rskill-playbook-clarify_ambiguity

A kind: playbook rSkill: a symbolic S2 decision procedure the Reasoner reads, not a neural policy. It has no weights; the authored PLAYBOOK.md is its runtime.

What this skill does

Resolves an underspecified or ambiguous goal before acting. When the request admits more than one interpretation — two candidate bowls, a missing destination, an unsafe-to-guess choice — it disambiguates from spatial memory, then from the scene, and only then asks the operator a concise question; it never guesses on an irreversible action (placing, pouring, opening). Concrete walkthrough: the two-bowls 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 (query_scene, memory_search, recall_object, emit_prompt). It is role: s2 and is never dispatched through ExecuteRskill. It actuates nothing: its job is to gate the downstream execute_rskill → Action chunk → C++ safety kernel with a single unambiguous goal — 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: hand-authored, not trained, so no weights and no upstream model. Provenance is the authoring decision record (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) and an empty capabilities_required: {}: resolving an ambiguous reference is operator interaction plus memory/scene queries, so it works on any robot. The read-only scene/memory queries are 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 underspecified or ambiguous.
playbook.done_predicate: the goal has a single unambiguous interpretation, confirmed from memory/scene or by the operator.
playbook.max_steps: 5.

Quick start

from openral_core.schemas import RSkillManifest

m = RSkillManifest.from_yaml("rskills/clarify-ambiguity/rskill.yaml")
assert m.kind == "playbook" and m.playbook is not None
print(m.playbook.trigger)

Reproduction

Packaging-only: tests/unit/test_playbook_rskill_manifest.py validates the manifest + SOP. No benchmark number to reproduce; tests/unit/test_reasoner_playbooks.py covers the reasoner's ## PLAYBOOKS system-prompt injection.

Evaluation

N/A — no eval/*.json; a playbook produces no benchmarkable policy output.

License

Code / content: Apache-2.0.
Weights: none.

OpenRAL
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rskill-playbook-clarify_ambiguity