ssl-skill-normalizer

SSL Skill Normalizer

Purpose

This skill converts markdown-based skill artifacts into a structured Scheduling-Structural-Logical (SSL) representation as introduced in:

Liang et al., "From Skill Text to Skill Structure: The Scheduling-Structural-Logical Representation for Agent Skills", arXiv:2604.24026 (2026).

SSL addresses the core limitation of free-form skill text: it is human-readable but hard for agents to reason over, discover, and audit. By mapping each skill into three complementary layers, SSL makes skills searchable (improved MRR 0.573 → 0.707 in the paper) and risk-assessable (improved macro F1 0.744 → 0.787).

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The Three SSL Layers

The representation is grounded in Schank & Abelson's theories of Memory Organization Packets (MOPs), Script Theory, and Conceptual Dependency. Each layer captures a different dimension of skill knowledge:

Layer 1 — Scheduling (When / Who)

Answers: When should this skill be invoked? By whom, given which inputs and outputs?

Fields extracted:

• id — stable lowercase identifier
• name — human-readable skill name
• goal — one-sentence purpose
• intent_signature — typed function signature (fn($input) -> $output)
• inputs — $-prefixed named input bindings
• outputs — $-prefixed named output bindings
• dependencies — explicit runtime tool or library requirements
• control_flow_features — e.g. sequential, conditional, loop
• entry_scene — ID of the first scene to execute
• subscene_refs — IDs of any nested/delegated scenes

Layer 2 — Structural (How / Order)

Answers: What are the macro-level execution stages and how do they connect?

Each scene is a named execution stage with:

• id — unique within the skill
• type — one of the restricted scene-type enum (see below)
• goal — what the scene accomplishes
• entry_condition — precondition for entering the scene
• exit_condition — postcondition that must hold on exit
• next_scene_rules — conditional transitions to the next scene ID, END_SUCCESS, or END_FAIL
• inputs / outputs — $-prefixed bindings consumed and produced
• entry_logic_step — ID of the first logic step in this scene