Data & analysis

TinkerClaw ORCA

Try it

Stop editing files one at a time. ORCA drafts every change in parallel, then applies them per-file-serialized so disjoint files fly and shared files never collide. Spawns subagents and writes to your repo — see Permissions, Data Flow & Consent.

What it does

Stop editing files one at a time. ORCA drafts every change in parallel, then applies them per-file-serialized so disjoint files fly and shared files never collide. Spawns subagents and writes to your repo — see Permissions, Data Flow & Consent.

The skill document

ORCA — parallel multi-agent coding

One of dozens of skills and plugins in TinkerClaw — a self-improving OpenClaw fork that's been running 24/7 for months.

Your agent edits twelve files. One. At. A. Time.

You watch it read, think, patch, verify — then start again on the next file as if the other eleven didn't exist. The work is embarrassingly parallel and it is running in single file.

The reason nobody parallelises it is the fear of two workers touching the same file. ORCA removes the fear instead of working around it: the only contended thing in a repo is a shared file, so it puts a short-lived lease on files and on nothing else. Disjoint files run at full concurrency. Shared files queue for a moment. A patch that goes stale while waiting is re-derived rather than clobbering someone.

"Did it merge cleanly?" stops being a question you ask.

Part of TinkerClaw — real-time token tracking, self-improving crons, persistent cognitive memory. This is one piece of that stack; the repo has dozens more.

👉 https://github.com/globalcaos/tinkerclaw

Clone it. Fork it. Break it. Make it yours.

How it works — two phases

Phase A — no lease, fully parallel. Every unit reads, diagnoses and drafts its exact patch simultaneously. That is ~95% of the wall-clock, and none of it contends.

Phase B — brief per-file lease. Acquire the lease, apply the prepared patch, verify that file, release. Disjoint units run concurrently; units sharing a file serialise; a staleness guard makes a worker re-derive a patch that no longer applies.

Phase C — one commit per unit (optional, on by default). Each unit stages only its own files — never git add -A — so a parallel session's unrelated work is never swept in.

Requirements

  • OpenClaw with a configured model provider.
  • A subagent-spawn CLI. Point at it with spawnCliPath in args, or the ORCA_SPAWN_CLI env var.
  • A git repo. ORCA works on the repo you name and nowhere else.

Usage

Workflow({
  scriptPath: "/scripts/parallel-implement.workflow.js",
  args: {
    repoRoot: "/path/to/your/repo",
    units: [
      { id: "u1", task: "what to change", writes: ["src/a.ts"], reads: ["src/x.ts"] },
      { id: "u2", task: "what to change", writes: ["src/b.ts"] }
    ],
    commit: true
  }
})

units[].writes are the lease keys. Keep them disjoint for maximum parallelism; overlapping writes are safe — they simply serialise.

What it does. Runs git commands and applies patches inside the repoRoot you pass, and spawns one subagent per unit through your own provider.

What it does not do. It touches nothing outside repoRoot, reads no credentials, makes no network calls of its own, and never force-pushes or amends. It does not use --no-verify — your hooks run.

Your off switches. commit: false leaves everything uncommitted for you to inspect. Leave ORCA_CONDUCTOR unset and every unit runs on your default model. A file you do not list in writes is a file ORCA will not write.

It costs money. One subagent per unit, on your provider, at your rates. A 12-unit run is 12 agents. Start with two.

Optional extras, off unless you set them. ORCA_CONDUCTOR (per-domain model routing) and ORCA_OWNERSHIP_SCRIPT (a session-ownership pre-flight). Without them ORCA falls back to a plain dirty-worktree check and a single model.

When NOT to use it

  • A single-file change — just edit it.
  • Edits where unit B must read unit A's committed result — split into separate runs.

Included Files

FilePurpose
scripts/parallel-implement.workflow.jsThe orchestrator. No host paths, no hardcoded machine assumptions

Related skills

OrcaRouter (orcarouter.ai). Use this skill for ANY OrcaRouter request — reading, creating, and updating data. Whenever a task involves OrcaRouter, use this skill instead of calling the API directly.

Launch and coordinate agent sessions inside the Orca IDE runtime (stablyai/orca). Topics: send - find a running Orca terminal by title/preview/worktree and deliver a prompt to it, always disambiguating with a question when more than one candidate matches and never targeting the caller's own pane [send.md]. launch - start a new agent session (claude, antigravity, openclaw, codex, and 30+ other supported agents) in a fresh or existing worktree [launch.md]. install - install the stablyai/orca skill bundle into Claude Code, OpenClaw, or Antigravity, asking whether to ghq-clone + symlink or use a plain marketplace install [install.md]. Use when: "send this to the claude session working on X", "hand this off to another agent in Orca", "spawn a new codex/antigravity worktree", "which Orca terminal is running Y", "install the orca skills into openclaw/antigravity", "Orca worktree", "Orca terminal send".

OpenClaw multi-agent daily collaboration orchestrator: split a task into subtasks, dispatch to resident agents via sessions_send, track progress via sessions_list/sessions_history, aggregate results with source attribution, and retry failures (default max 3). Reads openclaw.json for agents.list and agentToAgent.allow (bidirectional whitelist); three-tier trigger (explicit dispatch / suggest+ask for fuzzy big tasks / silent otherwise). Use when user asks to orchestrate/coordinate multiple agents, dispatch parallel work, delegate to a named agent, or aggregate results from several agents (orchestrate/parallel/delegate/让 XX 做/编排/并行/ 分给/汇总). 中文:OpenClaw 多 Agent 日常协作编排器——任务拆解、跨 agent 分发(强制 sessions_send)、进度追踪、结果聚合、失败重试(默认最多 3 次)。 三档触发:用户点名或含编排动词直接执行;模糊大任务建议并行并询问用户; 其余情况保持沉默。直接读 openclaw.json(agents.list + agentToAgent.allow 双向白名单)获取 agent 名单与授权。适用于多 agent 家庭协作、并行调研、批量巡检、发布前多视角审查、团队日报汇总等场景。

Tinker desktop toolbox CLI for AI agents. Use when the user needs to open a Tinker plugin, list installed plugins, control plugin windows from the command line, call plugin MCP tools, or integrate Tinker with an AI agent via MCP. Triggers include "open a Tinker plugin", "list tinker plugins", "tinke

Answers questions about a past agent run from its recording rather than from memory, and replays or forks that run. Use when asked why an earlier run did something, or to reproduce a failure.

2 installs1 stars

Set up an OpenClaw personal AI assistant by generating tailored configuration files through guided Q&A.

24 installs