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milknado

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UpdatedJune 3, 2026 at 04:57

Mikado-method execution with milknado: dependency graphs, parallel ralph loops in worktrees, and code-review-graph context. Use when planning goals, running autonomous loops, or inspecting graph state.

Installation

Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.

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Source

paulnsorensen

paulnsorensen/milknado

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Related occupationsSOC

Based on SOC occupation classification

Software DevelopersComputer and Mathematical Occupations·SOC 15-1252

SKILL.md

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name	milknado
description	Mikado-method execution with milknado: dependency graphs, parallel ralph loops in worktrees, and code-review-graph context. Use when planning goals, running autonomous loops, or inspecting graph state.

Milknado

When to use

Decomposing a goal into ordered work (milknado plan)
Running ready leaves as parallel agent loops (milknado run)
Inspecting node status or adding prerequisites (milknado status, milknado add-node)

Configuration

milknado.toml: agent_family (claude | cursor | gemini | codex), plus optional planning_agent / execution_agent overrides.
Built-in family defaults use stdin for planning prompts and ralphify-compatible print-mode binaries for execution.

MCP

This plugin ships mcp.json registering the milknado-mcp stdio server (milknado_graph_summary, milknado_todo_add). Install the milknado package in the workspace (e.g. path dep in pyproject.toml) so uv run milknado-mcp resolves.

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milknado-config

paulnsorensen/milknado

Configure milknado's per-flavor worker execution by interviewing the user and writing milknado.toml — agent_family, [milknado.worker.tools] allowlists, and [milknado.flavor.*] tables for named flavor presets (implement, spec, spike, prototype, research). Use when the user wants to set up or tune how milknado runs workers — phrases like "configure milknado flavors", "set up milknado.toml", "tune the research/spec/spike agent", "which model should milknado use for prototypes", "skip quality gates for spikes", "milknado worker tools", "per-flavor agent config". Interviews for agent family, then per-flavor model/agent, tools, quality gates, and brief prepend; writes the tables and validates them with `milknado agents check` plus a flavor resolution probe. Do NOT use for running graphs (milknado run) or planning (milknado plan).

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Write, edit, refactor, or review Python with concise declarative contracts, idiomatic control flow, Sliced Bread boundaries, and the Python de-slop rules. Use for Python implementation work in this repository, especially when the user asks for Pythonic, succinct, LLM-optimized, de-slopped, Pydantic, dataclass, pattern-matching, or vertical-slice code.

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harvest

paulnsorensen/milknado

Harvest milknado execution state back into the hallouminate-wiki roadmap goal files. Use when the user says "harvest the roadmap", "export results to the wiki", "sync milknado back to the wiki", "update the goal outcomes", "write the outcomes back", or "/harvest". Calls milknado_roadmap_export to rewrite each goal's harvest block plus status/last_synced, leaving human-authored Intent/Acceptance byte-identical; a goal node milknado discovered mid-run gets a fresh goal file created for it, and `hallouminate index` is refreshed best-effort. Do NOT use to import a roadmap into milknado (that is /load-roadmap) or to edit Intent/Acceptance text.

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load-roadmap

paulnsorensen/milknado

Import a hallouminate-wiki roadmap into milknado so its goals become executable nodes. Use when the user says "load the roadmap", "import roadmap <slug>", "pull the roadmap into milknado", "seed milknado from the wiki", or "/load-roadmap". Calls milknado_roadmap_import to seed one roadmap node plus one goal node per goal file (deterministic wiki_ref keys, idempotent re-import, no task nodes), then optionally kicks milknado_plan_batches to decompose goals into tasks. Do NOT use to write execution results back to the wiki (that is /harvest) or to author a brand-new roadmap from scratch.

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