Standing operating model — Opus (and its glm-5.2 Opus-equivalent) orchestrates while spawned workers do the work across three substrates: native Sonnet/Haiku Agents, z.ai GLM lanes (glm-fleet.mjs), AND ollama-cloud peer lanes (ollama-fleet.mjs), ALWAYS at max reasoning / --reasoning high, then the orchestrator reviews, corrects, and finalizes. Ships copy-paste dispatch templates, the GLM tier roster, dual-substrate routing, the governed autonomous loop, and Agent-not-Workflow discipline. Use to start or run this model on any non-trivial build, research, multi-file edit, audit, refactor, or fan-out task.
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name
opus-fleet
description
Standing operating model — Opus (and its glm-5.2 Opus-equivalent) orchestrates while spawned workers do the work across three substrates: native Sonnet/Haiku Agents, z.ai GLM lanes (glm-fleet.mjs), AND ollama-cloud peer lanes (ollama-fleet.mjs), ALWAYS at max reasoning / --reasoning high, then the orchestrator reviews, corrects, and finalizes. Ships copy-paste dispatch templates, the GLM tier roster, dual-substrate routing, the governed autonomous loop, and Agent-not-Workflow discipline. Use to start or run this model on any non-trivial build, research, multi-file edit, audit, refactor, or fan-out task.
version
1.1.0
status
active
triggers
["/opus-fleet","opus orchestrates","spawn agents","fan out agents","three lane execution","research code testing lanes","max reasoning agents","agent fleet","glm fleet","zai fleet","dual substrate","glm-fleet"]
scope
harness
invocation
workflow
opus-fleet — Opus orchestrates, Sonnet/Haiku agents execute
The default way to run any non-trivial task. The main Opus session never grinds parallelizable work itself — it orchestrates: decompose → dispatch a fleet of Sonnet + Haiku Agents (always max reasoning) across three lanes → review and correct every result → finalize. Greater quality, and cheaper — Sonnet bills a separate weekly pool, so the fan-out barely touches the Opus quota.
Every substantial task — build, research, multi-file edit, audit, verification, refactor. Skip only trivial single reads and pure conversation.
Fleet sizing — don't under-spawn. Native Agents are FLAT (they can't spawn sub-agents — Explore has no Agent tool, and the model keeps them leaf-only). So trade depth for BREADTH: fanning out up to ~12 parallel native Agents in one message is encouraged when the work divides that way (Sonnet bills a separate weekly pool — breadth is cheap). Recursion depth is the GLM / nano-spawn substrate's job, capped at depth 5 (heavy tier) with decaying per-level fan-out. Self-size to the task — but the failure mode here is timidity, not excess.
Run independent lanes in parallel (multiple Agent calls in one message). Every prompt is self-contained and ends with the max-reasoning directive.
Agent(
description: "<3-5 word label>",
subagent_type: "Explore" // read-only research; or "general-purpose" for edits/bash
model: "sonnet", // sonnet = judgment/code-gen; haiku = read/scan/mechanical
prompt: `Use MAXIMUM reasoning depth — be exhaustive and rigorous.
<ROLE>: <Research | Code-generation | Testing> lane for an Opus-orchestrated task in <repo>.
CONTEXT (self-contained — you lack the conversation): <what changed / state>.
TASK: <exact, bounded>.
DO NOT: touch protected paths; commit; rewrite dated history.
RETURN: <exact format — table / verdict / diff>.`
)
Model routing
Lane
Default model
Use when
Research (classify, judge, prior-art)
sonnet
needs judgment over what it finds
Research (census, grep-survey, read-dump)
haiku
mechanical breadth, go heavy
Code-generation (patches, new files, refactors)
sonnet
correctness matters
Testing (run scripts, parse logs, verify)
haiku
mechanical execution + report
Explore = read-only (can't edit) — safest for research. general-purpose = full tools — for edits/bash. Never spawn an Agent to read one known file (do it inline).
The orchestrator loop (Opus keeps these)
Decompose into independent lanes; identify what genuinely parallelizes.
Dispatch parallel Agents (one message, multiple calls), each self-contained, each at max reasoning.
Review + correct — first-run agent output is a hypothesis. Verify every load-bearing claim against local evidence; override what's wrong.
Finalize — Opus only: scoped-pathspec commit, push, irreversible/outward calls, protected-path discipline. Agents never finalize.
Hard rules (failure-anchored)
Agent, never Workflow. Workflow agent() bills the main/Opus quota; the Agent tool with model:"sonnet" bills the separate Sonnet pool.
Always max reasoning. The Agent tool has no effort param, so keep the orchestrator at max effort (agents inherit) AND pin Use MAXIMUM reasoning depth as the first line of every agent prompt.
Verify every agent result. A Haiku testing lane (2026-06-22) couldn't run its check, guessed from reading code, and reported the guard would BLOCK existing .claude files — direct evidence showed WARN. Override agent guesses with local runs.
Agents earn their keep. Same session, a Sonnet research lane found a real ENOENT (yuri-supercharge-gate.mjs pointing at a deleted test) that the solo pass missed — the fan-out is not theater.
Self-contained prompts. Agents lack the conversation; an underspecified prompt returns confident garbage. Always include state + constraints + return format.
GLM lanes: never tee/>, always --out. A second reader on the z.ai/Anthropic stream triggers transport:EPIPE (fixed root cause — don't reintroduce). glm-fleet.mjs collects each lane's text from its own --out file.
GLM agents run at --reasoning high. That is the GLM max-equivalent — there is no higher tier. Pin it on every GLM task (the native-Agent "max reasoning" analogue).
The GLM fleet is DISARMED by default.glm-fleet.mjs dry-runs (zero spend, zero fan-out) without YURI_GLM_FLEET=1; arming it — and YURI_SWARM_CONVERGENCE / YURI_NANOSWARM_SPAWN — is owner-gated (durable z.ai spend + process fan-out are not git-reversible).
Verify GLM output locally — lanes over-claim. A peer lane reporting "done" is a hypothesis (precedent: 18/19 false "done", a raw arithmetic hallucination). Re-check against local evidence before trusting, exactly as with native agents.
Anti-rationalization table
Excuse
Reality
"This is small, I'll just do it solo."
Then it's trivial → fine. But "multi-file / multi-step / needs a survey" is exactly the fan-out case; solo is slower and misses things (the ENOENT above).
"Workflow is right here and easier."
Workflow drains the Opus quota and is banned here. Agent(model:"sonnet") is the only fan-out path.
"The agent said it passed, ship it."
Agent output is a hypothesis. The Haiku lane guessed wrong this session. Verify against a local run before trusting.
"Max reasoning costs too much."
Sonnet bills a separate weekly pool. The cost concern is inverted — fan out liberally.
"I'll skip the context, the agent will figure it out."
It can't — it has no conversation history. Underspecified → confident garbage. Self-contained prompt every time.
"GLM is the read-only / advisory lane."
Wrong — GLM lanes carry the full harness (write / edit / bash) and build exactly like native Agents; glm-5.2 is an Opus-peer. Route by quota / context / recursion / native-tool-need, never by a capability tier that doesn't exist.
"I'll pipe the GLM call through tee to watch it."
That triggers transport:EPIPE. Use --out and read the file. Never a second stream reader on the lane.
"Arm the autonomous loop so it just runs unattended."
Arming spends durable budget + fans out processes — owner-gated, always. Build + smoke DISARMED; the owner flips the flag.
Optional — loop until done
For unknown-size discovery, wrap rounds with the convergence governor _SYSTEM/Scripts/swarm-convergence.mjs (obligation floor + critical-signal block + adversarial "what's missing" pass + damping). DISARMED by default (YURI_SWARM_CONVERGENCE=1). Use when one fan-out round isn't provably complete.
z.ai GLM fleet — the second substrate
The native Claude fleet has a full twin, not a sidekick: a z.ai GLM lane fleet dispatched through _SYSTEM/Scripts/glm-fleet.mjs. Every GLM lane runs the complete YURI operator harness via llm-lane.mjs — read/grep/search/xrefandwrite_file/edit_file/bash — so it develops, designs, codes, writes files, and runs/self-verifies exactly like a native Agent. It is a peer build substrate — never read-only or advisory. glm-max (glm-5.2, 1M context) is an Opus-peer: on par or better at reasoning, coding, and visual design. It bills the z.ai plan (independent of the Anthropic quota), runs at --reasoning high (the GLM max-equivalent), and the same three lanes mirror onto it — Research, Code-generation, Testing — all doing real work.
The fullest mirror is ai claude-zai: a complete Claude Code session running on GLM (OPUS→glm-5.2, SONNET→glm-4.7, HAIKU→glm-flash) — the exact same setup as this one, on z.ai. Reach for it for a full interactive GLM build session; use glm-fleet.mjs to fan GLM peers out in parallel from here.
DISARMED by default → dry-run (prints the plan, spends nothing). Arm (owner-gated) via EITHER the session env YURI_GLM_FLEET=1 OR a persistent local flag touch _SYSTEM/state/glm-fleet.enabled (gitignored, reversible by rm). glm-fleet.mjs --list shows the current arm state.
GLM tier roster (mirrors Opus/Sonnet/Haiku):
Tier
alias
model
ctx
use
Opus-peer · orchestrator
glm-max
glm-5.2
1M
heavy reasoning, design, architecture, code, synthesis (premium)
Sonnet · workhorse
glm
glm-4.7
200K
code-gen, refactor, analysis, judgment
Haiku · fast build
glm-flash
glm-4.7-flash
200K
census, scan, fast edits, test-runs (free)
Haiku+ · concurrency
glm-flashx
glm-4.7-flashx
200K
wide cheap fan-out (paid)
Sub-orch overflow
glm-sub-orch
glm-5.1
200K
when glm-max is quota-gated
Reactive · voice
glm-turbo
glm-5-turbo
200K
sub-4s interactive
Vision
glm-vision
glm-4.6v
64K
screenshots, images (live-verified)
OCR
glm-ocr
glm-ocr
32K
document / PDF extraction (needs image payload)
Live-probe 2026-06-22: glm-max, glm, glm-flash, glm-turbo, glm-5.1, glm-vision verified live. glm-flashx (id accepted but empty output — likely not provisioned) and glm-ocr (needs an image payload) are UNVERIFIED via the text path — registered, not yet relied on.
Dispatch programmatically:
import { glmFleet } from'./glm-fleet.mjs'; // path relative to the importing file; the shell form below is location-independentconst { results, runDir } = awaitglmFleet([
{ lane: 'glm-max', label: 'DESIGN', prompt: `Use MAXIMUM reasoning depth. ROLE: design + architecture lane ... RETURN: design doc + RESULT_LABEL` },
{ lane: 'glm', label: 'CODEGEN', prompt: `Use MAXIMUM reasoning depth. ROLE: code-generation lane — write/edit the files directly and self-verify by running them ... RETURN: diff summary + RESULT_LABEL` },
{ lane: 'glm-flash', label: 'TEST', prompt: `Use MAXIMUM reasoning depth. ROLE: testing lane — run the suite, parse logs, report ... RETURN: results + RESULT_LABEL` },
], { concurrency: 3 }); // needs YURI_GLM_FLEET=1, else dry-run
Or from the shell (collect via --out — NEVER tee/>):
YURI_GLM_FLEET=1 node _SYSTEM/Scripts/glm-fleet.mjs --tasks '[{"lane":"glm-max","label":"ORCH","prompt":"..."}]' --concurrency 3
# results land in .claude/jobs/<run>/results/<label>.json · dry-run plan: drop YURI_GLM_FLEET or add --dry-run
ollama-cloud fleet — the third substrate
The third peer fleet: ollama-cloud lanes dispatched through _SYSTEM/Scripts/ollama-fleet.mjs — cross-family models (not Claude, not GLM) carrying the full YURI operator harness via llm-lane.mjs, so they build, code, write, and self-verify like any other lane. ONE base ollama-cloud lane fans out across the roster by --model <X>:cloud; the ollama Pro plan allows 3 concurrent. It bills the ollama plan (independent of both the Anthropic and z.ai quotas) and is the cheapest bulk fan-out — reach for it to spread wide, cheap, parallel work.
Roster (tier → model), all at --reasoning high:
flash → deepseek-v4-flash:cloud — default bulk, best quality-per-usage, blast freely
minimax → minimax-m3:cloud · kimi → kimi-k2.7-code:cloud — the efficient working trio
DISARMED by default → dry-run (prints the plan, spends nothing). Arm (owner-gated) via EITHER YURI_OLLAMA_FLEET=1 OR touch _SYSTEM/state/ollama-fleet.enabled (gitignored, reversible by rm). ollama-fleet.mjs --list shows the arm state + roster.
The key hydrates from the macOS keychain at the LANE layer (models.jsonkeychain_service: YURI_OS_MUSUBI:OLLAMA_API_KEY), so a spawned lane authenticates without the ai wrapper or an exported OLLAMA_API_KEY (root-cause fix 2026-06-23 — every caller benefits, not just the wrapper).
Multi-substrate routing — three equal build fleets
NOT a capability tier. All three fleets have the SAME powers — read, write, edit, bash, design, code, test, self-verify. GLM and ollama-cloud are peer extensions of Claude Code, never advisory or read-only. Route by operational fit, then spread work across ALL THREE for max throughput on independent quotas:
factor
native Claude Agents
z.ai GLM lanes
ollama-cloud lanes
build powers
read · write · edit · bash · test
identical
identical
quota pool
Claude / Sonnet weekly
z.ai plan
ollama Pro (independent — run all three at once)
recursion
FLAT (leaf-only)
recursive via nano-spawn, depth ≤ 5
FLAT (peer fan-out, 3 concurrent)
max context
per-model
glm-5.2 = 1M
1M input (deepseek-v4 family)
reasoning / coding tier
Opus / Sonnet / Haiku
glm-5.2 / glm-4.7 / glm-flash
nemotron / minimax / kimi / deepseek-v4
native MCP / browser / computer-use
yes
no
no
finalize (commit / push)
no — orchestrator only
no — orchestrator only
no — orchestrator only
The only hard native-only needs are MCP tools, browser, and computer-use. Everything else either fleet does equally. Default to spreading a large build across all three on their independent quotas, in parallel: native (Anthropic), GLM (z.ai), ollama-cloud (ollama Pro) — three quota pools = up to ~3× throughput. glm-max is a first-class orchestrator/red-teamer; the ollama lane is the cheap bulk muscle (deepseek-v4-flash for breadth, deepseek-v4-pro reserved for true-1M / hardest jobs).
Spawning a sub-orchestrator (Opus-equivalent fan-out)
A spawned orchestrator — a native general-purpose Agent OR a glm-max lane — can run this fleet model itself if handed the protocol. Inject FLEET_PROTOCOL_PREAMBLE (exported from glm-fleet.mjs) as the first block of its prompt; glm-5.2's 1M context also lets you paste this whole SKILL.md verbatim. That is how "the Opus and Opus-equivalent spawns sub-dispatch with this knowledge."
Honest caveat: native Claude subagents are flat — Explore has no Agent tool and the standing model keeps the native fleet leaf-only (Opus is the sole native spawner). Compensate with breadth (up to ~12 parallel; see Fleet sizing). Real recursion lives on the GLM substrate via the governed nano-spawn path (YURI_NANOSWARM_SPAWN=1, owner-gated) at depth ≤ 5. Until that is armed, "sub-orchestration" means a glm-max lane calling glm-fleet.mjs for its own peer round.
The governed autonomous loop
One owner input → a fully governed fleet:
Decompose — Opus splits into leaves, each tagged substrate native | glm | both; build the obligation ledger (swarm-convergence.buildObligationLedger).
Dispatch — native Agents (one message, parallel) + glmFleet(...) in parallel; each self-contained, max reasoning / --reasoning high.
Aggregate — read native returns + .claude/jobs/<run>/results/*.json into one pool.
Adversarial verify — swarm-convergence.runAdversarialPass with a glm-max runner ("what's missing"); Opus re-verifies load-bearing claims against local evidence.
Converge — converge(...); not converged → re-dispatch nextRoundWork, capped ≤ 3 rounds (damping force-stops on budget).
Finalize — Opus only: scoped-pathspec commit, push, irreversible/outward calls.
Arm the quality gate with YURI_SWARM_CONVERGENCE=1; the nano-barrier safety block (orphans/contested claims) is always on regardless. Both the convergence arm and the GLM-fleet arm are owner-gated.
Runnable now — _SYSTEM/Scripts/runSwarm.mjs (the audit's missing "orchestrator binary"). The GLM-substrate version of this loop is wired into one entry point: runSwarm({leaves:[{id,lane,prompt}]}, {rounds,concurrency}) runs decompose-ledger → glmFleet dispatch → aggregatePoolOutputs → runAdversarialPass (glm-max) → converge → re-dispatch only the gap leaves (obligation-floor failures and adversarial gaps; null-leaf gaps re-run all; ≤rounds) → finalizeGuard → manifest. CLI: node _SYSTEM/Scripts/runSwarm.mjs --leaves-file leaves.json --rounds 3. ARMED via the gitignored flags _SYSTEM/state/swarm-convergence.enabled + _SYSTEM/state/glm-fleet.enabled (env vars still work). Live-verified end-to-end (a 2-leaf armed run converged round 1; hermetic tests cover gap re-dispatch, floor-failure re-dispatch, and disarmed-no-spend). The NATIVE substrate stays Opus-orchestrated (Agent tool) and pairs with this at the top. The recursive (Level-B) nano-spawn/dispatchPool depth is wired but its deep-arm is deferred to hardening (the audit's livelock/lease seams).
Specialized roles — MURE (built 2026-06-22)
The roles seam is now realized as MURE (群れ) — a 20-role self-governing agent collective in _SYSTEM/config/fleet-roles.json + _SYSTEM/mure/. Each role = {id, archetype, capabilities, substrate, lane, autonomyClass, mathHooks, goalScope, independentOf}. Use it instead of hand-casting lanes when a task wants named roles:
node _SYSTEM/mure/mure.mjs --roster # the 20 roles, 6 groups
node _SYSTEM/mure/mure.mjs --demo # DISARMED plan of a sample task → roles → leaves (zero spend)
node _SYSTEM/mure/company.mjs --task-file t.json # cast a real task; armed (YURI_MURE_ARMED / _SYSTEM/state/mure.enabled) → live GLM dispatch
company.runCompany(task) casts each subtask to its best-matching role (capability match), gates every decision through the 6-gate self-governance charter (governance.mjs), and splits the work: GLM roles → runSwarm leaves (dispatched), native roles → Agent specs (for THIS Opus session to spawn). The math layer is wired into role decisions via math-bridge.mjs (decision-sim goal-scoring, quantum order-effect, energy veto, prediction Brier). DISARMED by default; arming is owner-gated; finalize stays Opus/owner. Full manual: _SYSTEM/mure/README.md.
Session Notes
2026-06-22 (v1.0.0, created): Built via the model itself — Sonnet research lane (overlap audit + discoverability mechanics: byte-0 frontmatter, skill-recall ranks description not triggers, skill-sync --sync publishes skills/→.claude/skills/), Opus synthesized + registered, Haiku testing lane verified discoverability. Corrections caught this session: Haiku WARN/BLOCK misguess (→ verify-agent-output rule), research found an ENOENT solo missed. Tools: Agent (Explore/sonnet, general-purpose/haiku), Write, Edit, Bash, skill-sync, skill-recall. Errors: none on build. Registered in skills/domain-index.json → 01-agent-assembly; alias commands/opus-fleet.md.
2026-06-22 (v1.1.0, dual-substrate): Added the z.ai GLM fleet as the second substrate. Built _SYSTEM/Scripts/glm-fleet.mjs (parallel GLM dispatcher on lane-dispatch.mjs retry + semaphore, DISARMED dry-run default, @capability: glm-fleet-dispatch, FLEET_PROTOCOL_PREAMBLE for sub-orchestrators). Wired GLM tier roster into models.json (+glm-4.7-flash/flashx, glm-4.6v, glm-ocr) + llm-lane.mjs aliases (glm-max=5.2, glm=4.7, glm-flash→4.7-flash remapped off 4.5-air, glm-sub-orch=5.1, glm-vision=4.6v, glm-ocr). Added 5 sections (GLM fleet, dual-substrate routing, sub-orchestrator injection, governed autonomous loop, roles seam) + 4 hard rules + 3 anti-rat rows. GLM reasoning ceiling = high. Verified: models.json valid, glm-flash→glm-4.7-flash resolves, glm-fleet --list/--smoke --dry-run clean. Built by Opus directly (integration-critical); research via 3 Sonnet Explore lanes (zai internals, governance inventory, online GLM benchmarks) + 1 Sonnet Plan lane (build design); HTML viz approved pre-build. Arming the loop stays owner-gated.
2026-06-23 (v1.2.0, third substrate): Added the ollama-cloud peer fleet — _SYSTEM/Scripts/ollama-fleet.mjs (mirrors glm-fleet on lane-dispatch.mjs retry + semaphore; ONE base ollama-cloud lane + --model <X>:cloud; roster tiers flash/minimax/kimi/nemotron/deepseek-pro/gemma; DISARMED dry-run default; @capability: ollama-fleet-dispatch) + ollama-fleet.test.mjs (9/9 red/grey/green). Root-cause RELIABILITY fix: the 3 ollama-cloud lanes in models.json lacked keychain_service, so llm-lane.mjs keychain hydration was skipped for any non-ai-wrapper caller → missing_key; added keychain_service: YURI_OS_MUSUBI:OLLAMA_API_KEY to all 3 (lane-layer fix → every caller benefits). Verified: armed 3-model LIVE smoke (flash 2s + minimax 9s + kimi 33s, all ok, key UNSET → keychain hydrated end-to-end at concurrency 3). Sections added: "ollama-cloud fleet" + "Multi-substrate routing" (dual→triple). Built by Opus directly. Arming stays owner-gated (flag NOT created; smoke via transient env only). Tools: Bash, Read, Edit, Write, capability-scan, llm-lane, keychain probes.
