workflow

workflow — multi-phase JS orchestration over provider subagents

Wrong lane? A flat one-shot fan-out of independent tasks → /cc-fleet:subagent; interactive collaboration you message back and forth → /cc-fleet:team; arbitration in cc-fleet-shared/routing.md.

When this skill cites cc-fleet-shared/<file>.md, OPEN it with the Read tool at ../cc-fleet-shared/<file>.md relative to this SKILL.md — the cited content is load-bearing, not optional background.

A workflow is a JavaScript script that fans out provider cc-fleet subagent leaves and runs in a cc-fleet process, OFF the main session's context. You write the script; cc-fleet workflow run executes it. The orchestration plan lives in script variables (CPU, ~0 of your tokens) — you are invoked only when authoring the script, not on every scheduling decision. The API mirrors the native Claude Code Workflow tool — write the script exactly as you would a native workflow; the only addition is the provider option on agent().

When to use it

• Multi-phase or dynamic orchestration over many provider subagents: fan-out + barrier, per-item pipeline, loop-until-dry, branch-on-result, with a board run-tree.
• A single flat batch of independent one-shots is not a workflow — that's /cc-fleet:subagent. Don't write a script for it.

The provider ask ladder (ask at most once per task)

1. The user named a provider or model → use it.
2. Else run cc-fleet default --json: if it returns a provider (source "configured" or "auto"), use it and STATE it in your kickoff line (e.g. "using glm (default)").
3. Else (several providers, none default) ask the user ONCE which to use — list the enabled providers from cc-fleet list --json (name + default_model + the one-line note in cc-fleet-shared/providers.md). After they pick, run cc-fleet default <chosen> so you never ask again. (cc-fleet default <p> is user-layer; only run it to FILL a blank default, never with --force.)
4. A mid-task provider failure (insufficient balance / rate limit / auth) → STOP, tell the user what happened, propose the next provider, and WAIT for their confirmation. Never switch providers silently.

Model tier within a provider: fan-out / leaf work → omit --model (or --model fast); judge / synthesis / sustained work → --model strong. The provider's roster decides the actual model — see cc-fleet-shared/providers.md.

In a script, agent()'s opts.provider is optional: omitted, the leaf uses the run's default provider, resolved ONCE at launch and recorded with the run — so --resume stays stable even if the default changes later. A script meant to be shared or reproducible should still pin provider explicitly.

The script API (mirrors the native Workflow tool)

• const meta = {name, description, whenToUse?, model?, phases?: [{title, detail?}]} — a top-level pure literal (no calls/vars/spreads; the native export const meta form is also accepted). name + description are required; model is the default for agents that omit it. Read statically before the run → the board shows the named, phase-skeletoned run immediately.
• agent(prompt, opts) → Promise<string|object> — runs ONE provider subagent leaf. opts.provider is optional (omitted → the run's default provider, above); the rest are optional: model, schema, label, phase, timeout (seconds), max_budget_usd, max_turns, isolation: "worktree", profile ("slim" default / "slim-ro" / "full"), tools, skills, mcp. An unknown option key throws (typos fail loudly). On a leaf failure the promise rejects — an un-caught top-level await agent() aborts the run; inside parallel/pipeline a failed element degrades to null. Leaf failures classify like subagent failures — dispatch table in "Leaf failures" below.
  • schema (a plain object) goes to the claude child via --json-schema: claude injects a forced StructuredOutput tool and enforces that it is CALLED (the native mechanism — no JSON instruction is added to the prompt); the promise resolves with the parsed structured payload. The three rules:
    • a validation failure — or a result envelope without a structured payload — FAILS the leaf; there is NO automatic retry;
    • the forced StructuredOutput call costs turns — give a schema'd leaf max_turns ≥ 3 (a budget of 1 starves it);
    • needs claude ≥ 2.1.88 (the slim-profile floor); an older claude fails the leaf with a classified usage error. Client-side validation backstops with a recursive JSON-Schema subset: type (object/array/string/number/integer/boolean/null; integer accepts 5.0), required, nested properties, array items, scalar enum, string pattern (RE2 best-effort — the wire enforces the authoritative ECMA regex) / format (email/uri/uuid/date/date-time), additionalProperties, allOf/anyOf/oneOf, and intra-document $ref (#/… pointers; an external URI is unsupported and fails).
  • isolation: "worktree" runs the leaf with cwd = a fresh git worktree (torn down after), so parallel file-editing leaves don't collide (requires a git repo).
  • profile: "slim" (the default; write-capable) / "slim-ro" (read-only research) / "full" (ONLY to compare against a full session or diagnose a suspected slim regression). Writes files → slim, read-only → slim-ro. tools, skills (default true) and mcp refine a slim leaf, are rejected with profile: "full", and tools REPLACES the whole set, never appends. Tool whitelists / per-profile mcp defaults / the pre-2.1.88 fail-open downgrade: cc-fleet-shared/providers.md. The run journal folds the effective profile + tools, so a --resume re-runs a leaf whose shape changed.
• Background = an unawaited promise. There is no run_in_background/wait(): start a leaf with const p = agent(...), keep working, await p later (Promise.all for a batch). Every leaf — awaited or not — is pool-bounded, journaled at completion, and the run only finalizes after all of them settle. A leaf that rejects with nobody ever handling it fails the run (a silently dropped failure is still a failure); fire-and-forget tolerance is an explicit p.catch(() => null).
• parallel(thunks) → Promise<array> — run each 0-arg thunk concurrently; BARRIER (settles once all finish), null where an element failed: await parallel([() => agent("a", {provider: "glm"}), () => agent("b", {provider: "glm"})]). Concurrent execs stay ~pool size even for a huge list (excess queues).
• pipeline(items, ...stages) → Promise<array> — push each item through all stages independently with NO inter-stage barrier (item A can be in stage 3 while B is in stage 1). Each stage is (prev, item, index) => … (sync or async; its return value is awaited). A failing stage drops that item to null and skips its remaining stages. DEFAULT to pipeline over parallel — only use parallel when a stage genuinely needs ALL prior results together.
• workflow(path, args?) → Promise — run another .js inline on the same engine (shared pool/journal/budget), one level deep only; resolves with the child's top-level return value.
• budget — two parallel cap surfaces. USD: budget.total (the --budget-usd cap in USD, or null), budget.spent(), budget.remaining() (Infinity when uncapped) — USD floats (an Anthropic list-price estimate). Tokens: budget.tokens_total (the --budget-tokens cap, or null), budget.tokens_spent(), budget.tokens_remaining() — ints (input+output, cache-read excluded). agent() throws once either cap is reached; a while (budget.remaining() > N) loop scales depth to the cap. (Native's budget.total is a token target; here it is USD — --budget-usd is the cross-provider cap since providers price tokens differently — and tokens are the separate tokens_* surface.)
• phase(title, detail?) — name the current phase (tags subsequent agents lacking an explicit phase; the detail shows on the board row). log(msg) — a narrator line (board live log + stderr); console.log/info/warn/error/debug alias onto it (non-strings render as JSON, Errors by message).
• args — the parsed --args-json '<json>' value (or the workflow(child, args) value); undefined when none was given.

What a workflow script can NOT use (determinism — the journal depends on it)

• Date / Math.random() throw; eval / Function / dynamic code are removed; there is no setTimeout / require / fs / ESM import — pass timestamps or randomness in via args.
• Plain script statements only (the body runs inside an async wrapper, so top-level await and return work); async generators (async function*) are not supported.

Running it

RUN=$(cc-fleet workflow run audit.js)        # detached; prints ONLY the bare run id
cc-fleet workflow status "$RUN" --json       # manifest + every tagged leaf (run→phase→agent)
cc-fleet workflow list --json                # all runs, newest first
cc-fleet workflow stop "$RUN"                # reap a running run (engine + in-flight leaves)
cc-fleet workflow stop "$RUN" --leaf <job|label>  # hold ONE agent in place (run keeps going); --phase <title> holds a phase
cc-fleet workflow restart "$RUN" --leaf <job|label>  # re-run a held/running agent in place; --phase <title> a phase;
                                             # on a FINISHED run: keyed re-run (whole run, --leaf, or --phase)
cc-fleet workflow wait "$RUN" --timeout 3m --json  # block silently until the run settles ("Waiting on a run" below)
# or watch the board's Dynamic Workflows view: live log, token/cost columns, prompt/answer drill-in.
# x/r there are level-scoped: run row = the run, Phases pane = the phase, agent pane = the leaf
# (a held agent shows ▶ until you restart it). --foreground runs inline (debug).
# `held` in status output = parked by the control plane: an operator paused it (board
# x, stop --leaf/--phase) or a restart was refused (budget gate); a restart in flight
# may show it briefly. Not an error/retry/backoff — the run waits on it indefinitely.
# If held persists across polls, resume it with restart --leaf/--phase or tell the
# user it is parked; never wait it out.
# --max-concurrency N overrides the default pool (min(16, cores-2));
# --budget-usd N caps total spend; --no-persist-io disables the prompt/answer drill-in.

The run is detached so it outlives this call and your session stays responsive.

Waiting on a run: arm wait in a backgrounded Bash (push, not poll)

Right after launching, arm the notifier — a backgrounded Bash whose EXIT is your wake-up:

RUN=$(cc-fleet workflow run audit.js)
# Bash tool with run_in_background=true; the harness wakes you when it exits:
cc-fleet workflow wait "$RUN" --timeout 3m --json

End your turn and keep working — never spawn an agent (or loop yourself) to poll a run. On the wake, dispatch on the envelope's wait_outcome (+ exit code):

• terminal (exit 0 done/stopped · 1 failed) — fetch the detail with workflow status "$RUN" --json (it carries run_error and the per-leaf list; the wait envelope deliberately doesn't) and report.
• engine_gone (1) — the engine died without finalizing; propose cc-fleet workflow run <script> --resume "$RUN" (the journal replays the finished leaves).
• parked (3) — every remaining leaf is held. FIRST re-check workflow status: leaves running/queued again means it was a transient (the engine was between leaves) — re-arm silently. Still parked → name the envelope's held leaves to the user and propose restart --leaf; never wait it out.
• timeout (124) — a heartbeat, not a verdict. Compare counts/spent_* with the previous snapshot: progress → one short progress line and re-arm with a longer window; zero delta → inspect (workflow status; is one long leaf still inside its own timeout?) and escalate only on a real anomaly, else re-arm.

Window sizing: make the FIRST window short (2–3m — a provider auth/balance failure surfaces on the first leaf call), then 10–15m per re-arm. One wait per run; they are independent. After a session restart, re-arm every running run from workflow list --json.

For a human live view: cc-fleet workflow watch "$RUN" streams the run's events as text (in a terminal, or a backgrounded shell → the /tasks panel) and cc-fleet watch streams the whole fleet; the board's Dynamic Workflows view has the rich drill-in. Both print only canonical status — never a provider reply.

Leaf failures — dispatch on error_code (do not parse prose)

A failed leaf's error_code is in workflow status --json (jobs[]) and in the rejection that reaches the script. Same vocabulary as a one-shot subagent (the full table with context lives in /cc-fleet:subagent); the dispatch:

error_code	What you do
INSUFFICIENT_BALANCE / KEY_INVALID / RATE_LIMITED	STOP — provider ask ladder, step 4 (never switch silently). KEY_INVALID → the user rotates the key; RATE_LIMITED → brief wait, one retry.
NO_DEFAULT_PROVIDER / DEFAULT_PROVIDER_DISABLED / DEFAULT_PROVIDER_UNKNOWN	No usable default for a provider-less agent() — apply the provider ask ladder, then re-run.
MODEL_NOT_FOUND	cc-fleet refresh <provider>, or drop the leaf's model to use the provider default.
SUBAGENT_TIMEOUT	Raise the leaf's timeout or split the task; awaited background leaves are backstopped at 45m.
SUBAGENT_OUTPUT_TOO_LARGE	The leaf's output exceeded the byte cap — have it write to a file and answer concisely; a blind retry overflows again.
SUBAGENT_STOPPED	An operator stopped it (stop --leaf / run stop) — terminal, NOT a failure; never auto-retry.
SUBAGENT_FAILED / PROVIDER_API_ERROR	Inspect (workflow status); restart --leaf once, or propose a provider switch (ask first).
FINGERPRINT_MISSING / FINGERPRINT_STALE	Self-heal flow in cc-fleet-shared/troubleshooting.md (STALE = no claude binary — the flow can't help; fix Claude Code / PATH).
CODEX_PROXY_UNAVAILABLE / CODEX_CLOUDFLARE_BLOCKED	cc-fleet codex login / free the port; a Cloudflare block → switch network, don't rotate credentials.
UNKNOWN_PROVIDER / PROVIDER_DISABLED / CONFIG_LOAD_FAILED	Config problem — cc-fleet list --json, cc-fleet add / edit --enable; CONFIG_LOAD_FAILED → cc-fleet doctor.
SUBAGENT_BAD_ARGS	Bad leaf options — fix the script, re-run.

Resume (content-hash journal)

Each run records a content-hash journal of its completed leaves. Re-run the same script under an existing run id to replay:

cc-fleet workflow run audit.js --resume "$RUN"   # journaled leaves return cached (no provider exec); only un-run leaves run

A leaf is keyed by its determinant (provider + model + prompt + schema + slim shape), so an unchanged re-run is ~100% cache hits, a leaf whose prompt you edited (and anything downstream of its output) re-runs, and a run that was killed resumes by replaying what finished before the kill. The determinism lockdown makes this exact: with no clock/PRNG, the same script+args produce the same keys. A failed leaf is never journaled, so resume re-runs it.

Non-goals (state plainly, don't oversell)

• No pause. A running claude -p can't be cleanly suspended; use workflow stop (reaps the run) + run --resume (cheap restart via the journal) instead.
• Client-side schema validation is a JSON-Schema subset — the list above, not the full spec (an external $ref URI is unsupported and fails; an unknown format is an annotation, not enforced). claude enforces that StructuredOutput is called; this backstop checks what it was filled with, and a failure is terminal (no retry).
• Key-safety is unchanged: the provider key flows only via apiKeyHelper; prompts go to the leaf via stdin, never argv; the journal/events/board carry no key.

Worked example — research sweep (fan-out → pipeline → loop)

const meta = {
    name: "api audit",
    description: "map endpoints, draft checks, then probe for gaps",
    phases: [{title: "map"}, {title: "build"}, {title: "probe"}],
};

phase("map");
const maps = (await parallel(
    args.map((m) => () => agent("List exported endpoints in module " + m,
                                {provider: "deepseek", label: "map:" + m}))
)).filter(Boolean);  // e.g. --args-json '["auth","billing","users"]'

phase("build");
// pipeline (no barrier): each map flows straight into its own checklist draft
const checklists = await pipeline(
    maps,
    (prev, item, i) => agent("Draft an audit checklist for these endpoints:\n" + prev,
                             {provider: "glm", label: "build:" + i}),
);

phase("probe");
const gaps = [];
while (gaps.length < 10) {           // loop-until-dry (the runtime hard-caps 1000 leaves/run)
    const g = await agent("Given these checklists, name ONE uncovered risk, or reply NONE:\n"
                          + checklists.join("\n"), {provider: "kimi"});
    if (g.trim() === "NONE") break;
    gaps.push(g);
}

log(`done: ${maps.length} maps, ${checklists.length} checklists, ${gaps.length} gaps`);
return { maps, checklists, gaps };

One run, three phases, a barriered fan-out, a no-barrier pipeline, and a bounded loop-until-dry — all sequenced by the script in a cc-fleet process, off your context.

Anti-patterns

• A script for a single flat independent batch → /cc-fleet:subagent.
• A long-lived collaborator you message back and forth → /cc-fleet:team.
• Date.now() / setTimeout — unavailable (determinism); pass timestamps via args.
• Trusting schema as deep validation, or treating a plain agent() result as JSON without schema.
• Unbounded ambition: the runtime hard-caps 1000 agent() calls/run, pools concurrency at min(16, cores-2), and caps a single parallel/pipeline list at 100,000 elements.
• Switching providers silently after a balance / rate-limit / auth failure → stop, tell the user, wait for their pick (provider ask ladder, step 4).