Technical Summary: SKILLREVISE

Problem Statement

Large Language Model (LLM) agents increasingly operate in verifier-driven environments where success depends on executing multi-step workflows, satisfying constraints, and recovering from failures. While "agent skills" (reusable procedural artifacts) are designed to bridge the gap between atomic tools and high-level prompts, existing methods for acquiring these skills face significant limitations, particularly in cold-start settings where only an initial, imperfect skill is available.

Current approaches fall into three categories, each with distinct drawbacks:

1. Retrieval-based methods: Often select skills that do not fully match the target task, leading to disrupted execution.
2. Self-evolution methods: Require accumulated trajectories and experience, making them ineffective when starting from scratch (cold-start) or prone to overfitting.
3. Direct authoring (Expert or One-shot LLM): Expert authoring is costly and may misalign with actual agent execution behaviors. One-shot LLM generation often produces skills that are syntactically correct but behaviorally weak, lacking validation against execution traces.

The core challenge is that skills do not improve agent performance as reliably as atomic tools; low-quality or mismatched skills can degrade performance. There is a need for a method to iteratively refine an initial, imperfect LLM-authored skill using execution evidence without requiring a large pre-existing corpus of successful trajectories.

Methodology: SKILLREVISE

The authors propose SKILLREVISE, an execution-grounded framework designed to iteratively refine an initial skill ($S_0$) through a bounded revision loop. The framework decomposes the revision process into task-specific diagnosis and general repair knowledge, avoiding the need for extensive prior experience.

Core Components

1. Diagnosis ($D_i$): A task-specific component that converts raw execution evidence into repair constraints. For each revision attempt $i$, it consists of:

   • Verification Specification ($V_i$): Observable requirements (output paths, schemas, formats, pass/fail assertions).
   • Failure Attribution ($A_i$): A summary of failed checks, observed behavior, probable causes, and defect labels.
   • Preservation Constraints ($K_i$): Records of checks that passed and choices that should be preserved to avoid regression.

2. Principle Memory ($M$): A repository of reusable repair principles rather than task-specific solutions. Each principle abstracts a recurring skill-design defect into an operational repair pattern, specifying when to apply it, what defect it addresses, and how to verify the repair. The system initializes with a set of seven principles (e.g., "Expose outputs to verifier," "Preserve correct behavior").

3. Revision Operator ($R_\phi$): This module maps the current skill ($S_i$), diagnosis ($D_i$), and retrieved principles ($P_i$) to a revised skill candidate ($\hat{S}_{i+1}$) and a revision trace ($z_i$). Crucially, it applies execution anchors—specific instructions on how a textual edit should change the executor's behavior (e.g., "reload the file," "parse JSON," "check keys")—ensuring edits are grounded in actionable steps.

4. Bounded Revision Episode: The framework operates under a finite budget (e.g., $B=3$ rounds).

   • Execution & Diagnosis: The current skill is executed; if failures occur, a diagnosis is generated.
   • Retrieval & Binding: Relevant principles are retrieved from $M$ and bound to the current diagnosis.
   • Revision & Re-execution: A candidate skill is generated and re-executed.
   • Utility-Gated Selection: The system maintains a set of observed candidates. The final output is not necessarily the last generated revision, but the best observed skill ($S^*$) based on a utility function $U(S, T)$. This function balances success rate, execution efficiency, transfer performance, and interference costs, ensuring that efficiency gains are only rewarded if the task is successfully completed.

Key Contributions

The paper makes three primary contributions:

1. Formulation: It reframes cold-start skill improvement as a bounded, execution-grounded revision of an existing LLM-authored skill, distinct from retrieval or open-ended self-evolution.
2. Framework: It introduces SKILLREVISE, which integrates task-specific Diagnosis, reusable Principle Memory, and execution-anchored revision with utility-gated selection to systematically retain the optimal skill version.
3. Evaluation: It provides extensive empirical validation across three benchmarks (SkillsBench, SkillLearnBench-Random, SWE-Skills-Bench-Hard) and an ALFWorld study, demonstrating consistent gains over no-skill and one-shot baselines across multiple executor models.

Experimental Results

The framework was evaluated using a shared verifier-driven harness across five LLMs (GPT-5.5, Opus-4.7, Kimi-2.6, Qwen-3.6plus, DeepSeek-V4-Pro).

• Performance Gains: On the standard SkillsBench, SKILLREVISE improved the GPT-5.5 agent's success rate from 36.05% (no skill) and 39.53% (one-shot generation) to 61.63% within just three revision rounds. Similar substantial improvements were observed on SkillLearnBench-Random and SWE-Skills-Bench-Hard.
• Cross-Model Transferability: Revised skills exhibited strong transferability. When fixed skills revised by GPT-5.5 were applied to other executors (e.g., Opus-4.7, DeepSeek-V4-Pro), they often outperformed the "no-skill" baseline, suggesting the skills capture generalized procedural knowledge rather than model-specific artifacts. However, executor-specific revision generally yielded the highest performance.
• Ablation Studies: Removing the Diagnosis component caused the largest performance drop (from 53/86 to 28/86 on SkillsBench), highlighting the necessity of explicitly bridging verifier evidence to skill-level defects. The Preserve Ledger and Execution Anchors were also critical for preventing regressions and ensuring edits induced concrete actions.
• ALFWorld Study: In a principle-absorption study, a compact bank of 10 principles learned from 25 calibration tasks improved performance on a 100-task held-out set, solving 71/100 tasks, demonstrating the ability to abstract local failures into reusable repair patterns.

Significance and Claims

The paper claims that execution-grounded revision is a reliable paradigm for automated skill authoring, particularly in cold-start scenarios. The significance of the work lies in demonstrating that:

• Skills are most effective when they are not static advice but testable procedural memories refined by observed failures.
• A structured approach combining diagnosis, general principles, and utility-gated selection can overcome the brittleness of one-shot generation and the data-hunger of self-evolution.
• The framework successfully bridges the gap between expert authoring (costly) and one-shot generation (unreliable) by iteratively refining an initial draft using verifier feedback.

The authors modestly note limitations, including dependence on verifier-visible feedback (which may lead to overfitting if tests are sparse) and the fact that the method does not fall back to "no-skill" execution if a skill consistently degrades performance. They position SKILLREVISE as a step toward robust, automated skill acquisition that aligns agent behavior with task constraints and verification criteria.