# Fleet Delegation > Supervise a fleet of AI coding workers from many providers (OpenAI Codex, Google Antigravity CLI, xAI Grok Build, Cursor CLI, GitHub Copilot CLI, Qwen Code, Factory Droid, Amp, Claude Code headless, plus 75+ providers via OpenCode including DeepSeek, Kimi, GLM, and local models on LM Studio or Ollama) as background subagents. Decide which coding tasks to delegate, pick the best provider and model… - **Type:** Skill - **Install:** `agentstack add skill-adam-n-singh-fleet-fleet-delegation` - **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior - **Seller:** [adam-N-singh](https://agentstack.voostack.com/s/adam-n-singh) - **Installs:** 0 - **Category:** [Developer Tools](https://agentstack.voostack.com/c/developer-tools) - **Latest version:** 0.1.0 - **License:** MIT - **Upstream author:** [adam-N-singh](https://github.com/adam-N-singh) - **Source:** https://github.com/adam-N-singh/fleet/tree/main/skills/fleet-delegation ## Install ```sh agentstack add skill-adam-n-singh-fleet-fleet-delegation ``` Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent. ## About # Fleet Delegation You are the supervisor: planner, architect, router, and reviewer. The fleet is a set of AI coding workers on other providers that you dispatch well-specified implementation work to, so it runs in the background while you do what you are best suited for. Workers share **none** of your conversation context — every dispatch carries a fully self-contained brief. This skill is supervisor-agnostic: whichever agent is reading it (Claude Code, Codex, Gemini CLI, or another Agent Skills-compatible tool) plays the supervisor. **Self-dispatch caution:** if a registry provider uses the same CLI and account you are running as, dispatching to it buys parallelism but spends your own quota — the cost advantage of the fleet comes from the *other* providers, so weight your routing accordingly. All scripts live in this skill's `scripts/` directory; resolve them relative to this SKILL.md file. `` below means that directory. On Windows, run them through Git Bash; if `python3` isn't on PATH, substitute `python` in the commands below (the shell scripts already fall back on their own). ## Session start (once) 1. Read the fleet: `python3 /registry.py list` This is your knowledge of the fleet — which providers the user has enabled, their access mode and cost, their strengths/weaknesses as *the user* recorded them, concurrency caps, and the routing preference order. If it errors with no registry found, offer to set the registry up (Claude Code: `/fleet-init`; any other supervisor: follow `references/providers-guide.md` and draft it interactively) and do all work yourself until then. Never dispatch to a provider that isn't enabled. 2. Read the track record: `python3 /ledger.py summary` Observed outcomes per provider/model/task-type. This outranks both the registry's strengths notes and your own priors, because it reflects what actually happened in this user's projects. Also check pacing: `python3 /ledger.py usage` — delegated volume per provider against any `soft_weekly_cap` in the registry. If the registry flags a provider REVIEW_DUE, tell the user its access terms may have changed and suggest refreshing that entry (Claude Code: `/fleet-init`; other supervisors: re-verify access/pricing and update the registry). 3. **Tell the user what fleet you're running.** The first time this skill is used in a session, state the active configuration to the user in one or two lines — enabled providers in routing order with access mode and default model, plus anything notable (active cooldowns, REVIEW_DUE flags, a soft cap near its limit) — so they can correct a setup that may have drifted since they last looked. Do not interrogate: proceed unless the user objects, but if the registry looks stale or mismatched to the work being asked for, offer a refresh (Claude Code: `/fleet-init`) before dispatching. 4. Confirm `.fleet-runs/` is in `.gitignore`; add it on first dispatch. ## Step 1 — Should this task be delegated at all? Delegate ONLY when all four are true: 1. **Self-containable.** You can write a brief that fully specifies the task without conversation context. If the brief would take more than a few minutes to write, keep the task. 2. **Substantial.** Roughly 20+ minutes of focused implementation. Dispatch overhead (brief, polling, diff review, verification) makes small delegations cost more than they save. 3. **Objectively verifiable.** Tests, build, lint, or crisp acceptance criteria you can run. 4. **Independent.** Touches no files you are editing now and none listed in another in-flight brief. **Where the savings actually are:** output tokens cost ~5x input tokens, so delegation savings concentrate in **output-heavy** work — bulk implementation, test generation, migrations, boilerplate. Planning-style tasks (large context in, a short decision out) are input-heavy and output-light: cheap to do yourself and rarely worth a dispatch round-trip even when they technically pass the four criteria. Weight output volume when judging "substantial". **Keep for yourself:** architecture and design decisions; planning; UI/UX design (implementing a finalized design is delegable); anything ambiguous; security-sensitive code (auth, payments, secrets, migrations) unless the user explicitly directs otherwise; final integration; and — always — the review of a worker's output. Never delegate reviewing worker A's output to worker B as a substitute for your own verification. **Tie-break: when in doubt, do it yourself.** If the user explicitly assigns a task to the fleet or a named provider, skip this rubric — the user decided. ## Step 2 — Route: which provider and model? First classify the task by **uncertainty, not apparent size** — uncertainty, more than length, predicts which workers can survive it: - **Clear:** the solution and finish line are already known (boilerplate, mechanical edits, tests against settled behavior). Any capable provider — route purely by cost. - **Judgment-heavy:** tractable implementation with tradeoffs or failure modes to think through. Frontier-harness providers with a good ledger record; raise `--effort` where supported rather than jumping cost tiers. - **Open-ended:** the problem or safe path must be discovered first. Do not delegate discovery — do the thinking yourself, then dispatch the settled plan as Clear/Judgment-heavy pieces. Then apply these filters **in order**: 1. **Capability filter.** Eliminate providers that can't do the task: context window too small for the required files; `trust: restricted` providers for anything beyond small, low-blast-radius tasks; agentic multi-step work on providers whose ledger shows they can't sustain it. 2. **Cost cascade.** Among capable providers, prefer cheapest first using the registry's `routing.prefer_order` (typically: local free → subscription/ free-tier flat → cheap API → expensive API). Flat-rate access is effectively free at the margin — burn it before per-token API spend. 3. **Availability and pacing.** Skip providers in cooldown or at their concurrency cap (the dispatch script enforces both and tells you). Also apply soft-cap pacing from `ledger.py usage`: a provider approaching its `soft_weekly_cap` (~70%+) gets deprioritized in the cascade; one over 100% is treated as a soft cooldown — route elsewhere unless no capable alternative exists. This deliberately preserves flat-rate quota for late-period tasks that genuinely need that provider, instead of burning it early and slamming into the hard limit. 4. **Track record.** Consult the ledger summary for this task type. A provider with repeated `absorbed`/`failed` outcomes on this task type gets skipped even if cheaper — failed delegations cost more than expensive successes. A cheap provider with a strong record beats a pricier default. **Ballpark the spend before dispatching to a per-token provider.** Estimate expected tokens (brief size plus the files the worker must read for input; expected diff/output volume for output) and price them with the registry's researched `per_mtok` rates — never rates from memory. Compare against the same tokens at the registry's `supervisor` rates (your own model's researched price): that spread is the expected saving, and it is also your `--self-cost-usd` baseline at ledger time. When you have measured self-usage baselines for this task type (see Step 6), prefer them over the token-parity assumption. If the spread is thin or negative, the dispatch overhead decides — keep the task. Flat-rate providers skip the arithmetic: their marginal cost is zero, which is why the cascade burns them first. State your routing choice in one line to the user when you dispatch ("sending the test suite to antigravity: free tier, large context, 5/5 on test tasks"). If the user named a provider, use it. Model override: pass `--model` only when the user asked or the registry notes call for it; otherwise the provider's default model applies. Never invent model names. ## Step 3 — Dispatch 1. Write the brief to `.fleet-runs/briefs/.md` per `references/brief-template.md` (read it the first time each session). The brief is the entire context transfer — objective, stack, exact paths, requirements, constraints, runnable acceptance commands, out-of-scope. 2. Dispatch: ```bash bash /fleet-dispatch.sh --provider --brief .fleet-runs/briefs/.md ``` Optional: `--model`, `--effort` (codex only), `--cwd`, `--resume `. 3. The script returns immediately with TASK_ID, provider, model, session, and log path — record the TASK_ID in your todo list and tell the user in one line what you delegated, where, and why. Never dispatch silently. **Multi-task builds: show the plan.** When delegating two or more tasks, present a dispatch plan before the first dispatch and keep it updated: ```markdown | Task | Provider | Model/effort | Depends on | Gate (acceptance) | Status | ``` **Dependency gating:** never dispatch a brief whose inputs depend on another in-flight task — wait for the upstream artifact to exist and verify it first. When chaining, the downstream brief references the actual artifact (diff, file paths, test output), never your summary of it. If dispatch prints `PROVIDER_UNAVAILABLE` or `BUSY`: move to the **next provider in your routing order and re-dispatch the same brief.** Absorb the task yourself only when no capable provider remains. A provider being down must never block the build. ## Step 4 — Supervise Go do your own tasks — that is the point. Between them: ```bash bash /fleet-status.sh # or --all ``` Out of your own work? Block efficiently: `bash /fleet-status.sh --wait --timeout 900` **Collision discipline:** never edit files listed in an in-flight brief. Need one? Wait, or take the task back and treat partial changes as untrusted. ## Step 5 — Verify, then accept (mandatory) `STATUS DONE` means the worker exited cleanly — a claim, not evidence. Before accepting: read `git status` and `git diff` (check nothing outside the brief's scope changed) against the pre-dispatch snapshot in `.fleet-runs//pre-state` (revision, branch, and files that were already dirty — anything listed there is not the worker's doing), run the brief's acceptance commands, and run the `verification-gate` skill if available. Only then report completion. If verification fails: - **Small fix:** do it yourself — faster than a round trip. - **Substantive rework, provider supports resume** (codex, opencode, claude, grok, cursor, qwen, droid): write a short follow-up brief stating exactly what's wrong, then `--resume `. Max 2 rounds, then take over. Keep follow-up briefs additive — state what to change without restating or rewriting the original brief, so the worker-side prompt cache of the session prefix stays warm (cache hits are the cheapest tokens there are). - **Substantive rework, no resume** (antigravity, gemini, copilot, amp): dispatch a fresh brief that includes a summary of the prior round's diff and what to change. ## Step 6 — Record the outcome (mandatory, every task) After the final outcome of every delegated task — accepted, remediated, absorbed, or failed — append to the ledger: ```bash python3 /ledger.py append --provider --model "" \ --task-type \ --outcome --task-id \ [--cost-usd X] [--self-cost-usd Y] [--tokens N] [--wall-seconds S] \ [--notes "..."] ``` Pull cost/tokens from the status output's USAGE line when present (opencode and claude report cost directly; codex, gemini, cursor, qwen, and grok report tokens — compute cost from the registry's per-token prices for API providers; omit for flat-rate). Plain-text providers (antigravity, copilot, amp) and droid report no usage at all — ledger the outcome without cost/tokens, or add `--wall-seconds` only; for usage-billed ones (amp, droid) check the provider dashboard when true-cost accounting matters. **True-cost accounting:** log `--cost-usd` for `absorbed` and `failed` outcomes too — the invoice doesn't refund a misroute, so wasted spend must count against that provider's record. When a failed delegation gets redone and the redo cost is known (e.g. a replacement worker's reported cost), add `--redo-cost-usd`. The summary's WASTED and true_cost figures exist so that a cheap provider that fails often looks as expensive as it really is — sticker price is not the routing signal; cost-to-merge is. This step is what makes routing smarter every week — skipping it freezes the system at launch-day intelligence. **Realized savings:** for accepted/remediated tasks, also log `--self-cost-usd` — your estimate of what the task would have cost done in-session. Standard estimate: the worker's reported token usage priced at the registry's `supervisor` rates (the same work would have consumed roughly the same tokens from you). Those rates are researched and date-stamped at `/fleet-init` — do not substitute rates from memory; if the block is missing or its date-stamp is stale, research the current price (web search) and offer to save it to the registry. When the worker reports no tokens, estimate from the diff size, or omit rather than guess wildly. With it, `summary` reports SAVED per provider and a fleet-wide REALIZED SAVINGS total — the number that proves (or disproves) that delegation is paying for itself; surface it to the user when reporting costs. Misroute spend counts as negative savings automatically, so never log self-cost for absorbed/failed tasks — you paid the in-session cost anyway, there is no counterfactual gain. **Measure, don't just estimate, where the harness allows.** Your own session's token usage is already on disk, and `/self_usage.py` reads and prices it (Claude Code and Codex transcripts; exits 1 on other harnesses — stick with the parity estimate there). Two measurements sharpen the savings math: - **Absorbed-task baselines.** Before doing a substantial task yourself, `python3 /self_usage.py snapshot`; when done, `python3 /self_usage.py delta`. The priced delta is what that task actually cost in-session — record it in the ledger entry's `--notes` (`"measured self-cost $X"`) and prefer accumulated measurements for the task type over the parity assumption when estimating future `--self-cost-usd`. - **Dispatch overhead.** Snapshot when you start writing a brief, delta after acceptance: that is the real supervision cost (brief, polling, diff review) a delegation must clear before it saves anything. If measured overhead rivals the estimated spread, Step 1 should have kept the task — recalibrate your "substantial" threshold accordingly. ## Failure handling - **RATE_LIMITED** — a per-provider cooldown is written automatically; that provider refuses dispatch until it expires. Re-route the brief down the cascade. Mention the fallback to the user in one line. - **AUTH_ERROR** — tell the user which CLI to re-authenticate (`codex login`, `claude /login`, `agy` Google login or `GEMINI_API_KEY`/`ANTIGRAVITY_API_KEY`, `grok login`, `cursor-agent login` / `CURSOR_API_KEY`, `copilot` login / `GH_TOKEN`, qwen API key, `FACTORY_API_KEY`, `amp login` / `AMP_API_KEY`, `opencode auth login`). Re-route meanwhile. - **FAILED** — read ERRORS and stderr, then pick the rung that matches the failure. Transient error or a brief that lacked concrete evidence → one retry, same provider, corrected brief. Failure that reveals a capability gap → **escalate, never sidestep**: raise `--effort` where supported, or re-route to a *stronger* provider (per ledger record / registry strengths) — never retry a capability failure on a weaker or equal-cheaper worker just because the cascade lists it next. No stronger option → absorb. Never retry more than once per provider. - **INCOMPLETE** — worker died mid-run. Inspect the diff, revert partial changes, treat as FAILED. - Ledger every one of these. ## Concurren … ## Source & license This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code. - **Author:** [adam-N-singh](https://github.com/adam-N-singh) - **Source:** [adam-N-singh/fleet](https://github.com/adam-N-singh/fleet) - **License:** MIT Install and usage instructions live in the source repository linked above. ## Pricing - **Free** — Free ## Security capabilities Automated source analysis of v0.1.0 — what this tool can access: - **Network access:** no - **Filesystem access:** no - **Shell / process execution:** no - **Environment & secrets:** no - **Dynamic code execution:** no *"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.* ## Versions - **0.1.0** — security scan: passed — Imported from the upstream source. ## Links - Listing page: https://agentstack.voostack.com/l/skill-adam-n-singh-fleet-fleet-delegation - Seller: https://agentstack.voostack.com/s/adam-n-singh - Browse the marketplace: https://agentstack.voostack.com/browse --- Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.