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Dabbler AI Orchestration

Dabbler AI Orchestration

Darndest Dabbler

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3 installs
| (0) | Free
Project wizard, session-set explorer, cost dashboard, and adoption-bootstrap entry point for the Dabbler AI-led workflow.
Installation
Launch VS Code Quick Open (Ctrl+P), paste the following command, and press enter.
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Dabbler AI Orchestration

An AI-led coding-session workflow for VS Code. Manage structured AI sessions, automatic cross-provider verification, cost tracking, and git-worktree-aware session-set state — all from the activity bar.

Session Set Explorer in action


What you get

  • Sessions, not infinite chats. Bounded slices of work — one session, one orchestrator conversation, one verification, one commit. Sessions live inside ordered session sets that you and the AI co-design before any code is written. The activity-bar tree shows what's in flight, what's queued, and what's done.

  • Cost-minded routing. Every reasoning task (code review, analysis, documentation, end-of-session verification) goes through the AI router, which picks the cheapest capable model per task and escalates only when needed. Real projects we tested measured 73% savings vs Opus-only on a CLI/library project (990 routed calls) and 32% savings on a UI app with UAT/E2E gates (370 calls). Two sample reports ship in the GitHub repo.

  • Cross-provider verification, every session. Each session ends with an independent verification by a model from a different provider than the one that did the work. The verifier returns structured JSON; disagreements surface for human adjudication rather than being silently merged or dismissed.


Get started

After install, the Session Set Explorer shows a Get Started welcome the first time you open a workspace with no docs/session-sets/ folder. Click Copy adoption bootstrap prompt and paste it into a fresh AI chat (Claude Code, Gemini Code Assist, or any GPT-based tool). The AI fetches the canonical setup instructions and walks you through:

  1. A budget dialog — set a not-to-exceed (NTE) dollar cap for verification spend. Verification calls typically cost $0.05–$0.80 each; entering $0 switches to manual cross-provider review at no API cost.
  2. A plan alignment — the AI proposes a session-set decomposition based on what you describe.
  3. A numbered action checklist — every intended write, config, and scaffolding step is listed. You batch-approve before anything touches disk. No per-write confirmation prompts. You can interrupt at any time.

Once your first session set exists, the welcome content disappears and the standard activity-bar tree takes over.

If you'd rather drive the setup from VS Code's UI directly, run Dabbler: Get Started from the command palette (Ctrl+Shift+P / Cmd+Shift+P). The wizard includes a Configure AI Router button that opens the visual config editor once your project is set up.


What it'll cost

API spend is real and varies by project size and verification appetite. Honest framing:

  • $0 budget — verification routes through a different AI assistant you open manually (e.g. open a second AI chat as the verifier), or you skip verification with the decision logged. No API spend.
  • Non-zero budget — the router makes synchronous API calls for cross-provider verification, capped at your not-to-exceed (NTE) threshold. Verification calls typically run $0.05–$0.80 each; a 3-session set usually totals $0.15–$2.50; a 6-session set $0.30–$5.00. These are empirical medians — outliers exist.

The router writes one JSON line per call to ai_router/router-metrics.jsonl so you can audit spend at any time. The Cost Dashboard command surfaces cumulative spend visually; python -m ai_router.report produces a full markdown manager-report with the Opus-baseline savings headline, per-task-type unreliability rates, and auto-generated action items. The framework is open-source (MIT) — your costs are entirely your provider's API spend; nothing in this extension is paywalled.


Requirements

  • VS Code 1.85+
  • Python 3.10+ with a workspace .venv/ (the Dabbler: Install ai-router command auto-detects or creates it for you)
  • API keys as environment variables:
    • ANTHROPIC_API_KEY (Claude Sonnet, Opus)
    • GEMINI_API_KEY (Gemini Flash, Pro)
    • OPENAI_API_KEY (GPT-5.4, GPT-5.4 Mini)
    • All three are required so cross-provider verification has somewhere to route to.
  • One orchestrator AI agent installed as a VS Code extension (Claude Code, Codex/GitHub Copilot, or Gemini Code Assist — the framework is agent-agnostic and supports switching mid-set).

Optional: PUSHOVER_API_KEY + PUSHOVER_USER_KEY for end-of-session phone notifications.

Sign-up links and a full prerequisites checklist live in the GitHub repo's README.


Other features

  • Visual config editor (Dabbler: Open Dabbler Config Editor) — edit router-config.yaml, budget.yaml, and the gitignored local-overrides.yaml through a six-section panel without touching YAML directly. Sections cover routing mode, budget threshold, provider API-key env vars, significance flagging, Pushover notifications, and a local-overrides summary. Includes a live-validation drift banner and a "Send a test notification" button.
  • Significance flagging — Dabbler: Flag Decision for Cross-Provider Review appends a one-line reason to the active set's review queue. Dabbler: Scan Workspace for @dabbler:outsource-review Annotations walks source files for # @dabbler:outsource-review("...") and // @dabbler:outsource-review("...") annotations and queues new findings automatically.
  • Cancel/Restore lifecycle — cancel a session set mid-stream with a recorded reason; restore later if priorities shift. The audit trail accumulates across cycles.
  • UAT checklist editor integration — for sets that opt in with requiresUAT: true, the orchestrator authors a checklist that pairs with the freely-available UAT checklist editor. Pending review blocks downstream sessions unless explicitly overridden.
  • Worktree auto-discovery — parallel session sets running in sibling git worktrees show up in the activity-bar tree even when the worktree isn't open as a separate workspace folder.

Learn more

  • GitHub: darndestdabbler/dabbler-ai-orchestration
  • Workflow mechanics: docs/ai-led-session-workflow.md (trigger phrases, the 10-step procedure, the rule list every orchestrator obeys).
  • Repository reference: docs/repository-reference.md (deep feature descriptions, UAT/E2E flag matrix, worked end-of-session output, file map).
  • Sample reports: docs/sample-reports/ (real python -m ai_router.report outputs from contrasting projects).

License

MIT. Copyright © 2026 darndestdabbler.

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