Workflow

workflow — multi-phase JS orchestration over provider subagents

Wrong lane? A flat one-shot fan-out of independent tasks → /cc-fleet:subagent.

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

Execution environment — check before running anything. Confirm this shell runs on the machine where cc-fleet is installed. In a remote or split-host agent session the shell can run on a separate machine with its own filesystem and PATH, so cc-fleet: command not found or a wrong working directory can indicate that the shell is running on the wrong machine, not that cc-fleet is broken — route commands through a host-executing bridge and pass host paths for the files you reference, and stop cleanly if no such bridge exists. (Different-machine case only; for the same-machine codex sandbox — a clean doctor but network-blocked leaves — see Approval & sandbox below.)

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().

Preflight (once per session)

On the first workflow run in a session, run cc-fleet doctor --json (your shell tool) and read the per-check results. The ONLY hard stops are the claude binary check and the fingerprint check — cc-fleet drives the claude CLI as the leaf worker engine, so if either fails no leaf can run: stop and tell the user to install or fix Claude Code. Do not stop on the "providers reachable" check — it aggregates every enabled provider, so one unrelated provider (especially a daemon-backed codex / openai-*, whose loopback proxy is only up during a spawn) flips the run to exit 1 while your target provider is fine. tmux checks are optional — workflow needs no tmux.

Approval & sandbox (non-bypass codex)

Each leaf is a network-using claude -p child (→ the provider API), and the run writes its script (/tmp) and a journal. On a codex that is not in full-bypass mode, expect — and pre-explain to the user — two things, neither a cc-fleet bug:

• Approval prompts are normal. A non-bypass approval policy asks the user to approve each cc-fleet … call (workflow run, every chunked wait); approving is expected; to reduce the prompts the user can lower the approval friction (--ask-for-approval) or run codex bypassed.
• A sandbox can block every leaf. A workspace-write sandbox disables network by default, so each leaf fails looking like PROVIDER_UNREACHABLE / a timeout while the real cause is the sandbox, not the provider. Heuristic: cc-fleet doctor is locally clean but all leaves are "unreachable" → the sandbox is blocking egress — approve the escalation, have the user enable network for the workspace-write sandbox, or run codex bypassed.

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); provider: "claude" runs the official claude CLI on the user's OWN Claude Code login (subscription OAuth) instead of a configured provider — a literal model id (fable/opus/sonnet/a full id, omitted → claude's login default, typically the costliest tier so name one), no roster keywords, no key material. 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.
  • max_turns — size it generously for read-heavy work: a multi-file / git-inspecting leaf spends ~1 turn per file read or command, so give it 30–50 (or omit it); a small cap (8/10) starves it and the leaf fails SUBAGENT_MAX_TURNS mid-run (budget ~$0.3–0.9 for such a leaf). A schema'd leaf additionally needs ≥ 3 just for the forced StructuredOutput call (above).
• 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.) A provider: "claude" leaf spends the lead session's own subscription window, not a metered provider — use it for one or two synthesis / judgement nodes, never a wide fan-out. Its usage still flows into the run's token / USD surfaces, but the USD is claude's notional list-price (a subscription is not metered per token); max_budget_usd / --budget-usd still gate against that notional figure.
• 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.

A codex-launched run is grouped on the board under your Codex launcher (a codex <thread> header, not (no session)) — still give each leaf a label (the script opt; --label on a bare cc-fleet subagent) and the run a clear meta.name so you can pick yours out within that group.

Where to write the script. For a one-off research / analysis run, write the script to /tmp/cc-fleet-<name>.js — do NOT add it to the user's repo unless they ask to keep the workflow. For read-only research, give the leaves profile: "slim-ro" and write the prompts so the leaves do not edit files.

Waiting on a run: block in chunks (codex has no background wake)

codex has no background-task wake, so you await a run by blocking the shell in bounded chunks — codex tolerates a long blocking command, and chunked blocking is the await. Keep the run id and re-issue wait until the run settles:

RUN=$(cc-fleet workflow run audit.js)               # KEEP $RUN for the whole run's life
cc-fleet workflow wait "$RUN" --timeout 2m --json   # one chunk; re-issue while still running

wait returns the moment the run settles OR the window elapses. While the run is still progressing it exits 124 with wait_outcome timeout (a heartbeat, not a verdict) — re-issue the SAME wait "$RUN" for the next chunk. Always keep $RUN: a timed-out wait loses nothing, a fresh wait "$RUN" just resumes blocking and the run carries on detached. Never spawn an agent to poll, and never tight-loop status — wait is the blocking primitive.

If the codex shell yields with "Process running with session id …", that backgrounded wait is STILL the live wait — do NOT issue a second wait "$RUN" (a needless concurrent wait); let it return. Size each --timeout chunk SHORTER than your shell's foreground window so the command returns cleanly rather than being backgrounded. (Omitting --timeout / passing --timeout 0 blocks in one shot until the run settles — use only when you mean to block to the very end and will reattach to the yielded session; the chunked pattern is the default.)

Make the FIRST chunk short (2–3m — a provider auth/balance failure surfaces on the first leaf call), then 10–15m per re-issue. Dispatch each returned chunk on wait_outcome (+ exit code):

• terminal (exit 0 done/stopped · exit 1 failed) — fetch the detail with cc-fleet workflow status "$RUN" --json (it carries run_error and the per-leaf jobs[]; the wait envelope deliberately omits them) and report. To read a leaf's actual ANSWER (status/wait omit answers), use cc-fleet workflow result "$RUN" --label <leaf> --json — so label the leaves whose output you'll want.
• engine_gone (exit 1) — the detached engine died without finalizing; propose cc-fleet workflow run audit.js --resume "$RUN" (the journal replays the finished leaves).
• parked (exit 3) — every remaining leaf is held. FIRST re-check cc-fleet workflow status "$RUN" --json: leaves running/queued again means it was a transient (the engine was between leaves) → re-issue wait. Still parked → name the envelope's held leaves to the user and propose cc-fleet workflow restart "$RUN" --leaf <job|label>; never wait it out.
• timeout (exit 124) — still running. Compare counts / spent_usd / spent_tokens with the previous chunk: progress → one short progress line and re-issue with a longer --timeout (10–15m); zero delta over several chunks → inspect (workflow status; is one long leaf still inside its own timeout?) and escalate only on a real anomaly, else re-issue.

After a session restart, re-issue wait for every running run from cc-fleet workflow list --json.

For a human live view: cc-fleet workflow watch "$RUN" streams the run's events as text 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 structured error_code is in workflow status --json (jobs[]); an in-script catch receives a plain Error message (e.g. agent(provider): KEY_INVALID: …), so read the code from status --json rather than parsing the message for control flow. Same vocabulary as a one-shot subagent (the full table with context lives in /cc-fleet:subagent); the dispatch:

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);
}

// one final synthesis node on your OWN subscription — a single judgement leaf, not a fan-out
const verdict = await agent("Rank these gaps by severity and name the top three:\n"
                            + gaps.join("\n"), {provider: "claude", model: "opus", label: "verdict"});

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

One run, three phases, a barriered fan-out, a no-barrier pipeline, a bounded loop-until-dry, and a single claude synthesis node — all sequenced by the script in a cc-fleet process, off your context. The script's top-level return value is NOT persisted or retrievable — to read the run's output, fetch a labeled leaf's answer with cc-fleet workflow result "$RUN" --label verdict --json.

Anti-patterns

• A script for a single flat independent batch → /cc-fleet:subagent.
• 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).