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). --- # 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 - `subsc...