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SkillAlign: Aligning Skill Interfaces for LLM-based Agents

Shuo Ren, Xiaomian Kang, Jiajun Zhang

arXiv:2609.07255v1cs.AI

TL;DR

SkillAlign addresses the gap that skill utility may depend on exposure interface, not only on which skill is selected. It represents candidates through multiple procedural views and evaluates fixed-interface counterfactuals across benchmarks. Exposure form changes success and context cost, while adaptive exposure is learnable but remains far from oracle selection.

  • Problem

    Existing skill research often treats a selected skill’s interface as fixed, despite evidence that the same skill may help, distract, or mislead depending on its exposure.

  • Method

    SkillAlign converts provider-supplied candidate skills into multi-view procedural cards and renders them through alternative interfaces under controlled counterfactual evaluation.

  • Results

    Exposure form changes task success and context cost across ALFWorld and SkillsBench, with compact interfaces often achieving better cost–performance tradeoffs than full-library exposure.

  • Takeaways & Limitations

    Skill-augmented agents should optimize not only which skills to use, but also how those skills are presented.

  • Takeaways & Limitations

    The manually designed interfaces cover only part of the possible representation space, and current SFT/DPO exposure policies remain far from oracle exposure.

Abstract

from arXiv · show

Language-model agents increasingly rely on skills: reusable procedural knowledge for reasoning, tool use, and interaction. Existing work studies how skills are acquired, retrieved, compressed, or composed, but often assumes that once a skill is selected, its interface to the agent is fixed. We argue that this overlooks a key source of skill utility: the same skill can help, distract, or mislead depending on how it is exposed. We propose SkillAlign, a provider-agnostic framework that represents candidate skills as multi-view procedural cards and renders them through alternative exposure interfaces, including full instructions, hints, compressed summaries, workflows, or no exposure. This enables counterfactual evaluation where the task, agent, and candidate skills are fixed while only the exposure interface varies. Across ALFWorld and SkillsBench, we show that exposure form substantially affects task success and rendered context cost, and that compact top-k exposure can outperform full-library injection. We further conduct a replay-based policy-learning analysis on ALFWorld, showing that adaptive exposure contains learnable signal but remains far from oracle selection. Our results suggest that skill-augmented agents should optimize not only which skills to use, but also how those skills are presented.

1 Introduction

SkillAlign identifies skill exposure as a distinct design problem: the same candidate skill may affect an agent differently depending on how it is presented. It evaluates alternative interfaces under controlled conditions and finds that exposure form changes performance and cost.

  • Results: Across ALFWorld and SkillsBench, exposure form changes task success and context cost even when the same skills are selected.The evaluation uses multiple skill providers and agent backends, comparing fixed exposure modes with learned exposure policies.
  • Results: Concise exposure can match or approach full-document injection in some settings, while learned selectors show headroom over any single fixed interface.These findings support treating exposure form as a first-class factor in skill-augmented performance.
  • Motivation: SkillAlign treats skill-interface alignment as a missing problem alongside skill writing, acquisition, retrieval, compression, composition, and updating.Its central claim is that skill utility depends on both relevance or quality and the interface through which the skill conditions the agent.
  • Approach: The framework represents candidate skills as multi-view procedural cards and renders them as full instructions, hints, compressed summaries, workflows, or no exposure.This separates skill selection by upstream providers from the decision of how selected skills condition the agent.
  • Approach: Counterfactual evaluation holds the task, agent backend, and candidate skills fixed while varying only the exposure interface.The protocol measures interface-induced changes in task success and rendered skill-context cost.

2 Related Work

Prior work studies how skills are represented, acquired, retrieved, and optimized, while SkillAlign focuses on how already available skills should condition an agent. Its interface decision follows candidate provision and precedes evaluation in the agent’s interaction loop.

  • Skill representations: Skills package reusable procedural know-how that coordinates tools, memory, and runtime context under task-specific constraints.This distinguishes skills from ordinary tools, which expose operations without necessarily specifying when and how to use them.
  • Skill representations: Existing systems represent skills as text-based instructions, code-backed routines, or hybrid artifacts.These forms include reasoning templates, workflow memories, executable scripts, API calls, and structured runtime support.
  • SkillAlign’s focus: SkillAlign instead studies which interface should expose a fixed set of candidate skills to the agent.It renders candidates as full instructions, hints, compressed summaries, workflows, or no exposure, then evaluates the resulting interaction trajectory.
  • Acquisition and retrieval: Skill research also addresses acquisition from trajectories, task requirements, external corpora, or lifelong evolution.These approaches concern where skills come from and how they are improved before they are made available to an agent.
  • Acquisition and retrieval: Retrieval and routing methods select skills using dense or sparse matching, generative identifiers, or structure such as hierarchies, dependencies, and graphs.Other systems optimize the skill artifact itself, including methods for reducing or compiling verbose skills.

3 Method

SkillAlign treats skill exposure as a controllable interface decision rather than a fixed consequence of retrieval. It represents candidate skills with multiple procedural views, renders them through selected interfaces, and evaluates their effects using counterfactual trajectories.

  • 3.1 Skill Exposure as an Interface Decision: SkillAlign takes candidate skills as given and separates upstream skill selection from the choice of how those skills condition the agent.Candidates may come from exhaustive provision, vector retrieval, graph-based retrieval, or oracle annotations; retrieval is not optimized by the framework.
  • 3.1 Skill Exposure as an Interface Decision: The framework renders candidate skills through full, hint, compressed, workflow, or no-exposure interfaces, each providing a different conditioning structure.Full preserves the original document; hint gives a concise cue; compressed captures affordances, assumptions, constraints, and risks; workflow emphasizes ordered guidance.
  • 3.2 Multi-View Skill Cards and Rendering: Each heterogeneous raw skill becomes a multi-view procedural card that preserves the artifact while exposing alternative task-facing views and metadata.The views include original content, concise cues, compact affordance-and-risk summaries, and ordered workflows.
  • 3.3 Fixed Exposure Modes and Counterfactual Evaluation: Fixed exposure modes hold the task, agent backend, and candidate skills constant while varying only the interface, isolating exposure-form effects on trajectories.The resulting runs measure task success or reward, injected context cost, and negative transfer across interfaces and candidate sources.
  • 3.4 Exploratory Exposure Policy Learning: SkillAlign also explores learning exposure choices from counterfactual outcomes, using utility-based labels or preferences to predict one interface for each candidate set.This exploratory component estimates whether interface selection can improve beyond fixed modes.
  • 3.4 Exploratory Exposure Policy Learning: The exploratory policy-learning component is deliberately basic and lightweight, serving to test feasibility rather than fully solve exposure optimization.Experiments use lightweight LoRA adapters on Qwen3-4B, with SFT and DPO evaluating alternative learning strategies.

4 Experiments

Across ALFWorld and SkillsBench, exposure interface changes task performance and rendered context cost even when candidate skills are fixed. Compact, aligned interfaces often offer strong performance at lower cost, while adaptive exposure shows learnable but incomplete gains over fixed choices.

  • Main Results: Exposure-interface changes produce substantial performance differences under identical candidate sources, tasks, and agent backends.Under all, ALFWorld success ranges from 47.9 to 72.1 for MiniMax and 70.7 to 82.1 for GLM; under vector-topk, MiniMax ranges from 64.3 to 75.0 on ALFWorld and GPT-Codex from 28.9 to 32.2 on SkillsBench.
  • Main Results: Candidate filtering and exposure design are complementary: retrieval reduces irrelevant skill mass, while SkillAlign controls how remaining skills condition the agent.All + Compressed still aggregates compressed information from all 1,000 skills, whereas Vector-topk reduces the candidate set before exposure.
  • Main Results: Compact interfaces often achieve strong reward at much lower context cost than full exposure.Vector-topk + Hint and Vector-topk + Compressed rank highly in multiple columns while remaining below 1K token-equivalent units for compact views.
  • Main Results: Full exposure is a diagnostic stress condition because its degradation may combine truncation, attention dilution, irrelevant priors, and instruction conflict.The paper therefore emphasizes Vector-topk and Graph-topk comparisons, where candidate skills are already filtered.
  • Structured Provider: Graph-topk + Compressed is generally stronger than other graph-provider interfaces, while Graph-topk + Full is more costly and not consistently better.Structured candidate selection does not eliminate the need for an aligned exposure interface.
  • Scaling: At skills_1000, full exposure drops to 47.9 while compact exposure reaches 72.1, and performance drops again at skills_2000 as the skill pool becomes noisier.Compact exposure is slightly lower at skills_500 than skills_1000, but remains more stable as libraries grow.
  • Adaptive Exposure: Replay-trained policies improve over the best fixed interface, but the large oracle gap and label-distribution mismatch show that current policy learning remains preliminary.DPO improves over SFT, providing preliminary evidence that preference supervision captures additional exposure-selection signal.

5 Conclusion

SkillAlign reframes skills as externalized procedural priors whose usefulness depends on how they condition LLM agents. Across ALFWorld and SkillsBench, exposure form affects performance, and the paper argues that systems should optimize skill presentation alongside skill acquisition and retrieval.

  • SkillAlign studies and improves how skills are exposed to LLM agents rather than treating them as static prompt snippets.
  • The framework reframes raw skills through full descriptions, hints, compressed summaries, and workflow-style guidance.
  • Exposure form is a first-class factor across ALFWorld and SkillsBench.
  • Full-library exposure can be harmful, while compact interfaces often provide better cost–performance tradeoffs.
  • Exposure effects persist across candidate providers, while the best interface varies across tasks, backends, and skill sources.
  • Future skill-agent systems should optimize how skills are presented in addition to skill acquisition and retrieval.

Limitations

The paper identifies limitations in its manually designed exposure interfaces, simple exposure-policy learning, and reliance on candidate-skill quality. These boundaries leave richer representations, stronger adaptive selection, and joint retrieval–exposure optimization for future work.

  • SkillAlign uses a small set of manually designed exposure interfaces that does not cover the full design space of skill representations.
  • Future work could learn richer interfaces or generate task-adaptive views directly.
  • The SFT and DPO exposure policies are feasibility probes, and their large oracle gap shows adaptive exposure remains far from solved.
  • SkillAlign complements rather than replaces retrieval, so poor candidate retrieval can limit even a well-aligned exposure interface.
  • Skill-agent systems should jointly consider retrieval quality, exposure form, and downstream agent behavior.

Use of AI Assistants

The authors used AI assistants only for limited editing, grammar checking, and code-debugging assistance. They report that the research ideas, methods, experiments, analysis, and manuscript writing remained author-conducted and reviewed.

  • AI assistants supported limited language editing, grammar checking, and code-debugging assistance.
  • The authors conducted and reviewed the research design, experiments, analysis, and final manuscript.

A Implementation and Evaluation Details

SkillAlign is a provider-agnostic exposure layer that renders candidate skills into analyzed skill contexts and supports counterfactual interface comparisons. Its implementation separates candidate provision from exposure selection and records inputs, interfaces, costs, outcomes, and metadata across evaluation settings.

  • Implementation: SkillAlign receives task context, agent context, and candidate skills, then renders the candidates before prompt insertion.
  • Implementation: The provider-agnostic design keeps exposure independent of benchmark runners and retrieval systems.
  • Implementation: Rendered skill context includes selected skill ids, exposure interface, rendered length, and decision records for counterfactual analysis.
  • Evaluation settings: Experiments distinguish none, all, vector-topk, and Graph-topk candidate sources to separate exposure effects from candidate provision.
  • Evaluation settings: Provider identity is logged separately from exposure interface to distinguish poor candidate selection from inappropriate exposure.
  • Evaluation settings: Each run records task, provider, selected skills, interface, context length, reward or success, steps, backend, and metadata.
  • Skill cards and interfaces: SkillAlign cards store multiple views of one skill for different cognitive roles rather than merely different lengths.
  • Skill cards and interfaces: The full interface renders raw_text, preserving execution-critical scripts, arguments, assumptions, examples, APIs, and configuration details.

C.1 Uncertainty Analysis

Task-level bootstrap resampling preserves the main qualitative patterns, but overlapping intervals caution against over-interpreting small numerical differences. A length-matched comparison further indicates that structured compact exposure can outperform generic summaries, while the best interface remains setting-dependent.

  • Uncertainty Analysis: 10,000 task-level bootstrap resamples produce percentile-based 95% confidence intervals for ALFWorld success rates and SkillsBench average rewards.The intervals quantify uncertainty across evaluation tasks rather than backend run-to-run stochasticity.
  • Uncertainty Analysis: Overlapping confidence intervals for closely spaced results mean that small numerical differences should not be over-interpreted.The main qualitative patterns of the point estimates remain preserved under task resampling.
  • Length-Matched Exposure: At comparable compact context lengths, Compressed generally outperforms Generic Summary across providers, backends, and benchmarks, although Full remains stronger in some settings.The comparison fixes candidate skill ids and ordering and evaluates both interfaces across the full ALFWorld and SkillsBench sets.
  • Length-Matched Exposure: The results indicate that context shortening explains part, but not all, of compact exposure’s benefit.The structured Compressed interface is compared against a generic free-form summarization baseline at comparable context lengths.
  • Skill Interfaces: Skill cards preserve structured procedural views, including routing summaries, compressed specifications, workflows, preconditions, failure modes, and keywords.Quality rules preserve operational details such as dependencies, validation commands, state checks, side effects, and stopping conditions when present.
  • Adaptive Exposure: The learned exposure policy selects an interface for provider-supplied candidate skills rather than choosing which skills to retrieve.Available modes include none, full, hint, compressed, and workflow; the policy receives the selected candidate cards and predicts one interface mode.

E.3 Replay Evaluation

Replay evaluation tests the learned exposure policy by looking up fixed-interface outcomes rather than rerunning environments. The analysis is therefore cheaper than fresh interaction but should be interpreted as policy-learning analysis rather than a fully optimized deployment setting.

  • Replay Evaluation: The evaluator uses off-policy replay to look up the fixed-interface trajectory result for each learned interface prediction on ALFWorld.This avoids rerunning every environment episode and is reported as replay evaluation rather than fresh online interaction.
  • Replay Evaluation: The learned-policy analysis reports both reward and interface-selection distribution and compares learned policies against oracle exposure.The comparison estimates remaining headroom for adaptive interface selection.
  • Replay Evaluation: The replay-based results should be interpreted as policy-learning analysis rather than as a fully optimized deployment setting.The evaluation uses cached fixed-interface outcomes instead of fresh online interaction.

E.4 Training Details

The learned exposure-policy model is trained separately from the main agent backends and predicts one exposure interface for each selected candidate skill set. Training uses LoRA adapters, while the main evaluation cost comes from fixed-interface counterfactual experiments across providers, interfaces, benchmarks, and backends.

  • Policy Training: The exposure-policy model receives task descriptions, provider metadata, candidate skill-card content, and interface definitions, then predicts one exposure interface.It is used only for the replay-based exposure-selection analysis and is trained separately from the main agent backends.
  • Policy Training: LoRA adapters are used for the learned exposure-policy model rather than full model training.The training settings are reported separately from the main agent evaluation configuration.
  • Compute and Evaluation: The main evaluation cost comes from fixed-interface counterfactual experiments across candidate providers, exposure interfaces, benchmarks, and agent backends.SkillsBench is more expensive than ALFWorld because it requires containerized task execution.
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