翻訳待ち：Show HN: I make my AI agents file paperwork before they're allowed to code

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。

Notifications You must be signed in to change notification settings Fork 0 Star 4 BranchesTags Open more actions menu Latest commit History 1,233 Commits 1,233 Commits Folders and files NameName Last commit message Last commit date .agents/plugins .agents/plugins .claude .claude .github/workflows .github/workflows .zed .zed cmd cmd docs docs internal internal pack pack plugins/conveyor plugins/conveyor scripts scripts test-results test-results web web .env.example .env.example .gitignore .gitignore AGENTS.md AGENTS.md CLAUDE.md CLAUDE.md LICENSE LICENSE Makefile Makefile README.md README.md compose.dev.yaml compose.dev.yaml compose.yaml compose.yaml conveyor.example.yaml conveyor.example.yaml go.mod go.mod go.sum go.sum install.sh install.sh sqlc.yaml sqlc.yaml Repository files navigation Generating code is easier than ever. Checking that the code matches product intent is now the bottleneck, and it is a bottleneck that gets worse as agents get faster: a queue of unsupervised agents can ship more unread code per day than a team can read. A software factory is the answer to this shape of problem. You do not inspect every screw; you fix the process so that inspection happens at the points where mistakes can happen, and you make every unit traceable so that when something is wrong you know what else is affected. Conveyor is a software factory for agent-written code. It queues work from Requirements, System Design documents, and Decisions. Human operators confirm the documents, and approve plans when required. Agents on your machines plan, implement, and review that work. Conveyor has used this process to build itself since July 2026. Task board Confirmed System Design Requirement drift Task plan and lineage Review feedback returning to implementation MCP client setup The knowledge graph Conveyor links each change to the documents, task, review, and test evidence behind it. flowchart LR intent["Confirmed requirements and designs"] --> task["Task"] task --> delivery["Delivered change"] intent -.-> check{"Misalignment checks"} delivery -.-> check repository["Observed repository"] -.-> check check -->|mismatch found| signal["Signal"] signal --> followup["Judgment or gated follow-up"] followup -->|re-enters the factory| task Loading Conveyor checks each delivery against confirmed requirements and governing designs. When a delivery and its confirmed intent disagree, Conveyor raises a signal. Repository drift and post-merge failures raise signals too. Conveyor never rewrites code or documents on its own. An operator can acknowledge the signal or send follow-up work through the normal gates. Architecture Operators in a browser Agents such as Codex or Claude | | React dashboard MCP work-order server board, tasks, docs claim, plan, review | | +------------------+-------------------+ | conveyord in Go REST API, dashboard, event log | PostgreSQL events, documents, links, queue | conveyor worker on your machine supervises your agent CLIs | Git worktrees, repositories, PRs conveyord is one Go binary. PostgreSQL stores the event log, documents, lineage projection, and River queue. The worker launches agent CLIs with your local credentials. Installation A factory host needs PostgreSQL 15 or newer, Git, an authenticated gh CLI, an API key for an OpenAI-compatible model endpoint, and the agent CLIs you plan to run. Install the latest conveyor and conveyord binaries into ~/.local/bin: curl -fsSL https://raw.githubusercontent.com/kidus-tiliksew/conveyor/main/install.sh | sh The installer verifies the release checksum before replacing either binary and does not need sudo. Pinning a reviewed version, building from source, and upgrades are covered in Installation. From there, Getting started (solo) stands up a factory end to end on one machine, and Getting started (multiplayer) covers a shared team server. Documentation Full docs live in docs/. There's no docs site yet. Getting started Installation: release installer, source builds, prerequisites Getting started (solo): one person, one machine, end to end Getting started (multiplayer): a shared team server Guides CLI reference: every conveyor and conveyord command Authentication: sign-in, tokens, roles, GitHub identity Configuration: the three config surfaces and every environment variable MCP reference: the tools an agent uses to work a task The factory Concepts: the software factory, the knowledge graph, light and dark factory patterns The document corpus: requirements, System Designs, decisions, and the propose-confirm cycle Tasks: how work is created, given context, executed, reviewed, and linked Misalignment: drift, staleness, and pending proposals Operations Worker operations: durable worker enrollment, service install, recovery GitHub lifecycle: how issues, PRs, and review statuses are projected onto GitHub Known limitations: accepted boundaries of the current implementation Playbooks (installable as agent skills with conveyor skills install) Planning: draft and push documents from a local agent session Task filing: file well-formed tasks and dependency chains Working a task: the claim, checkout, submit, review lifecycle Status Conveyor is under active development. Its event log records defects and reconciliation work alongside successful merges. License Conveyor is available under the MIT License. MIT license Activity Stars 4 stars Watchers 0 watching Forks 0 forks Report repository