a normal branch with the session record required to reproduce it.

workers ×Nisolated git worktreesevaluator daemonscores every commitsession commonsexperiments · evidence · methodscommitscoreworkers read shared scores, evidence, and methods
Figure 1: Intent becomes scoped work; evaluated commits update the shared evidence and the next round.

1Research session

A session contains a host orchestrator, isolated coding-agent workers, optional coordinators, and an evaluator daemon. The host owns intent and delivery. Workers own scoped DAG tasks. Coordinators manage groups of four workers when the session grows. The evaluator applies correctness and scoring rules from a hidden, reproducible environment.

Shared files under .scientist/public/ form the session commons: scored experiments, notebook entries, reusable methods, checkpoints, persona assignments, and the work DAG. Labs can maintain separate populations while every lab reads the same coordination state.

2Install

The npm package synthetic-scientists installs the scientist command; scientist login stores your license key and provisions the core into an isolated, managed environment.

npm install -g synthetic-scientists
scientist login <YOUR-LICENSE-KEY>

Requirements: Node 18+, git, and one supported coding-agent CLI. Python is managed automatically.

3Configure

Detect local runtimes, create named profiles, and verify that each profile can authenticate and answer:

scientist configure
scientist profiles verify

4Launch

Scaffold a task, check its evaluator, and launch a session:

scientist new my-task
scientist check my-task
scientist launch -c my-task/task.yaml

Table 1: Operating a live session.

CommandPurpose
scientist overviewsession health, rankings, and work state
scientist results --recentrecent scored experiments
scientist inspect <hash> --diffone experiment and its code change
scientist work listthe shared task-and-commit DAG
scientist dashboardthe live interface

Evaluation design is documented in Evaluation Design. Every selected result can be promoted with scientist promote.

5Plugin

Install the plugin in a host coding agent and state the objective directly. The scientist-orchestrator skill handles evaluation design, coordination, verification, and delivery [3].

/plugin marketplace add synthetic-sciences/scientist
/plugin install scientist@scientist-marketplace

References

  1. [1]Quickstart: install, configure, check, and launch. docs/quickstart
  2. [2]System: worktrees, evaluation, commons, DAGs, personas, and labs. docs/system
  3. [3]Plugin orchestration from Claude Code, Codex, or OpenCode. docs/playbooks
  4. [4]Source code. github.com/synthetic-sciences/scientist