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arxiv: 2606.09316 · v3 · pith:ZHVOTCAPnew · submitted 2026-06-08 · 💻 cs.AI

Anything2Skill: Compiling External Knowledge into Reusable Skills for Agents

Qianjun Pan , Yutao Yang , Junsong Li , Jie Zhou , Kai Chen , Xin Li , Qin Chen , Liang He This is my paper

Pith reviewed 2026-06-27 16:49 UTC · model grok-4.3

classification 💻 cs.AI
keywords skill extractionretrieval-augmented generationprocedural memoryagent capabilitiesSkillBankexternal knowledge compilationstructured contracts
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The pith

Anything2Skill extracts reusable skills from arbitrary external records and installs them in agents so they can retrieve both facts and pre-compiled procedures at runtime.

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

The paper claims that standard retrieval-augmented agents waste effort re-deriving task procedures from scattered manuals, logs, and examples each time. Anything2Skill instead decomposes knowledge records, extracts candidate skills under a skill-tree prior, and packages them as structured contracts that list invocation conditions, steps, constraints, and evidence. These contracts are stored and reconciled in a persistent SkillBank that supports taxonomy-aware updates and version tracking. At inference the agent pulls both original passages and matching skills, which the experiments show raises success to 98.85 percent on qsv and 94.10 percent on GitHub-CLI. A reader would care because the approach converts one-off knowledge access into reusable capability without requiring the agent to reinvent procedures.

Core claim

Anything2Skill is a taxonomy-guided pipeline that first decomposes external records into evidence windows, performs plan-and-expand skill extraction under a skill-tree prior, converts the results into structured skill contracts, and maintains them in a SkillBank through registry reconciliation, lifecycle tracking, and visible projections; at runtime agents retrieve both declarative passages and these procedural contracts, allowing RAG to supply evidence while the compiled skills supply executable guidance.

What carries the argument

The structured skill contract, which encodes invocation conditions, contraindications, action moves, workflow steps, constraints, output specifications, supporting evidence, and confidence scores for each extracted procedure.

If this is right

  • Agents no longer re-infer the same workflow from raw passages on every similar task.
  • SkillBank enables versioned updates and reconciliation so that new records can refine or replace earlier skills.
  • Retrieval at inference time can jointly rank passages and skills, giving the agent both declarative and procedural context.
  • The same pipeline applies to any corpus of manuals, logs, trajectories, or documentation without domain-specific retraining.

Where Pith is reading between the lines

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

  • If the skill contracts prove reliable, future agents could treat the SkillBank as a form of long-term procedural memory that persists across sessions and users.
  • The approach may reduce the need for few-shot prompting or fine-tuning by turning external knowledge directly into executable units.
  • Taxonomy-aware compilation could be extended to automatically detect conflicting skills and surface them for human review.

Load-bearing premise

The plan-and-expand extraction step produces accurate, non-hallucinated, and reusable procedures from any external record without introducing systematic errors that would lower downstream agent performance.

What would settle it

An experiment that replaces the extracted skills with randomly generated or manually verified incorrect contracts and measures whether success rates on qsv and GitHub-CLI fall below the RAG-only baseline.

Figures

Figures reproduced from arXiv: 2606.09316 by Jie Zhou, Junsong Li, Kai Chen, Liang He, Qianjun Pan, Qin Chen, Xin Li, Yutao Yang.

Figure 1
Figure 1. Figure 1: Overview of ANYTHING2SKILL, which compiles heterogeneous knowledge records into reusable procedural skills. The framework uses a skill taxonomy as a structural prior for skill extraction, manages extracted skills through compilation, reconciliation, and versioned tree projection, and supports inference by retrieving both declarative evidence and procedural skills for agent execution. σ − denote invocation … view at source ↗
read the original abstract

Retrieval-augmented generation (RAG) enables agents to access external knowledge at inference time, but it primarily retrieves fragmented declarative evidence, leaving agents to repeatedly infer task procedures from passages, manuals, examples, logs, or trajectories. This raises a fundamental question: can skills extracted from external knowledge bases be installed into an agent, enabling it to rapidly approximate domain expertise? In this paper, we propose Anything2Skill, a taxonomy-guided framework that compiles heterogeneous external knowledge into reusable, retrievable, and executable skills for agents. Given a corpus of knowledge records, \textsc{Anything2Skill} first decomposes each record into evidence windows and performs plan-and-expand skill extraction under a skill-tree prior. The extracted candidates are then converted into structured skill contracts that specify invocation conditions, contraindications, action moves, workflow steps, constraints, output specifications, supporting evidence, and confidence scores. To construct a deployable procedural memory, Anything2Skill manages the extracted skills in a persistent SkillBank through taxonomy-aware compilation, registry-level reconciliation, lifecycle tracking, versioned updates, and visible skill-tree projection. At inference time, agents retrieve both task-specific passages from the original knowledge base and relevant procedural skills from the SkillBank, allowing RAG to provide declarative evidence while compiled skills provide reusable procedural guidance. Experiments on qsv and GitHub-CLI show that Anything2Skill combined with RAG achieves 98.85\% and 94.10\% success rates, respectively, substantially outperforming RAG-only agents. These results suggest that compiling latent procedural knowledge into explicit skills is an effective way to extend retrieval-augmented agents from knowledge access toward capability reuse.

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 proposes Anything2Skill, a taxonomy-guided framework that compiles heterogeneous external knowledge into reusable skills for agents. It decomposes records into evidence windows, performs plan-and-expand extraction under a skill-tree prior, converts candidates to structured skill contracts (specifying conditions, steps, constraints, evidence, and confidence), manages them in a persistent SkillBank with taxonomy-aware compilation and lifecycle tracking, and at inference retrieves both RAG passages and skills from the SkillBank. Experiments on qsv and GitHub-CLI report 98.85% and 94.10% success rates for the combined system, substantially outperforming RAG-only agents, suggesting that explicit skill compilation extends RAG agents toward capability reuse.

Significance. If the extraction process reliably produces accurate, non-hallucinated procedures, the work would meaningfully advance retrieval-augmented agents by shifting from repeated inference over declarative fragments to reuse of compiled procedural memory. The SkillBank management and dual retrieval (declarative + procedural) address a recognized gap in current agent systems; the reported success rates, if substantiated with proper controls, would constitute a concrete demonstration of this extension.

major comments (3)
  1. [Abstract] Abstract: the reported success rates of 98.85% (qsv) and 94.10% (GitHub-CLI) are given only for the combined Anything2Skill+RAG system; no baseline comparisons, RAG-only numbers, ablations of the plan-and-expand extraction or contract conversion steps, error analysis, or failure-mode discussion are supplied, preventing attribution of gains to the skill compilation mechanism.
  2. [Method] Method (skill extraction and SkillBank): the central claim rests on the plan-and-expand extraction under skill-tree prior plus conversion to structured contracts producing accurate, reusable procedures; however, no quantitative evaluation of extraction fidelity, hallucination frequency, or agreement with source records is provided, leaving open the possibility that systematic mis-specification of workflows or constraints would inject errors into the SkillBank.
  3. [Experiments] Experiments: without ablations isolating SkillBank contribution, comparisons to alternative skill-acquisition methods, or cross-validation of extracted contracts against ground-truth procedures, the claim that the framework extends RAG agents via reusable skills remains under-supported by the presented evidence.
minor comments (2)
  1. [Abstract] The abstract introduces 'skill contract' and 'SkillBank' without a concise formal definition or illustrative example at first mention.
  2. [Method] Notation for confidence scores and taxonomy projection could be clarified with a small example table early in the methods.

Simulated Author's Rebuttal

3 responses · 1 unresolved

We thank the referee for the constructive feedback. The comments correctly identify areas where additional quantitative support and clearer presentation would strengthen attribution of the reported gains. We respond point-by-point below and will revise the manuscript accordingly.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the reported success rates of 98.85% (qsv) and 94.10% (GitHub-CLI) are given only for the combined Anything2Skill+RAG system; no baseline comparisons, RAG-only numbers, ablations of the plan-and-expand extraction or contract conversion steps, error analysis, or failure-mode discussion are supplied, preventing attribution of gains to the skill compilation mechanism.

    Authors: We agree that the abstract should enable immediate attribution. We will revise it to report the RAG-only baselines alongside the combined-system numbers and to reference the ablation studies and error analysis already present in the experiments section. This change will be made in the next version. revision: yes

  2. Referee: [Method] Method (skill extraction and SkillBank): the central claim rests on the plan-and-expand extraction under skill-tree prior plus conversion to structured contracts producing accurate, reusable procedures; however, no quantitative evaluation of extraction fidelity, hallucination frequency, or agreement with source records is provided, leaving open the possibility that systematic mis-specification of workflows or constraints would inject errors into the SkillBank.

    Authors: The concern is valid. The manuscript currently supports extraction quality through qualitative examples and end-task gains. In revision we will add a quantitative human-evaluation study on a sampled subset of extracted contracts, reporting agreement with source records and estimated hallucination rates. revision: yes

  3. Referee: [Experiments] Experiments: without ablations isolating SkillBank contribution, comparisons to alternative skill-acquisition methods, or cross-validation of extracted contracts against ground-truth procedures, the claim that the framework extends RAG agents via reusable skills remains under-supported by the presented evidence.

    Authors: We will expand the experiments section with ablations that isolate SkillBank retrieval (including comparisons against alternative skill-acquisition baselines) and will report cross-validation results on the subset of tasks for which ground-truth procedures exist. The revision will also explicitly discuss the scope of available ground truth. revision: partial

standing simulated objections not resolved
  • Comprehensive cross-validation of all extracted contracts is constrained by the lack of ground-truth procedural annotations for the full heterogeneous corpus.

Circularity Check

0 steps flagged

No circularity: framework is procedural and benchmarked externally

full rationale

The paper describes a taxonomy-guided extraction pipeline (plan-and-expand under skill-tree prior, conversion to contracts, SkillBank management) followed by retrieval at inference time, with performance measured on external task benchmarks (qsv, GitHub-CLI). No equations, fitted parameters, self-citations as load-bearing premises, or renamings of known results appear in the provided text. The reported success rates are direct empirical outcomes of the combined system rather than quantities defined by the extraction process itself. This satisfies the default expectation of no significant circularity.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 2 invented entities

The framework introduces several new constructs (skill contracts, SkillBank, skill-tree prior, taxonomy-aware compilation) whose correctness is not independently verified in the abstract; the central performance claim rests on the untested premise that LLM-based extraction reliably surfaces reusable procedures.

axioms (1)
  • domain assumption External knowledge corpora contain extractable procedural skills that can be represented as structured contracts without loss of fidelity.
    Invoked in the description of decomposition and contract conversion steps.
invented entities (2)
  • SkillBank no independent evidence
    purpose: Persistent store for versioned, retrievable, executable skills with taxonomy projection and lifecycle tracking.
    New registry-level component introduced to manage extracted skills.
  • skill contract no independent evidence
    purpose: Structured specification containing invocation conditions, contraindications, action moves, workflow steps, constraints, output specs, evidence, and confidence.
    New data structure for representing compiled skills.

pith-pipeline@v0.9.1-grok · 5850 in / 1482 out tokens · 22430 ms · 2026-06-27T16:49:28.134015+00:00 · methodology

discussion (0)

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