From holyclaude-cloud
Decide whether to run a single legion task as a local Claude Code subagent or as a cloud Modal worker. Used by the orchestrator skill per task. Inputs are the task spec + current swarm load + heuristics from legion.toml. Output is "local" or "cloud" with a one-line reason.
How this skill is triggered — by the user, by Claude, or both
Slash command
/holyclaude-cloud:dispatcherThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Per-task router. Called once per task by the orchestrator's dispatch loop.
Per-task router. Called once per task by the orchestrator's dispatch loop.
{id, title, spec, deps, estimated_minutes, files_touched}{in_flight_local, in_flight_cloud, max_workers, throttle_active}legion.toml [dispatch]:
local_file_threshold (default 5)cloud_minutes_threshold (default 5)always_cloud_patterns (regex list)Apply in order. First match wins.
always_cloud_patterns matches the title → cloud. Reason: "matched pattern ".local. Reason: "throttle backoff".estimated_minutes ≤ 2 AND len(files_touched) ≤ 2 → local. Reason: "trivial task".cloud. Reason: "browser work; cloud has dedicated chromium".cloud. Reason: "throughput-bound".len(files_touched) < local_file_threshold AND estimated_minutes < cloud_minutes_threshold → local. Reason: "small enough for local".cloud. Reason: "default route".Return JSON: {"target": "local" | "cloud", "reason": "..."}.
If cloud:
modal run ~/holyclaude-cloud/modal/worker.py::run_task with the task fields.If local:
general-purpose) with the framed task prompt and the same constraints (work on a worktree branch, push when done).These rules are the prior. For Phase 1, don't call Opus — just apply the rules. Adding an LLM tiebreaker is a Phase 2 polish that will marginally improve routing at meaningful cost (one Opus call per task, on top of the per-worker cost). The rules cover ~95% of cases.
If you want to eyeball the routing before dispatch, the orchestrator already shows the task graph with assignments at the human checkpoint.
Provides behavioral guidelines to reduce common LLM coding mistakes, focusing on simplicity, surgical changes, assumption surfacing, and verifiable success criteria.
Searches, retrieves, and installs Agent Skills from prompts.chat registry using MCP tools like search_skills and get_skill. Activates for finding skills, browsing catalogs, or extending Claude.
npx claudepluginhub ajsai47/holyclaude-cloud