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arxiv: 2606.01139 · v3 · pith:FFIQC3QGnew · submitted 2026-05-31 · 💻 cs.AI

SkillRevise: Improving LLM-Authored Agent Skills via Trace-Conditioned Skill Revision

Yuxuan Liu , Zhaochen Su , Lingyun Xie , Yuhao Zhang , Qing Zong , Jiahe Guo , Zhongwei Xie , Yiyan Ji
show 6 more authors
Yauwai Yim Hongyu Luo Xiyu Ren Ruan Chenyu Haoran Li Yangqiu Song
This is my paper

Pith reviewed 2026-06-28 17:35 UTC · model grok-4.3

classification 💻 cs.AI
keywords LLM agentsskill revisionexecution tracesprocedural skillsagent self-improvementskill transfer
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The pith

SkillRevise refines initial LLM agent skills by diagnosing defects in execution traces and applying targeted repairs from stored principles.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces SkillRevise to handle the common case where only an imperfect skill is available at the start. It shows that execution traces can reveal specific flaws, which are then matched to repair principles and edited in place. The process repeats until a skill passes verification or the budget runs out. This approach matters because one-shot generation often yields skills that look correct but fail in practice, while expert writing is expensive. If the method works, agents can start from cheap initial skills and reach higher performance without manual redesign.

Core claim

SkillRevise diagnoses skill defects from execution evidence, retrieves relevant repair principles from a general memory, and applies execution-anchored edits. It retains the first verifier-passing skill within the revision budget and falls back to empirical utility only when no candidate succeeds.

What carries the argument

Trace-conditioned revision loop that extracts defects from execution traces, retrieves repair principles, and produces edited skill candidates for re-testing.

If this is right

  • Base agent success rate on SkillsBench rises from 36.05% to 61.63%.
  • Revised skills transfer to different executors and task environments.
  • The method outperforms one-shot baselines across three benchmarks and five LLMs.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Skill libraries could be built incrementally from cheap initial generations rather than expert authoring.
  • The separation of diagnostic traces from executor-specific code may allow skills to be reused in new agent architectures.
  • If repair principles prove general, the same memory could support revision in domains beyond the evaluated benchmarks.

Load-bearing premise

Execution traces contain enough diagnostic information to identify specific skill defects and that retrieved repair principles can be applied to produce edits that reliably improve verifier passage rates within the revision budget.

What would settle it

Running SkillRevise on SkillsBench yields no increase in base agent success rate above the 36.05% one-shot baseline across the tested LLMs.

Figures

Figures reproduced from arXiv: 2606.01139 by Haoran Li, Hongyu Luo, Jiahe Guo, Lingyun Xie, Qing Zong, Ruan Chenyu, Xiyu Ren, Yangqiu Song, Yauwai Yim, Yiyan Ji, Yuhao Zhang, Yuxuan Liu, Zhaochen Su, Zhongwei Xie.

Figure 1
Figure 1. Figure 1: Skill design must avoid both instance-specific [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: SKILLREVISE pipeline. Solid arrows show one bounded execution-grounded revision episode: execute the current skill, diagnose evidence, retrieve and bind active principles, generate an anchored candidate, re-execute it, and retain the first verifier-passing skill, with utility fallback only if no candidate succeeds. The dashed arrow denotes optional post-evaluation memory absorption. The trace zi documents … view at source ↗
Figure 3
Figure 3. Figure 3: Cross-model transfer on the 57-task GPT [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Per-task verifier outcome heatmap for GPT-5.5 across methods on SkillsBench. Rows are grouped by [PITH_FULL_IMAGE:figures/full_fig_p020_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Per-task verifier outcome heatmap for Opus-4.7 across methods on SkillLearnBench-Random. Columns [PITH_FULL_IMAGE:figures/full_fig_p021_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Per-task verifier outcome heatmap for Qwen-3.6-Plus across methods on SWE-Skills-Bench-Hard. [PITH_FULL_IMAGE:figures/full_fig_p022_6.png] view at source ↗
read the original abstract

Agent skills are procedural artifacts that enable LLM agents to execute workflows, verify constraints, and recover from failures. Existing self-evolving methods refine skills using accumulated trajectories. However, they struggle in cold-start settings, where only an initial, imperfect skill is available. Consequently, skill construction defaults to expert authoring or one-shot LLM generation. Expert-authored skills are costly and may not align with how LLM agents actually execute tasks, while one-shot generated skills can be syntactically well formed yet behaviorally weak. To bridge this gap, we propose SkillRevise, an execution-grounded framework designed to iteratively refine these initial skills. SkillRevise diagnoses skill defects from execution evidence, retrieves relevant repair principles from a general memory, and applies execution-anchored edits. By re-executing candidates, it retains the first verifier-passing skill within the revision budget and falls back to empirical utility only when no candidate succeeds. Evaluated across three benchmarks and five LLMs, SkillRevise substantially outperforms one-shot baselines, improving the base agent's success rate on SkillsBench from 36.05% to 61.63%. Furthermore, the revised skills transfer across both executors and task environments, suggesting that SkillRevise captures reusable procedural knowledge beyond any single executor.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper introduces SkillRevise, an execution-grounded iterative framework that diagnoses defects in initial LLM-generated agent skills from execution traces, retrieves repair principles from memory, applies anchored edits, and retains the first verifier-passing candidate within a revision budget (falling back to empirical utility otherwise). It claims this yields substantial gains over one-shot LLM generation, raising base-agent success on SkillsBench from 36.05% to 61.63% across three benchmarks and five LLMs, with transfer to new executors and task environments.

Significance. If the reported gains are shown to stem from trace-conditioned diagnosis rather than un-controlled search effort, the approach would offer a practical route to improving cold-start agent skills without expert authoring or large trajectory corpora, and the transfer results would indicate reusable procedural knowledge.

major comments (3)
  1. [Experimental evaluation / SkillsBench results] Experimental section (and any associated tables/figures reporting the 36.05% → 61.63% delta): the manuscript must explicitly state the total LLM calls, re-execution trials, and verifier invocations allotted to the one-shot baseline versus SkillRevise; without this control the headline improvement cannot be attributed to the trace-diagnosis + repair-principle mechanism rather than simply receiving a larger revision budget.
  2. [SkillRevise framework / revision algorithm] Method description of the revision loop: the paper should quantify or bound the diagnostic information present in the execution traces (e.g., failure modes captured, granularity of retrieved principles) and demonstrate that the observed verifier-pass rate improvements exceed what would be expected from random re-sampling within the same budget.
  3. [Transferability results] Transfer experiments: the claim that revised skills transfer across executors and environments requires an ablation showing that the transferred skills outperform both the original one-shot skills and skills revised under the target executor, to confirm that the improvement is not executor-specific.
minor comments (2)
  1. [Method] Notation for the revision budget, verifier, and memory contents should be introduced with explicit symbols and a small pseudocode block for reproducibility.
  2. [Abstract / Introduction] The abstract and introduction should cite the exact number of revision attempts or LLM calls used in the one-shot baseline for direct comparison.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive feedback emphasizing the need for explicit computational controls and targeted ablations. We address each major comment below and commit to revisions that strengthen the attribution of gains to the trace-conditioned mechanism.

read point-by-point responses

  1. Referee: [Experimental evaluation / SkillsBench results] Experimental section (and any associated tables/figures reporting the 36.05% → 61.63% delta): the manuscript must explicitly state the total LLM calls, re-execution trials, and verifier invocations allotted to the one-shot baseline versus SkillRevise; without this control the headline improvement cannot be attributed to the trace-diagnosis + repair-principle mechanism rather than simply receiving a larger revision budget.

    Authors: We agree that explicit budget controls are required to isolate the contribution of trace diagnosis and repair principles. In the revised manuscript we will add a dedicated subsection and table reporting the exact counts of LLM calls, re-execution trials, and verifier invocations used by the one-shot baseline and by SkillRevise under identical revision budgets. This will make clear that SkillRevise does not receive additional search effort beyond the controlled budget. revision: yes

  2. Referee: [SkillRevise framework / revision algorithm] Method description of the revision loop: the paper should quantify or bound the diagnostic information present in the execution traces (e.g., failure modes captured, granularity of retrieved principles) and demonstrate that the observed verifier-pass rate improvements exceed what would be expected from random re-sampling within the same budget.

    Authors: We will expand the method section to categorize and bound the diagnostic content of traces (e.g., by enumerating captured failure modes such as precondition violations, state mismatches, and recovery gaps, together with the granularity of retrieved repair principles). We will also add an ablation that replaces the principle-retrieval step with random edit sampling under the identical revision budget and verifier budget, demonstrating that the observed pass-rate gains exceed those from random re-sampling. revision: yes

  3. Referee: [Transferability results] Transfer experiments: the claim that revised skills transfer across executors and environments requires an ablation showing that the transferred skills outperform both the original one-shot skills and skills revised under the target executor, to confirm that the improvement is not executor-specific.

    Authors: We acknowledge that the current transfer results would be strengthened by the requested ablation. In the revision we will report additional experiments in which skills revised by SkillRevise on the source executor are compared, when transferred to the target executor, against both the original one-shot skills and skills that were revised directly on the target executor. This will confirm that the procedural knowledge is reusable rather than executor-specific. revision: yes

Circularity Check

0 steps flagged

No circularity; purely empirical claims with no derivations or self-referential reductions.

full rationale

The paper presents SkillRevise as an empirical framework for iterative skill revision using execution traces and repair principles, evaluated via benchmark success rates (e.g., SkillsBench improvement from 36.05% to 61.63%). No equations, mathematical derivations, fitted parameters renamed as predictions, or load-bearing self-citations appear in the provided text. All claims rest on external benchmark comparisons rather than any reduction to the method's own inputs by construction. The skeptic concern about revision budget vs. baseline is an experimental-design issue, not circularity.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The framework rests on domain assumptions about the informativeness of execution traces and the utility of a general repair memory; no free parameters or invented entities are mentioned in the abstract.

axioms (2)
  • domain assumption Execution traces supply sufficient evidence to diagnose concrete skill defects
    Invoked as the basis for the diagnosis step in the revision loop.
  • domain assumption A general memory of repair principles exists and can be retrieved to produce effective edits
    Central to the retrieval-and-edit component of SkillRevise.

pith-pipeline@v0.9.1-grok · 5799 in / 1410 out tokens · 29098 ms · 2026-06-28T17:35:43.459212+00:00 · methodology

discussion (0)

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Reference graph

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