rskill-playbook-decompose_mission
A
kind: playbook rSkill: a symbolic S2
decision procedure the
Reasoner reads, not a neural policy. It carries no weights — the authored
PLAYBOOK.md is its runtime.
What this skill does
Breaks a compound, multi-step instruction into an ordered list of subtasks, each
with its own verifiable done-condition (an internal TODO list), then executes and
verifies them in order. It decomposes the goal, records the subtasks to memory so
the plan survives a tick, dispatches the matching skill for each, verifies the
done-condition before advancing, and on a subtask failure replans
that subtask
only — escalating to a human if a subtask exhausts its replan budget. Concrete
walkthrough: the stack-bowls / drawer / cookie-box example in
PLAYBOOK.md.
How it works
This playbook is content, not code. When installed, the reasoner injects
PLAYBOOK.md into its system prompt and follows the SOP, composing tools it
already has (execute_rskill, query_scene, query_task_progress,
memory_write, memory_search, emit_prompt). It is role: s2 and is never
dispatched through ExecuteSkill. Every motion it triggers is an execute_rskill
→ Action chunk → C++ safety kernel — the playbook holds no actuation authority
(CLAUDE.md §1.1).
Observation → action contract
None. A playbook emits no Action chunks and requires no actuators
(actuators_required: [], chunk_size: 1). Its "output" is the sequence of
tool calls the reasoner makes while following the SOP, bounded by
playbook.max_steps.
How it was authored / Upstream provenance
N/A — a playbook is hand-authored, not trained: it has no weights and no
upstream model. Its provenance is the authoring decision record
(also linked via paper_url). To change behaviour, edit PLAYBOOK.md and bump
version.
Supported robots
Embodiment-agnostic — declares the explicit wildcard embodiment_tags: ["any"]
(never an empty list). Pure planning / orchestration, so capabilities_required
is empty ({}): it works on any robot. Each subtask it dispatches is gated at
runtime by the composed tools, not by this playbook's flags.
Sensors required
None directly. The tools it composes declare their own sensor needs.
Manifest summary
kind: playbook, role: s2, actions: [plan], chunk_size: 1.playbook.trigger: the goal is a compound, multi-step instruction.playbook.done_predicate: every subtask's verifiable goal has been confirmed met, or the mission has been handed off.playbook.max_steps: 24.
Quick start
1from openral_core.schemas import RSkillManifest
2
3m = RSkillManifest.from_yaml("rskills/decompose-mission/rskill.yaml")
4assert m.kind == "playbook" and m.playbook is not None
5print(m.playbook.trigger)
Reproduction
Packaging-only: the manifest + SOP are validated by
tests/unit/test_playbook_rskill_manifest.py. There is no benchmark number to
reproduce; the playbook's behaviour is exercised by the reasoner integration
tests in later phases.
Evaluation
N/A — no eval/*.json; a playbook produces no benchmarkable policy output.
License
- Code / content: Apache-2.0.
- Weights: none.
See also
PLAYBOOK.md — the decision procedure itself.
