SkCC
ACM CAIS 2026 — AgentSkills'26 Workshop

Compilation for Skills:
Capable, Portable, and Securable

SkCC is a compiler for LLM agents. Author skills once as SKILL.md, compile to Claude Code, Codex, Gemini CLI & Kimi CLI — with built-in security hardening.

+13.5pp
Pass Rate Gain
<10ms
Compilation
94.8%
Security Coverage
4
Frameworks
SkCC Compiler
Claude Code — Terminal
📈 +12.2pp Pass Rate on Claude Code

One Skill, Four Frameworks,
Four Different Results

LLM agents are highly sensitive to prompt formatting — the same SKILL.md can vary up to 40% in performance across frameworks. Manual per-platform rewriting is unsustainable.

🔴 Without SkCC

  • Format-agnostic SKILL.md deployed identically everywhere
  • Up to 40% performance variation from format alone
  • 37% of community skills contain security vulnerabilities
  • O(m × n) manual adaptations for m skills × n frameworks

🟢 With SkCC

  • Author once, compile to framework-native formats
  • Consistent pass rate improvements across all targets
  • 94.8% Anti-Skill Injection coverage at compile time
  • O(m + n) — m skills + n Emitters, no manual rewrites

Adaptation Complexity

O(m × n)O(m + n)

Four-Phase Compilation Pipeline

Inspired by classical compiler design (LLVM, MLIR). A unified SkIR decouples skill semantics from framework-specific formatting.

Input
SKILL.md
Phase 1
🔍
Syntax Parser
YAML frontmatter
+ Markdown AST
Phase 2
🧠
IR Builder
Strongly-typed
SkIR
Phase 3
🛡️
Security Optimizer
Anti-Skill Injection
+ Validation
Phase 4
🚀
Target Emitter
Claude · Codex
Gemini · Kimi
Output
Platform Skills

Built for Production

🎯

Multi-Framework Emission

Compile to Claude Code (XML), Codex (XML-Markdown), Gemini CLI (Markdown+YAML), and Kimi CLI (Full Markdown) from a single source.

🛡️

Anti-Skill Injection

Automatic compile-time safety constraint injection. 4 rule categories covering HTTP, Loop, DB, and Parse safety — 94.8% trigger rate on real skills.

Sub-10ms Compilation

Rust-native, zero-copy parsing with Arc. Average 8.93ms per skill across 225 skills. Peak memory under 50MB.

📋

Progressive Disclosure

Routing manifest generation (~50 tokens/skill) for efficient agent initialization. Full skill content loaded only on semantic match.

🔒

Four-Tier Security Model

Low → Medium → High → Critical classification with graduated enforcement. HITL triggers for high-risk operations.

💰

Token & Time Savings

10–46% runtime token reduction and 23–43% execution time reduction across frameworks. Structured formats reduce trial-and-error.

Contributors

O

Yipeng Ouyang

Lead Author
ouyangyipeng.github.io
G

Yuhao Gu

Author
yhgu2000.github.io
X

Yi Xiao

Author
Z

Xianwei Zhang

Advisor & Corresponding Author
xianweiz.github.io