AIP: A Graph Representation for Learning and Governing Agent Skills

Abstract

Agent Skills today consist largely of free-form prose requiring the agent to read, interpret, and re-derive how to act in every session. This imposes two compounding costs: reduced reliability on implementation-heavy tasks, and difficulty in skill creation and improvement. Editing prose is a fragile process that both humans and agents struggle with, particularly for domain-specific procedural knowledge that is underrepresented in model training. The Agent Instruction Protocol (AIP) addresses both by modeling a skill as a directed execution graph with discrete steps as nodes backed by deterministic scripts or natural-language descriptions, connected by explicit typed input/output edges, all governed by a schema-validated YAML specification. A compiler meta-skill translates existing human-written skills into this form.
The benefits are twofold. Firstly, compiling human-written skills to AIP raised Claude Sonnet’s mean task reward from 0.60 to 0.71 and pass rate from 53% to 67% across 27 real agent tasks from SkillsBench—a statistically significant gain (Wilcoxon signed-rank 𝑝 = 0.011), winning 12 tasks to 2 with 13 ties. It is often faster since the graph delivers vetted, runnable units to the agent rather than asking it to re-derive code, commands, and tool calls from natural language. Secondly, on creation and improvement, because each skill is schema-validated, functionally testable, and addressable node-by-node, failures can be diagnosed and repaired precisely. Two authored-skill failures were traced to the script level. After adjusting the AIP spec and recompiling, both recovered with zero regressions (one task going from 0/5 to 5/5), turning skill improvement into a measurable tuning loop rather than a prose rewrite. That same graph structure supports corpus-level governance and skill introspection, and provides a natural action space for reinforcement learning over skills.