SkillMigrator: Cross-Platform Skill Migration

• SkillMigrator is a framework family that enables the migration, adaptation, and secure reuse of skill representations across heterogeneous systems.
• It employs representation learning, supervised translation mappings, and modular pipelines to reconcile differing schemas and computational agents.
• Applications span digital learning platforms, LLM-based agents, and web automation, yielding measurable improvements in efficiency and compatibility.

SkillMigrator refers to a family of frameworks, toolchains, and methodologies enabling the migration, adaptation, and reuse of skills or skill representations across heterogeneous systems. Applications span digital learning platforms, LLM-based and embodied agents, web automation, and compiler-powered cross-framework deployment. SkillMigrator’s core function is to establish compatibility and maximize utilization of skill artifacts—ranging from manually labeled pedagogical units to induced macros and agent plugins—across platform, environment, and agent or model boundaries. This encompasses representation learning, translation layers, compositional reasoning, runtime rewiring, and rigorous security controls. The following sections synthesize major SkillMigrator designs and research paradigms.

1. Foundations: Motivations and Problem Formalizations

SkillMigrator arose from the challenge of reconciling skills dispersed across platforms, agents, or frameworks with mutually incompatible schemas, APIs, taxonomies, or runtime assumptions. For instance, educational platforms often encode ostensibly overlapping skills as divergent labels, while LLM agents may require skills encoded for one framework to be ported to another with different syntax or safety guarantees (Li et al., 2021, Ouyang et al., 5 May 2026). Embodied or hierarchical policies encounter drift as low-level skills and high-level policies evolve asynchronously (Lee et al., 24 Sep 2025).

The central mathematical abstraction is the alignment or translation between heterogenous skill spaces, which may be discrete taxonomies, vectorized embeddings, hierarchical library structures, or strongly-typed IRs. Across domains, the SkillMigrator mandate is to maximize transfer, minimize manual crosswalk effort, and support ongoing evolution/adaptation without performance regression, security exposure, or retraining burdens.

2. Representation Learning and Skill Space Alignment

A unifying principle in SkillMigrator systems is the representation of skills as structured artifacts—ranging from content/contextual embeddings to code units with security annotations or proto-typical trajectory clusters.

Digital Learning Platforms

In digital learning contexts (Li et al., 2021), skills are quantized into real-valued vectors, drawing from:

• Content-Only Models: Bag-of-Words (BoW), TF-IDF, Content2Vec (pretrained word embeddings over aggregated problem content).
• Context-Only Models: Skill2Vec (skip-gram trained over clickstream-derived skill sequences).
• Joint Models: Content2Vec⊕Skill2Vec and TAMF (Text-Associated Matrix Factorization linking content and co-occurrence).

Translation between skill spaces is achieved via a supervised linear machine translation mapping $M: \mathbb{R}^d \to \mathbb{R}^{d'}$ optimized with a small set of expert-annotated skill pairs. Cosine similarity over mapped embeddings is used for crosswalk candidate selection and recall evaluation.

Separator: Hierarchical Skill Structures

Hierarchical approaches, such as SkillX (Wang et al., 6 Apr 2026), decompose skills into a three-level library: planning, functional, and atomic skills, extracted via trajectory segmentation and clustered compositional primitives. These multilevel structures are portable and amenable to iterative refinement and cross-agent migration.

Table: Core Embedding Approaches in Educational Use-Cases | Model | Content Signal | Context Signal | Joint? | |----------------|---------------|---------------|----------| | BoW / TF-IDF | Problem text | – | No | | Content2Vec | Embeddings | – | No | | Skill2Vec | – | Skill logs | No | | TAMF | Embeddings | Co-occurrence | Yes |

3. Protocols and Pipelines for Skill Migration

SkillMigrator frameworks operationalize migration with modular pipelines, runtime protocols, and iterative refinement strategies.

Educational Crosswalks

Li et al. (Li et al., 2021) prescribe a pipeline of (1) embedding extraction (BoW, TF-IDF, etc.); (2) supervised translation mapping via linear regression with ground truth; (3) retrieval of top-k nearest neighbors in target taxonomy for each source skill; and (4) recall@k and MRR evaluation with gold crosswalks (Fleiss’ κ≈0.7–0.8). Deployment is enabled via scalable storage (e.g., FAISS index for sub-millisecond top-k selection) and GPU-backed embedding computation.

Plug-and-Play LLM Agent Integration

SkillKBs produced by SkillX (Wang et al., 6 Apr 2026) are migrated as plug-and-play libraries: at inference, planning skills are retrieved, pseudo-plans rewritten by LLM, and component skills injected into the agent prompt, yielding improvements of up to 10 percentage points in Pass@4 task success for weaker base agents.

Web Skill Migration via TIPs

SkillMigrator applied to LLM web agents (He et al., 16 Jun 2026) encodes skills as transferable interaction patterns (TIPs), each comprising: intent, operation template, slot schema with synonyms, and a page-layout skeleton. TIP induction is automated from trajectories; retrieval at test time uses a layout similarity kernel combining text and tree-edit distance, with slot rebinding via embedding-based Hungarian assignment. Macros are deterministically unrolled into tool-call primitives upon invocation, with significant reductions (8–10%) in LLM action calls on WebArena and Mind2Web.

Cross-Framework Compilation

Compiler-based SkillMigrator such as SkCC (Ouyang et al., 5 May 2026) leverage a strongly-typed intermediate representation (SkIR) to decouple skill semantics from platform syntax. Skills are compiled to multiple target agents (Claude, Codex, Kimi, Gemini) with platform-specific emitters and compile-time security analysis (Anti-Skill Injection). This reduces adaptation complexity from $O(m \times n)$ to $O(m+n)$, raises pass rates by 12.2–13.5 percentage points, and triggers security constraints in 94.8% of skills.

Skill-Policy Compatibility

In embodied/hierarchical agent settings, SkillMigrator manifests as a “lazy learning” interface layer (Lee et al., 24 Sep 2025), mapping high-level policy subtasks to the evolving skill library at inference via prototype-based alignment, thereby preserving policy compatibility without retraining.

4. Evaluation: Metrics, Benchmarks, and Results

SkillMigrator capabilities are empirically validated across domains with both quantitative and qualitative metrics.

Digital Learning Platforms

• Recall@5 and MRR for crosswalk prediction, with top configurations achieving 0.8 recall@5 and >0.6 MRR (Li et al., 2021).
• t-SNE/k-means clusters visually confirm that functionally analogous skills (e.g., unit conversion, Pythagoras theorem) are mapped across taxonomies.

LLM Agents and Web Automation

• Pass@4, Avg@4, and execution efficiency (#Actions, #Tokens) measure improvements when migrating SkillKBs (Wang et al., 6 Apr 2026). Gains of ~10 percentage points are reported even for non-backbone agents.
• LLM web agents with SkillMigrator TIP retrieval reduce per-task action counts (SkillMigrator: 5.4; ReAct: 6.5; PolySkill: 5.9) at steady-state success rates (He et al., 16 Jun 2026).

Cross-Framework Compilation

• SkillsBench benchmarks (Pass@1, token/time efficiency) show that compiled skills consistently outperform originals, with up to 46% runtime token savings and sub-10ms per-skill compilation (Ouyang et al., 5 May 2026).

Hierarchical Policy Compatibility

• Backward and forward transfer (BwSC, FwSC) rates under SIL-C are substantially higher than previous approaches (+18.6 to +42.5 pp), with robust performance in few-shot and noisy conditions (Lee et al., 24 Sep 2025).

5. Architectural and System-Level Considerations

SkillMigrator frameworks are distinguished by several architectural patterns:

• Modular pipelines handling ingestion, embedding, migration, security enforcement, and deployment (Li et al., 2021, Ouyang et al., 5 May 2026).
• Vector-store–backed retrieval systems (e.g., FAISS) and hybrid search (MMR, threshold-based filtered k-NN) for scalable migration and query (Wang et al., 6 Apr 2026).
• Protocol-driven agent negotiation including peer-to-peer DHT-based discovery, secure package transfer, sandboxed integration, and cryptographically signed code units (Tagkopoulos et al., 8 Apr 2025).
• Isolation and sandboxing of transferred code, permission auditing, and multi-layered safety guarantees, including HITL gating for high-risk skills (Ouyang et al., 5 May 2026).
• Append-only memory and prototype clustering to guarantee non-destructive skill evolution and compatibility (Lee et al., 24 Sep 2025).

6. Limitations, Challenges, and Future Directions

SkillMigrator frameworks introduce new design and maintenance burdens, including:

• One-to-many and coarse-to-fine mappings, particularly across platforms with different granularity, remain challenging. Thresholds or “None” classes handle unmatched cases (Li et al., 2021).
• Platform idiosyncrasies in format parsing and prompt sensitivity require ongoing maintenance of Emitter templates (Ouyang et al., 5 May 2026).
• Cross-domain transfer is hampered by tool-schema specificity, evolving APIs, and model capability mismatch (Wang et al., 6 Apr 2026).
• Structural matching (e.g., via tree-edit distance) is limited when similar layouts encode unrelated semantics; augmentation with visual signals could improve discrimination (He et al., 16 Jun 2026).
• Prototype drift and unsupervised clustering quality limit skill-policy remapping in noisy or unstructured environments (Lee et al., 24 Sep 2025).

Prospective improvements include curriculum-driven exploratory expansion, differentiable skill-ranking losses, automated semantic normalization, and tight feedback loops from runtime telemetry for continuous adaptation and vulnerability discovery (Wang et al., 6 Apr 2026, Ouyang et al., 5 May 2026).

7. Cross-Domain Synthesis and Theoretical Analogies

SkillMigrator methods are unified by their enablement of ad hoc, scalable, and secure migration of actionable capabilities. Analogies to lateral gene transfer emphasize the evolutionary advantage of rapid skill dissemination and recombination, whether between microbial organisms (Tagkopoulos et al., 8 Apr 2025) or distributed computational agents. Compilation, dynamic translation, runtime remapping, and robust retrieval all serve to maximize the utility and safety of skill artifacts in an environment of incessant framework, model, and task evolution.

SkillMigrator thus represents the key infrastructure for realizing cross-platform, cross-agent, and cross-domain transferability of skills, supporting both manual and automated adaptation, while maintaining rigorous compatibility, security, and efficiency standards.