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Agent Skills That Matter: Automate Verifiable Workflows End-to-End
Published:
If you use any coding agent in your day-to-day work, you’ve probably bought into the hype at this point: you’ve written your first AGENTS.md file, set up some slash commands, or maybe even built your first agent skill after retyping the same prompt for the N-th time.
Here’s the thing: almost all agent skills I’ve seen in the wild fall into a handful of recognizable patterns (e.g., the persona prompt), and most of those patterns don’t fully leverage agentic AI systems. They automate the production of output, often with minimal usage of the systems’ agency. Agent skills that matter automate the production of verified output, while scripting deterministic behaviours and exploiting AI agents’ agency for non-deterministic ones. This gap is exactly where real leverage lives.
publications
GitGoodBench: A Novel Benchmark for Evaluating Agentic Performance on Git
Published in REALM Workshop at ACL 2025 (Spotlight), 2025
A benchmark for evaluating AI agent performance on version control tasks, covering three core Git scenarios with datasets derived from open-source repositories.
Recommended citation: Lindenbauer, T., Bogomolov, E., & Zharov, Y. (2025). "GitGoodBench: A Novel Benchmark for Evaluating Agentic Performance on Git." Proceedings of the 1st Workshop for Research on Agent Language Models (REALM) at ACL 2025.
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From Knowledge to Noise: CTIM-Rover and the Pitfalls of Episodic Memory in Software Engineering Agents
Published in REALM Workshop at ACL 2025, 2025
An investigation into episodic memory in software engineering agents, showing that repository-level understanding is pivotal for patch localization and that naive memory augmentation can introduce noise.
Recommended citation: Lindenbauer, T., Groh, G., & Schütze, H. (2025). "From Knowledge to Noise: CTIM-Rover and the Pitfalls of Episodic Memory in Software Engineering Agents." Proceedings of the 1st Workshop for Research on Agent Language Models (REALM) at ACL 2025.
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The Complexity Trap: Simple Observation Masking Is as Efficient as LLM Summarization for Agent Context Management
Published in DL4C Workshop at NeurIPS 2025, 2025
We show that simple sliding window based observation masking matches the performance of LLM-based summarization for managing agent context, at a fraction of the computational cost.
Recommended citation: Lindenbauer, T., Slinko, I., Felder, L., Bogomolov, E., & Zharov, Y. (2025). "The Complexity Trap: Simple Observation Masking Is as Efficient as LLM Summarization for Agent Context Management." Deep Learning for Code Workshop (DL4C) at NeurIPS 2025.
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