Google has launched Conductor, an open supply preview extension for Gemini CLI that turns AI code era right into a structured, context pushed workflow. Conductor shops product data, technical choices, and work plans as versioned Markdown contained in the repository, then drives Gemini brokers from these recordsdata as a substitute of advert hoc chat prompts.
From chat primarily based coding to context pushed growth
Most AI coding immediately is session primarily based. You paste code right into a chat, describe the duty, and the context disappears when the session ends. Conductor treats that as a core downside.
As a substitute of ephemeral prompts, Conductor maintains a persistent context listing contained in the repo. It captures product targets, constraints, tech stack, workflow guidelines, and magnificence guides as Markdown. Gemini then reads these recordsdata on each run. This makes AI habits repeatable throughout machines, shells, and group members.
Conductor additionally enforces a easy lifecycle:
Context → Spec and Plan → Implement
The extension doesn’t leap straight from a pure language request to code edits. It first creates a observe, writes a spec, generates a plan, and solely then executes.
Putting in Conductor into Gemini CLI
Conductor runs as a Gemini CLI extension. Set up is one command:
gemini extensions set up https://github.com/gemini-cli-extensions/conductor --auto-updateThe --auto-update flag is non-compulsory and retains the extension synchronized with the newest launch. After set up, Conductor instructions can be found inside Gemini CLI if you end up in a mission listing.
Venture setup with /conductor:setup
The workflow begins with mission stage setup:
This command runs an interactive session that builds the bottom context. Conductor asks concerning the product, customers, necessities, tech stack, and growth practices. From these solutions it generates a conductor/ listing with a number of recordsdata, for instance:
conductor/product.mdconductor/product-guidelines.mdconductor/tech-stack.mdconductor/workflow.mdconductor/code_styleguides/conductor/tracks.md
These artifacts outline how the AI ought to purpose concerning the mission. They describe the goal customers, excessive stage options, accepted applied sciences, testing expectations, and coding conventions. They dwell in Git with the remainder of the supply code, so modifications to context are reviewable and auditable.
Tracks: spec and plan as top notch artifacts
Conductor introduces tracks to signify items of labor resembling options or bug fixes. You create a observe with:
or with a brief description:
/conductor:newTrack "Add darkish mode toggle to settings web page"For every new observe, Conductor creates a listing below conductor/tracks/ containing:
spec.mdplan.mdmetadata.json
spec.md holds the detailed necessities and constraints for the observe. plan.md comprises a stepwise execution plan damaged into phases, duties, and subtasks. metadata.json shops identifiers and standing info.
Conductor helps draft spec and plan utilizing the prevailing context recordsdata. The developer then edits and approves them. The necessary level is that every one implementation should observe a plan that’s specific and model managed.
Implementation with /conductor:implement
As soon as the plan is prepared, you hand management to the agent:
Conductor reads plan.md, selects the following pending activity, and runs the configured workflow. Typical cycles embody:
- Examine related recordsdata and context.
- Suggest code modifications.
- Run assessments or checks in keeping with
conductor/workflow.md. - Replace activity standing in
plan.mdand internationaltracks.md.
The extension additionally inserts checkpoints at section boundaries. At these factors Conductor pauses for human verification earlier than persevering with. This retains the agent from making use of massive, unreviewed refactors.
A number of operational instructions assist this move:
/conductor:standingexhibits observe and activity progress./conductor:overviewhelps validate accomplished work in opposition to product and magnificence tips./conductor:revertmakes use of Git to roll again a observe, section, or activity.
Reverts are outlined by way of tracks, not uncooked commit hashes, which is less complicated to purpose about in a multi change workflow.
Brownfield initiatives and group workflows
Conductor is designed to work on brownfield codebases, not solely recent initiatives. If you run /conductor:setup in an current repository, the context session turns into a approach to extract implicit data from the group into specific Markdown. Over time, as extra tracks run, the context listing turns into a compact illustration of the system’s structure and constraints.
Group stage habits is encoded in workflow.md, tech-stack.md, and magnificence information recordsdata. Any engineer or AI agent that makes use of Conductor in that repo inherits the identical guidelines. That is helpful for implementing take a look at methods, linting expectations, or authorized frameworks throughout contributors.
As a result of context and plans are in Git, they are often code reviewed, mentioned, and altered with the identical course of as supply recordsdata.
Key Takeaways
- Conductor is a Gemini CLI extension for context-driven growth: It’s an open supply, Apache 2.0 licensed extension that runs inside Gemini CLI and drives AI brokers from repository-local Markdown context as a substitute of advert hoc prompts.
- Venture context is saved as versioned Markdown below
conductor/: Recordsdata likeproduct.md,tech-stack.md,workflow.md, and code model guides outline product targets, tech selections, and workflow guidelines that the agent reads on every run. - Work is organized into tracks with
spec.mdandplan.md:/conductor:newTrackcreates a observe listing containingspec.md,plan.md, andmetadata.json, making necessities and execution plans specific, reviewable, and tied to Git. - Implementation is managed by way of
/conductor:implementand track-aware ops: The agent executes duties in keeping withplan.md, updates progress intracks.md, and helps/conductor:standing,/conductor:overview, and/conductor:revertfor progress inspection and Git-backed rollback.
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Michal Sutter is a knowledge science skilled with a Grasp of Science in Information Science from the College of Padova. With a strong basis in statistical evaluation, machine studying, and information engineering, Michal excels at reworking advanced datasets into actionable insights.

