Unified Minions skill for both deterministic shell jobs and LLM subagent
orchestration. Replaces the older `gbrain-jobs` routing intent. Use when:
submitting gbrain jobs, shell/background tasks, spawning subagents,
checking progress, steering running work, pausing/resuming, parallel
fan-out. One durable, observable, steerable queue interface. Also carries
the durable-execution doctrine for any operation expected to exceed ~2
minutes: capability ladder, deadman checks that verify the result was
reported, and content-addressed stage checkpoints for expensive pipelines.
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name
minion-orchestrator
version
1.1.0
description
Unified Minions skill for both deterministic shell jobs and LLM subagent
orchestration. Replaces the older `gbrain-jobs` routing intent. Use when:
submitting gbrain jobs, shell/background tasks, spawning subagents,
checking progress, steering running work, pausing/resuming, parallel
fan-out. One durable, observable, steerable queue interface. Also carries
the durable-execution doctrine for any operation expected to exceed ~2
minutes: capability ladder, deadman checks that verify the result was
reported, and content-addressed stage checkpoints for expensive pipelines.
triggers
["gbrain jobs submit","submit a gbrain job","submit a shell job","shell job","run shell command in background","deterministic background task","spawn agent","background task","run in background","check on agent","agent progress","what's running","steer agent","change direction","tell the agent","pause agent","stop agent","resume agent","parallel tasks","fan out","do these in parallel","long operation","durable execution","arm a deadman","the job went silent","operation died in the background","make this pipeline resumable"]
Minions is a Postgres-native job queue for durable, observable background work.
This single skill handles two lanes:
Deterministic shell jobs (gbrain jobs submit shell ...)
LLM subagent jobs (gbrain agent run ...)
When to route to Minions: durable, observable work that must survive restarts,
fan out across many parallel tasks, or persist across sessions. Routing policy
is defined in skills/conventions/subagent-routing.md — the project default is
pain_triggered (native subagents first, Minions after specific pain signals
fire); Mode A (all-through-Minions) is opt-in.
Guarantees:
Jobs survive gateway restart (Postgres-backed)
Every job has structured progress, token accounting, and session transcripts
Running agents can be steered mid-flight via inbox messages
Jobs can be paused, resumed, or cancelled at any time
Parent-child DAGs with configurable failure policies
Durable-execution doctrine (routing convention the agent follows — nothing
mechanically enforces it; see "Durable execution" below):
Operations expected to exceed ~2 minutes route through the capability
ladder, never a bare background shell that dies with the session.
A deadman check verifies the result was REPORTED to the user, not merely
that the process exited — a swallowed completion event looks identical to
a dead job from the user's side.
The deadman is second-line insurance, not a delivery guarantee: it can
itself die before firing (scheduler restart, deleted entry). Backstop:
on the next turn, sweep gbrain jobs list --status active and recent
completions for overdue work whose result never reached the user.
Deadman checks are idempotent. A double-fire, or a fire that races the
normal completion report, produces silence — it checks reported-state
first and tags any recovery report with the job ID so duplicates are
detectable.
A checkpoint or progress file is trusted only with a freshness check.
A stale checkpoint reads as "still running" forever; compare its
timestamp against the expected progress interval and against
gbrain jobs get <id>.
Route the Request: Shell Job vs Subagent
Condition
Action
User asks for deterministic command/script run
Shell job (CLI: gbrain jobs submit shell ...)
User asks to "run in minions" + explicit command/argv
Shell job (CLI, --params with cmd or argv)
User asks for research/reasoning/iterative agent
Subagent job (CLI: gbrain agent run)
User asks to steer/pause/resume an agent
Subagent job lifecycle tools (MCP-callable)
Single simple operation under ~30s
Consider inline execution first
Needs restart durability/observability
Submit as Minion job
Operation expected to exceed ~2 minutes
Route through the Durable execution ladder (below)
Parallel work (2+ streams)
gbrain agent run --fanout-manifest or parent + child subagents
If intent is ambiguous, ask one clarification:
"Do you want a deterministic shell command job, or an LLM agent job?"
Shell Jobs (Deterministic Scripts)
Use for reproducible command execution, ETL steps, cron work, and scriptable
tasks where no LLM reasoning loop is needed.
Preconditions (read before submitting your first shell job)
GBRAIN_ALLOW_SHELL_JOBS=1 must be set on the worker environment.
Without it, the shell handler refuses to register and submissions sit in
waiting silently. Gate lives in src/core/minions/handlers/shell.ts.
Security: flipping GBRAIN_ALLOW_SHELL_JOBS=1 authorizes arbitrary
command execution on the worker. On a shared queue, this is a remote code
execution surface. Treat as privileged infrastructure authorization.
Execution mode — pick one:
Postgres + daemon:gbrain jobs work runs a persistent worker that
claims and executes jobs from the queue.
PGLite + --follow:gbrain jobs submit ... --follow runs inline.
The daemon mode is not available on PGLite (exclusive file lock). See
docs/guides/minions-shell-jobs.md.
MCP boundary: shell-job submission is CLI-only. submit_job name="shell"
over MCP throws an OperationError with code permission_denied ("'shell'
jobs cannot be submitted over MCP") because shell is in PROTECTED_JOB_NAMES.
Agents CAN observe shell jobs via get_job / list_jobs / get_job_progress
(not protected), but cannot submit them. Operator or autopilot submits;
agent observes.
Verify setup: after configuration, run gbrain jobs stats (CLI) to
confirm the worker is registered and consuming the queue.
Submit (CLI, operator or autopilot)
Shell jobs take their command via --params as a JSON object with cmd (string)
or argv (array), plus cwd and optional env.
Command string form:
gbrain jobs submit shell --params '{"cmd":"echo hello","cwd":"/abs/path"}'
Argv form (no shell expansion):
gbrain jobs submit shell --params '{"argv":["bash","-lc","echo hello"],"cwd":"/abs/path"}'
Inline execution on PGLite or any one-shot deployment:
gbrain jobs submit shell --params '{"cmd":"echo hello","cwd":"/tmp"}' --follow
Queue/lifecycle flags exposed by gbrain jobs submit --help: --queue,
--priority, --delay, --max-attempts, --max-stalled, --backoff-type,
--backoff-delay, --backoff-jitter, --timeout-ms, --idempotency-key,
--dry-run.
Monitor (agents or operator)
These operations are MCP-callable and safe for agent use:
list_jobs --name shell --status active
get_job ID
get_job_progress ID
Check structured result fields (exit code, stdout/stderr tails, attempts,
timings) from get_job. Use get_job_stats (MCP) or gbrain jobs stats
(CLI) for the worker/queue health dashboard incl. the wedged-queue signal.
Control (MCP-callable)
cancel_job id=ID
replay_job id=ID
replay_job is not protected — only shell submission is. Agents can
cancel or replay a shell job without CLI access.
Use idempotency keys for recurring shell workloads to avoid duplicate runs.
Subagent Jobs (LLM Orchestration)
Use for open-ended reasoning, tool-using research, and fan-out synthesis.
User-facing entrypoint:gbrain agent run <prompt> is the canonical way
to submit subagent work. It handles the elevated-trust plumbing — subagent
and subagent_aggregator are both in PROTECTED_JOB_NAMES, so direct MCP
submission requires {allowProtectedSubmit: true}, which gbrain agent run
supplies.
Phase 1: Submit
gbrain agent run "Research Acme Corp revenue" --tools "search,query"
--tools accepts a comma-separated subset of BRAIN_TOOL_ALLOWLIST (see
src/core/minions/tools/brain-allowlist.ts): query, search, get_page,
list_pages, file_list, file_url, get_backlinks, traverse_graph,
resolve_slugs, get_ingest_log, put_page. Anything outside the allow-list
is rejected at submit time with allowed_tools references unknown tool.
For parallel work with a fan-out manifest:
gbrain agent run --fanout-manifest companies.json
The manifest describes N children + 1 aggregator. Each child runs
name="subagent" under the hood; the aggregator runs name="subagent_aggregator"
and claims AFTER every child terminates. See
src/core/minions/handlers/subagent.ts and
src/core/minions/handlers/subagent-aggregator.ts.
Flags (from src/commands/agent.ts):
--subagent-def <name> — named subagent definition
--model <id> — override model
--max-turns <N> — cap the LLM loop
--tools <csv> — allow-listed brain tools (see above)
--timeout-ms <N> — hard timeout per job
--fanout-manifest <file> — N children + 1 aggregator
Queue/priority/retry tuning is not exposed by gbrain agent run; submit the
raw subagent handler via gbrain jobs submit (requires CLI trust) if you
need those knobs.
Admission control (v0.46.11.0). Identical parentless subagent submits
(same owner lane, payload, and execution options) coalesce onto the existing
waiting job: gbrain agent run prints coalesced with the matched job id,
and the submit_agent MCP response carries coalesced: true. Treat that as
success — monitor the matched id, do NOT resubmit. Jobs still waiting after
the TTL (48h default for subagent; minions.ttl_waiting_hours.<name>)
are cancelled with reason prefix waiting_ttl_expired. If an operator has
configured a waiting quota (minions.quota_max_waiting.<name>), a submit
past the cap returns a structured, retryable rate_limited error — back
off and check gbrain jobs stats for a DIVERGENT QUEUE line before
retrying.
Phase 2: Monitor
list_jobs --status active # MCP — what's running?
get_job ID # MCP — full details + logs + tokens
get_job_progress ID # MCP — structured progress snapshot
gbrain jobs stats # CLI — queue health dashboard
gbrain agent logs ID --follow # CLI — streaming transcript + heartbeat
Progress includes: step count, total steps, message, token usage, last tool called.
Phase 3: Steer
Send a message to redirect a running agent:
send_job_message id=ID payload={"directive":"focus on revenue, skip headcount"}
The agent handler reads inbox messages on each iteration and injects them as
context. Messages are acknowledged (read receipts tracked).
Only the parent job or admin can send messages (sender validation).
Phase 4: Lifecycle
pause_job id=ID # freeze without losing state
resume_job id=ID # pick up where it left off
cancel_job id=ID # hard stop
replay_job id=ID # re-run with same or modified params
replay_job id=ID data_overrides={"depth":"deep"} # replay with changes
All lifecycle ops are MCP-callable.
Phase 5: Review Results
get_job ID # result, token counts, transcript
Token accounting: every job tracks tokens_input, tokens_output, tokens_cache_read.
Child tokens roll up to parent automatically on completion.
Durable execution (operations >2 minutes)
Background shells die silently: session compaction, harness restart, tool
timeout. Long gbrain operations (extract all, embed --stale, a full
sync --all) routinely run 10-60 minutes — past every one of those
ceilings. And even when the work survives, the completion event can be
swallowed (worker restart, dropped notification), leaving the user staring
at silence while a finished result sits unreported. Durable execution
covers both halves: the work survives, and the report provably lands.
Route any operation expected to exceed ~2 minutes through the highest
rung of this ladder the deployment supports. This is a harness-routing
convention the agent follows, not a mechanical guarantee — nothing stops a
bare background shell except this skill saying don't.
Rung 1 — Minion job + deadman (Postgres + worker)
Requires: Postgres engine, a running gbrain jobs work worker, and — for
the shell lane — GBRAIN_ALLOW_SHELL_JOBS=1 on the worker. All the
Preconditions above still hold: the flag defaults OFF, shell submission is
CLI-only across the MCP trust boundary, and PGLite has no worker daemon
(see Rung 3). Nothing in this section loosens that contract.
Work that needs LLM judgment goes through the subagent lane
(gbrain agent run, above) instead — a submitted agent job you never
check on is the same bug as an unwatched shell job.
Arm a deadman in the same action block (pattern below). Submitting
without arming is the classic half-fix: the job survives, the silence
doesn't.
Rung 2 — cron-checked progress file (no agent-waking scheduler)
When no scheduler can wake the agent but the host has a plain crontab (or
the work runs outside the queue entirely): make the operation write a
progress/heartbeat file as it advances (or rely on get_job_progress for
queue jobs), and register a recurring host-cron check that compares the
file's freshness against the expected progress interval. The agent also
checks it at the start of the next turn. A checkpoint that stops advancing
means stalled, not "still running" — the freshness comparison is the
entire value of this rung.
Rung 3 — foreground + output buffering (PGLite / no worker / no scheduler)
Run the operation inline in the foreground — on PGLite that's
gbrain jobs submit ... --follow, or just the raw command — and buffer
all output to a file, reading bounded slices, per
skills/conventions/exec-output.md. An empty tool result after a long
command is truncation, not a dead shell. This rung has no silent-death
insurance, so keep the operation in the foreground and stay with it;
backgrounding here recreates the exact failure the ladder exists to
prevent.
The deadman pattern
A one-shot, self-deleting scheduled check that fires at
expected-finish-plus-margin and verifies the result was reported — not
just that the process exited. Arm it in the same action block as the
submission (not after, not "if I remember").
Estimate expected_minutes honestly; round up.
Deadline = now + estimate + margin, where margin = max(10 min, 50% of estimate). Restarts delay delivery — too tight false-fires, hours-late
defeats the point.
Create the check with whatever one-shot scheduler the host offers: a
harness cron tool with delete-after-run semantics, at, or a crontab
entry the check removes on first fire. The check's instruction:
Verify the result was already reported to the user. gbrain jobs get <id> gives the job state; the reported-check asks whether a
completion message actually reached the user.
Reported → do nothing, stay silent, self-delete.
Completed but unreported (swallowed event) → pull the result via
gbrain jobs get <id> and post the recovery report now, tagged with
the job ID.
Still running → post a one-line status with a new ETA and arm ONE
follow-up deadman at +50% of the original estimate. One follow-up
max — no infinite chains.
Dead/failed → report what it produced before dying (stderr_tail
from gbrain jobs get <id>) and offer gbrain jobs retry <id>.
Disarm on normal completion. When the completion arrives and you
report it, remove the deadman. If it fires anyway, the reported-check
makes it a silent no-op — belt and suspenders.
Failure modes the pattern must own (mirrored in Contract and
Anti-Patterns): the deadman itself dying before it fires (second-line
insurance, backstopped by the next-turn overdue sweep), double-fire
(idempotent reported-check + job-ID-tagged reports), and stale checkpoints
(freshness check before trusting "still running").
Eval contract, imported with the pattern — a deadman deployment is judged
on:
ARMED_ON_SPAWN — created in the same action block as the long
submission?
SELF_DELETING — one-shot, and silent when all is well?
DETECTS_SWALLOWED — verifies the result was reported, not just that
the job exited?
RECOVERS — on a swallowed event, pulls the output and posts the
recovery report?
Hard fails: a long user-facing operation with no deadman armed; a deadman
that posts noise when the completion arrived normally; emulating the timer
with sleep or a poll loop instead of a scheduler entry (a sleeping
process dies with the session — the exact failure being insured against).
Sequencing and locks
Maintenance operations share locks (sync, embed, extract,
integrity). Run them sequentially, chained: submit job 1, arm its
deadman; on completion, submit job 2, arm the next; finish with
gbrain doctor and report the health delta. If a job dies mid-operation
its lock expires at TTL (a live, recently-refreshed holder is protected by
the steal grace) — never hand-delete lock rows to "unstick" a queue.
Typical long operations
Durations scale with corpus size; treat these as order-of-magnitude
anchors for --timeout-ms, not promises.
Operation
Typical duration
Suggested --timeout-ms
extract all
30-60 min on large brains
3600000
embed --stale
5-30 min (scales with missing count)
1800000
sync --all
5-20 min
1200000
integrity auto
10-30 min
1800000
dream
5-15 min
900000
Appendix: content-addressed stage checkpoints
For a multi-stage pipeline with an expensive middle (extract → score →
explain → render → verify, where the scoring stage burns real LLM spend),
make each stage a content-addressed checkpoint so a crash — or a
deadman-triggered retry, or a replay_job — resumes instead of
re-spending:
Key each stage on a hash of (the stage's own logic + its params +
the hashes of its upstream artifacts). Store artifacts under a .cache/
directory in the pipeline's working tree, addressed by content hash.
Warm re-run is a no-op: every key matches, nothing recomputes, the
whole pipeline replays in well under a second.
Busting cascades correctly: a param change or a logic edit changes
that stage's key, recomputing it and every downstream stage whose
upstream hashes changed. The stage's own source must be part of the
key — omit it and logic edits silently reuse stale artifacts.
Record provenance: a provenance file per run records each stage's
key, inputs, outputs, and computed-at, so every artifact traces to the
exact logic + data that produced it and "which stage recomputed and
why" stays answerable.
Verify integrity on restore: check live artifacts against recorded
checksums; a mismatch means recompute or restore from cache, never
silent reuse.
Judged on: IDEMPOTENT (warm re-run recomputes nothing),
CORRECT_BUSTING (a change recomputes exactly the affected stages),
PROVENANCE (every artifact traces to logic + params + upstreams),
INTEGRITY (corrupted artifacts detected, never silently reused).
This composes with the ladder rather than replacing it: the ladder keeps
the pipeline running and reported; stage checkpoints keep a retry cheap.
Pair them whenever a single stage costs more than pocket change in LLM
spend.
Output Format
When reporting job status to the user:
Job #ID (name) — status
Progress: step/total — last action
Tokens: input_count in / output_count out (+ cache_read cached)
Runtime: Xs
Children: N pending, M completed
Don't spawn a Minion for a single search query (use search tool directly)
Don't fire-and-forget without checking results
Don't spawn > 5 concurrent agents without checking gbrain jobs stats first
Don't resubmit when a submit reports coalesced — the work is already queued; monitor the matched job id instead
For subagent work, don't use sessions_spawn with runtime: "subagent" when Minions is available (use gbrain agent run instead)
Don't poll get_job in a tight loop (use get_job_progress for lightweight checks)
Don't run an operation expected to exceed ~2 minutes as a bare background shell — it dies with the session; route through the Durable execution ladder
Don't say "I'll report back when it finishes" without an armed deadman or a scheduled check that will actually fire
Don't let a deadman post noise when the completion arrived normally — check reported-state first, stay silent, self-delete
Don't emulate the deadman timer with sleep or a poll loop — a sleeping process dies with the session, which is the exact failure being insured against
Don't trust a checkpoint or progress file without a freshness check — a stale checkpoint reads as "still running" forever
Don't run lock-acquiring maintenance ops (sync, embed, extract, integrity) simultaneously, and don't hand-delete lock rows to unstick them (locks expire at TTL)
Tools Used
Submit a background job — submit_job (MCP, non-protected names only; shell jobs are CLI-only, subagent jobs via gbrain agent run)
Get job details — get_job (MCP)
List jobs with filters — list_jobs (MCP)
Cancel a job — cancel_job (MCP)
Pause a job — pause_job (MCP)
Resume a paused job — resume_job (MCP)
Replay a completed/failed job — replay_job (MCP)
Send sidechannel message — send_job_message (MCP)
Get structured progress — get_job_progress (MCP)
Queue stats — get_job_stats (MCP; admin scope over HTTP, same as the other
jobs ops here — includes the wedged-queue silent-halt signal) or gbrain jobs stats (CLI)
