Results for “dispatch”

VNX-specific domain expertise for the intelligence system, central state databases, and dispatch lifecycle. Use when working on quality_intelligence.db / runtime_coordination.db, code_snippets FTS5, snippet_metadata linkage, success_patterns / antipatterns, T0 state projection (t0_state.json), intelligence_injections lifecycle, dispatch → receipt → events → archive flow, central state architecture, project_id propagation across VNX, or any code that reads/writes VNX governance state. Triggers include: editing intelligence injection code, querying central DB for cross-project insights, debugging "intelligence not appearing in T0 state", modifying schema in `schemas/quality_intelligence.sql` or `schemas/runtime_coordination_v*.sql`, working with `scripts/quality_db_init.py` or `scripts/lib/coordination_db.py`.

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Use at the start of any session on a project with a build graph (`.hedgehog/hedgehog.db`), and again at every claim/dispatch/verify cycle within it. Owns consuming the build graph — claiming task packets, delegating each to the layer's agent, running `hedgehog verify` to gate the commit, recording debt and decisions, the intent check at an intent's last layer, and when to clear context. Not for a build agent handed an already-claimed packet — that agent never claims, dispatches, or verifies; this skill is the orchestrating session's own.

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Parallel map-reduce bulk ingest for large source sets. Map phase dispatches read-only knowledge-extractor agents (≤N-wide, default Haiku) emitting per-source JSON extracts; Python routes extracts by target library file (fuzzy-merging new topics, pre-allocating new files, flagging oversized topics); reduce phase dispatches one agent-knowledge-updater per file (parallel, one writer per file); single shelf-index rebuild + one log.md entry. Resumable. Supersedes kb-ingest-batch.

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Orchestrate complex development with AUTOMATIC parallel subagent execution, continuous dispatch scheduling, dependency analysis, file conflict detection, and IOSM quality gates. Analyzes task dependencies, builds critical path, launches parallel background workers with lock management, monitors progress, auto-spawns from discoveries. Use for multi-file features, parallel implementation streams, automated task decomposition, brownfield refactoring, or when user mentions "parallel agents", "orchestrate", "swarm", "continuous dispatch", "automatic scheduling", "PRD", "quality gates", "decompose work", "Mixed/brownfield".

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Before any multi-step work with a stated goal — a feature, a bugfix spanning multiple files, a QA sweep, a doc draft + iterations, a multi-day investigation — create an ATO Mission instead of doing it via bare `ato dispatch` calls. A Mission persists the goal + the verifiable success criteria, lets the coordinator tick drive the work across days, captures every event in a structured audit trail (SQLite + markdown narrative), and integrates parallel agents' work via merge strategies. Complement to `ato-warroom` (the cross-family decision before you start) and `ato-review` (the post-code-diff review). Missions is where multi-step work LIVES; war-rooms are where decisions ABOUT it get made; reviews are where the resulting commits get vetted. Fires when: the work has more than one decision point, a verifiable end state, or runs across more than one session. Use it for any ATO development that doesn't fit in a single dispatch.

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Analyze Ascend NPU schedule, operator dispatch, operator launch, and Host Bound profiling issues in Ascend profiling data. Use when need to diagnose device Free time, framework/operator dispatch latency, launch latency, PYTORCH_API/CANN_API launch gaps, aclrtSynchronizeStream stalls, task queue behavior, CPU scheduling interference, GC/lock pauses, CPU affinity, or schedule-side optimization actions.

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The Arbor research-coordinator protocol: the OBSERVE -> IDEATE -> SELECT -> DISPATCH -> DECIDE cycle over a durable Idea Tree. Preloaded automatically on /auto-research sessions. The coordinator never edits code or runs commands — executors do that in isolated worktrees; the coordinator steers the tree with idea_tree / dispatch_experiments / merge_experiment.

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Dispatch to the right problem-solving technique based on how you're stuck

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Use when legacy prompts or older framework flows reference `dispatching-parallel-agents` and you need the modern runtime to resolve that capability cleanly.

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Use when hunting CI/CD bot comment command vulnerabilities where issue_comment or pull_request_review_comment triggers invoke privileged workflows without verifying the commenter's identity or authorization. Trigger on: "bot command injection", "issue_comment trigger", "@github-actions", "slash command CI", "CI bot command", "comment triggered workflow", "unauthenticated bot", "github-actions publish", "comment dispatch", no authorization check on workflow_dispatch from comment, chatops CI/CD, supply chain via PR comment.

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Defeats control-flow-flattening obfuscation by identifying the dispatcher/state- variable structure and reconstructing the original control flow so the logic becomes readable. Activates for requests to defeat control-flow flattening, deobfuscate an OLLVM-flattened function, or recover original control flow from a dispatcher loop.

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Use when a task needs the judgment of a Dispatcher — assigning a new job to the best available driver/technician in real time, managing an ETA slip and deciding who to notify, triaging a coverage gap in a service territory, sequencing a driver's remaining stops after a delay, or deciding whether a job needs to be reassigned versus the customer rescheduled.

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Use when a task needs the judgment of an airline pilot — checking whether an ETOPS diversion-time rating still holds against a current MEL downgrade, deciding whether to accept or query a dispatch release, applying sterile-cockpit discipline and Pilot Flying/Pilot Monitoring role separation on a two-pilot flight deck, resolving a captain-dispatcher operational-control disagreement, or checking Part 117 flight-duty-period legality against an irregular-operations delay.

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Run work as an explicit event-driven state machine executed by subagents. The orchestrator is the runtime from the canonical loop — `state, effects = init(machine); execute(effects); while state is active: event = nextEvent(); state, effects = transition(machine, state, event); execute(effects); return state.output`. All routing judgment is authored ONCE, at plan time, into a machine file (states, event alphabet, transition table, budgets, final states); at runtime the orchestrator does pure table lookups and dispatches effects — it never decides, never authors, never ingests artifacts. Subagent/sync protocol is the timeboxed-iterating one: role prompts scaffolded once to disk and dispatched BY PATH, an initialiser preamble every subagent reads first, a self-populating cheatsheet, per-unit files, tiny structured returns, independent measurement gate. Use when the user says "workmachine", "run this as a state machine", "state-machine this", or when a goal has a small number of clearly named phases with explicit outcomes (built / passed / failed / blocked) and you want the routing to be auditable rather than judged on the fly. For clock-driven loops use timeboxed-iterating; for wide graphs with judged edge criteria use workgraph.

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Audit a 911 dispatch or emergency response system for operational reliability and compliance. Triggers: building or reviewing CAD systems, PSAP software, dispatch platforms, or emergency operations center tools.

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Dispatch to the right problem-solving technique based on how you're stuck

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Cycle de coordination ai-01 (coordinateur UNIQUEMENT — jamais sur un worker). Lit memoire + dashboards + inbox + GitHub, merge les PRs pretes, tranche les design-gates, dispatche des grains par lane, reporte. Arguments: [--dispatch] [--focus <topic>]

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Judge board subskill for simmer. Dispatches a panel of judges with different lenses, runs one deliberation round where they challenge each other's scores, then synthesizes consensus scores + single ASI. Drop-in replacement for simmer-judge that produces identical output format. Do not invoke directly — dispatched by the simmer orchestrator when JUDGE_MODE is board.

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Run an org-team workflow — a flat pod of up to four peer Claude Code sessions claims lane-tagged slices of an approved spec over the baseline MCP channel and implements them concurrently, each in its own git worktree. Each peer DECIDES its own in-lane implementation choices in its own main context (Article X); only un-decidable or cross-lane forks escalate peer→lead→human (yield_fork for task-bound forks, ask_lead for free-form queries). Opt-in and OFF by default (velocity.org_mode.enabled). Graduates the retired sprint-dispatch prototype into the constitutionally-sanctioned org-team model (Article X — Multi-session coordinated workflows). Requires git.

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Turns a plan phase into a PhaseSpec v1 and dispatches it to a specialist subagent via the Agent tool. Subagent reads files itself, returns a structured result, the result is logged into progress.md. Markdown is the shared state; no in-memory shared store across agents.

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Use multiple Claude agents to investigate and fix independent problems concurrently

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Use when facing 2+ independent tasks — "in parallel", "independent tasks", "multiple unrelated failures", "different test files or subsystems" — that can be worked on without shared state or sequential dependencies. Dispatch one agent per independent slice, each with only the context it needs, even when the user did not ask for subagents explicitly.

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Use when acting as the orchestrator agent that drives Syntropic137 - running the dogfood loop, deciding what to dispatch next, landing agent PRs, deploying a beta, triaging failed executions, or keeping the scratchpad, paper cuts and retrospectives current. Trigger phrases include "orchestrator tick", "keep the loop running", "what should we dispatch next", "land these PRs", "why are PRs stacking up", "deploy the next beta", "pit stop", "the run failed, what now", "write the retro", "is the system helping", "scale quality", "learning loop for the orchestrator". Do NOT use for deciding whether one task belongs in a workspace or locally (see `dogfooding`), for turning one incident into an eval case (see `learning-loop`), for designing a workflow A/B experiment (see `workflow-experimentation`), or for release-branch and version-bump mechanics (see `devops`).

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Use when: 'do work', 'dispatch builder', or team lead triggers automated task implementation. Governs end-to-end flow from dispatch through verification, fail handling, batch mode, and escalation.

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