Automating Skill Acquisition through Large-Scale Mining of Open-Source Agentic Repositories: A Framework for Multi-Agent Procedural Knowledge Extraction

Automating Skill A cquisition through Large-Scale Mining of Op en-Source Agen tic Rep ositories: A F ramew ork for Multi-Agen t Pro cedural Kno wledge Extraction Sh uzhen Bi 2 , 3 , Mengsong W u 1 , 2 , Hao Hao 1 , Keqian Li 1 , W entao Liu 1 , 2 , Siyu Song 1 , Hongb o Zhao 1 , and Aimin Zhou ∗ 1 , 2 ∗ Corresp onding author: amzhou@cs.ecnu.cn 1 East China Normal Univ ersity 2 Shanghai Innov ation Institute 3 Univ ersity of Science and T ec hnology of China Email addresses: sa22916003@mail.ustc.edu.cn, radi.cat@qq.com, haohao@sjtu.edu.cn, kqli@mail.ecnu.edu.cn, wtliu@stu.ecnu.edu.cn, siyusong00@gmail.com, hbzhao@stu.ecnu.edu.cn, amzhou@cs.ecnu.cn Abstract The transition from monolithic large language mo dels (LLMs) to modular, skill-equipp ed agen ts represents a fundamental architectural shift in artificial intelligence deploymen t. While general-purp ose models demonstrate remark able breadth in declarativ e kno wledge, their utility in autonomous w orkflo ws is frequently constrained b y insufficien t specialized pro cedural exp ertise. This rep ort in vestigates a systematic framework for automated acqui- sition of high-quality agen t skills through mining of op en-source rep ositories on platforms suc h as GitHub. W e fo cus on the extraction of visualization and educational capabilities from state-of-the-art systems including TheoremExplainAgent and Co de2Video, b oth uti- lizing the Manim mathematical animation engine. The framework encompasses rep ository structural analysis, semantic skill iden tification through dense retriev al, and translation to the standardized SKILL.md format. W e demonstrate that systematic extraction from agen- tic rep ositories, com bined with rigorous security gov ernance and m ulti-dimensional ev al- uation metrics, enables scalable acquisition of pro cedural knowledge that augmen ts LLM capabilities without requiring mo del retraining. Our analysis reveals that agent-generated educational con tent can ac hieve 40% gains in knowledge transfer efficiency while main taining p edagogical qualit y comparable to human-crafted tutorials. 1 In tro duction The deploymen t of artificial intelligence has undergone a paradigm shift from monolithic transformer- based large language mo dels to ward modular, skill-equipp ed agen t architectures [1, 2]. While con temp orary LLMs p ossess extensiv e declarativ e kno wledge spanning diverse domains, their ef- fectiv eness in autonomous task execution remains limited by insufficient sp ecialized pro cedural exp ertise required for real-world applications [2, 3]. This fundamen tal limitation has catalyzed the emergence of the “agen t skill” paradigm—a mo dular abstraction framework wherein pro cedu- ral kno wledge is encapsulated into discrete, filesystem-based units that agents can dynamically disco ver, load, and execute on demand [1, 2]. By arc hitecturally decoupling specific capabilities from underlying model parameters, this paradigm enables dynamic capabilit y extension without incurring the prohibitive computational and temp oral costs asso ciated with mo del retraining or fine-tuning [2, 3]. The skill-based archi- tecture transforms the fundamen tal question from “how do w e train a mo del to perform task X?” to “ho w do w e pro vide a mo del with executable pro cedural knowledge for task X?” Cen tral to adv ancing this architectural vision is the c hallenge of skill acquisition at scale. T raditionally , high-quality skills are manually authored b y domain exp erts, providing reliabilit y 1 guaran tees but suffering from severe scalability constraints [1, 2]. Autonomous discov ery meth- o ds, while promising, frequently struggle to maintain semantic coherence and p edagogical v alue in op en-world environmen ts [1, 4]. A third acquisition path wa y in v olves systematic extraction of pro cedural knowledge from existing op en-source soft ware, particularly sp ecialized agen tic rep ositories hosted on platforms suc h as GitHub [1, 5]. These rep ositories often contain sophisticated, domain-specific logic for complex tasks—including mathematical theorem visualization, educational conten t syn thesis, and m ultimo dal explanation generation—that can be systematically refactored in to standard- ized, reusable agentic skills [6, 7]. This rep ort presen ts a comprehensiv e framework for automated skill acquisition through large-scale mining of GitHub-based agen t rep ositories. W e fo cus sp ecifically on extraction of visu- alization and educational capabilities from tw o state-of-the-art systems: TheoremExplainAgen t (TEA), which generates long-form visual explanations of STEM theorems [6], and Co de2Video, whic h implements a co de-cen tric paradigm for educational video generation [7]. Our framework encompasses three primary comp onen ts: (1) rep ository structural analysis and contextualiza- tion, (2) semantic skill iden tification through dense retriev al mechanisms, and (3) systematic translation to the SKILL.md standardized format. 2 The F ormal P aradigm of Agen tic Skills 2.1 Mathematical F ormulation T o establish rigorous foundations for skill extraction, we first define the mathematical structure of an agentic skill. F ormally , an agen tic skill S is represented as a four-tuple: S = ( C , π , T , R ) (1) where eac h comp onen t serv es a distinct functional role in the skill’s op erational semantics [8]. The applicabilit y conditions C define the initiation set—the contextual prerequisites that determine when a skill b ecomes relev ant for activ ation [8]. This comp onen t enables efficient skill selection by allo wing agents to maintain a wareness of skill a v ailabilit y without loading complete pro cedural conten t into working memory . The p olicy π encapsulates the core pro cedural kno wledge, represen ting the sequence of ac- tions or reasoning steps the agent must execute. This p olicy ma y manifest in m ultiple forms: natural language prompt templates, executable Python scripts, reinforcemen t learning p olicies, or hybrid symbolic-neural w orkflows [8]. The p olicy comp onent distinguishes skills from simple to ol wrapp ers by em b edding domain-sp ecific reasoning and decision-making logic. T ermination criteria T provide the logical conditions for determining successful skill comple- tion, enabling b oth the executing agent and external orchestrators to v erify goal achiev emen t [8]. These criteria may include output v alidation rules, state v erification conditions, or success met- rics sp ecific to the task domain. The in terface R establishes a standardized callable b oundary , defining input parameters, output formats, and composition protocols that enable runtime in tegration with agen t arc hi- tectures [8]. This standardization is critical for enabling skill reuse across heterogeneous agent implemen tations and facilitating hierarchical skill comp osition. This formal structure ensures that skills remain sim ultaneously executable, reusable, and go vernable, distinguishing them from atomic tools (which lack complex procedural logic) and episo dic memories (which lack standardized callable interfaces) [8]. 2.2 The SKILL.md Sp ecification The architectural implementation of the agent skill paradigm has con verged on the SKILL.md sp ecification, originally developed by An thropic and subsequently released as an op en standard 2 [9, 10]. This sp ecification implements a progressive disclosure architecture designed to minimize con text windo w consumption while maintaining access to deep pro cedural knowledge [1, 2]. The progressive disclosure arc hitecture organizes skill information in to three hierarc hical lev- els, eac h activ ated under different context-loading conditions. T able 1 details this organizational structure. T able 1: Progressive Disclosure Architecture for Agen tic Skills Lev el Comp onen t Con ten t and Metadata Con text Load T ok en Us- age Lev el 1 Metadata Y AML fron tmatter: Name, Description, V ersion, T rigger Conditions Pre-loaded at startup 30–100 Lev el 2 Instructions Pro cedural knowledge: W ork- flo ws, b est practices, guid- ance, step-by-step logic Loaded up on acti- v ation 200–5,000 Lev el 3 Resources Auxiliary assets: Executable scripts, reference do cumen ts, templates, schemas Loaded on- demand by scripts Un b ounded Lev el 1 metadata serv es as an efficien t “table of con ten ts,” enabling agen ts to main tain a wareness of thousands of av ailable skills without context window degradation [1, 2]. When user requests match a skill’s descriptiv e metadata, the agen t activ ates Level 2, injecting pro cedural instructions in to the conv ersation con text as hidden meta-messages [1, 2]. This injection mo difies the agen t’s in ternal reasoning pro cess rather than its direct output, allo wing skills to reshap e problem-solving approaches [1, 3]. Lev el 3 resources remain dorman t un til explicitly in v oked by Level 2 instructions or exe- cutable scripts, enabling skills to lev erage arbitrarily large reference materials without impacting baseline context consumption [9, 13]. 3 Metho dological F ramew ork for Skill Extraction The systematic acquisition of skills from GitHub rep ositories requires a multi-stage pip eline that transforms monolithic co debases into mo dular SKILL.md artifacts. This section details the three primary stages: rep ository structural analysis, semantic skill iden tification, and standardized translation. 3.1 Rep ository Structural Analysis and Contextualization Skill extraction b egins with comprehensiv e structural decomp osition of target rep ositories. T o ols suc h as rep o2AI generate Markdown-formatted represen tations of complete directory hierarc hies and file conten ts [5]. This structural mapping provides essential context for LLM-based extrac- tion agents, enabling understanding of task orc hestration patterns and logical dep endencies [5, 6]. F or rep ositories implemen ting complex agen tic workflo ws, identification of cen tral orchestra- tion scripts (e.g., generate_video.py ) and configuration directories (e.g., task_generator/prompts_raw ) allo ws extraction pro cesses to fo cus on reasoning logic and to ol-use patterns that define sp ecial- ized exp ertise [6]. The structural analysis phase pro duces a hierarchical map of: • Core execution scripts and their input/output sp ecifications • Configuration files defining workflo w parameters and agent b ehaviors 3 • Auxiliary mo dules implementing domain-sp ecific algorithms • Do cumentation and usage examples demonstrating intended workflo ws This con textualization enables subsequent extraction stages to distinguish b et ween reusable pro cedural patterns and rep ository-sp ecific implementation details. 3.2 Seman tic Skill Identification through Dense Retriev al Once rep ository structure is mapp ed, the system iden tifies “latent skills”—recurring pro cedural patterns amenable to generalization across contexts [11, 12]. This identification task is form ulated as a tw o-stage ranking problem combining dense retriev al and cross-enco der refinemen t [11]. 3.2.1 Dense Retriev al Stage The extraction agent enco des task descriptions and co de mo dules in to dense vector representa- tions using trained bi-enco ders [11]. F or a rep ository containing N co de mo dules { M 1 , M 2 , . . . , M N } and a set of task descriptions { T 1 , T 2 , . . . , T K } , the bi-enco der pro duces embeddings e M and e T resp ectiv ely . Candidate skills are identified by computing cosine similarity: sim ( T i , M j ) = e T i · e M j ∥ e T i ∥∥ e M j ∥ (2) The top- K candidate mo dules for each task are retained for subsequent refinemen t [11]. 3.2.2 Binary Ranking Stage A cross-encoder rank er performs fine-grained relev ance assessment by jointly enco ding task- mo dule pairs and pro ducing relev ance scores [11]. Only mo dules exceeding a calibrated relev ance threshold τ are promoted for skill extraction. This tw o-stage approach ensures that extracted skills represent genuinely reusable patterns rather than pro ject-sp ecific implementations. Extraction criteria include: 1. Recurrence : The procedural pattern appears in m ultiple con texts or solves a class of problems 2. V erification : The co de is functional, well-documented, and free of critical bugs 3. Non-ob viousness : The logic required domain exp ertise or debugging to discov er 4. Generalizabilit y : The pattern can b e parameterized or adapted to different con texts Mo dules satisfying these criteria b ecome candidates for translation to the SKILL.md format [13, 14]. 3.3 T ranslation to the SKILL.md Standard The final extraction stage syn thesizes SKILL.md artifacts from iden tified pro cedural patterns. This translation pro cess inv olv es three primary comp onen ts [13, 15]: 4 3.3.1 F ron tmatter Generation The extraction agent syn thesizes metadata conforming to Y AML sp ecifications: • name : Low ercase, hyphen-separated identifier (e.g., visual-theorem-walkthrough ) • description : Concise statement of skill purp ose and activ ation conditions • version : Seman tic versioning for tracking skill ev olution • trigger : Pattern-matc hing rules for automatic skill activ ation • dep endencies : Required to ols, libraries, or prerequisite skills 3.3.2 Instruction Drafting Lev el 2 instructions are written as LLM-consumable pro cedural guidance rather than end-user do cumen tation [10, 17]. Effective instructions emphasize: • Step-by-step workflo w decomp osition with decision p oin ts • Error handling strategies and common failure mo des • Best practices deriv ed from rep ository analysis • Integration patterns with complementary skills or to ols Instructions av oid rep ository-sp ecific implementation details, instead fo cusing on generaliz- able pro cedural knowledge. 3.3.3 Asset Bundling Executable scripts, reference documentation, and configuration templates are organized into standardized sub directories ( scripts/ , references/ , templates/ ) [9, 13]. Assets are refactored to eliminate hardco ded paths, API keys, or rep ository-specific dep endencies, ensuring p ortabilit y across deploymen t environmen ts. 4 Deep Analysis of Source Rep ositories T o demonstrate the practical application of this extraction framew ork, we analyze tw o leading rep ositories in the domain of multimodal educational conten t generation: TheoremExplainAgent and Co de2Video. Both systems leverage the Manim mathematical animation engine to pro duce high-fidelit y visual explanations [6, 7]. 4.1 TheoremExplainAgen t: Multimo dal STEM Explanation TheoremExplainAgen t (TEA) addresses the challenge of comm unicating abstract STEM the- orems through long-form video conten t exceeding five minutes in duration [6]. The system implemen ts a tw o-agent architecture comprising a Planner and a Co ding Agen t [6]. 5 4.1.1 Planner Agent Arc hitecture The Planner functions as an instructional designer, transforming theorem statements into p ed- agogically structured storyb oards [6]. Key outputs include: • Scene Purp ose : High-level learning ob jective for eac h video segmen t • Scene Description : Natural language narrativ e of visual conten t • Scene La y out : Spatial organization sp ecifications for mathematical ob jects This structured decomp osition ensures logical sequencing and visual clarit y [6]. 4.1.2 Co ding Agen t with Error Correction The Co ding Agent translates storyb oards in to executable Manim Python scripts [6]. T o impro ve reliabilit y , TEA implemen ts a m ulti-attempt error-correction lo op enabling the agen t to analyze Python stac k traces and iterativ ely debug animation co de [6]. This self-refinemen t capabilit y significan tly reduces manual interv en tion requiremen ts. 4.1.3 Retriev al-Augmented Generation TEA in tegrates a Retriev al-Augmen ted Generation (RAG) system to ground the Co ding Agent in current Manim documentation [6]. This approach preven ts API hallucinations and ensures utilization of correct function calls for complex visualizations including geometric Bro wnian motion and gradient descen t animations [6]. T able 2 summarizes the technical sp ecifications relev an t to skill extraction. T able 2: T echnical Sp ecifications of TheoremExplainAgent for Skill Extraction F eature T ec hnical Implemen tation Relev ance to Skill Acquisi- tion Core Library Manim Communit y Edition Pro vides pro cedural target for vi- sualization skills [6] Kno wledge Base TheoremExplainBenc h (240 theorems) Div erse domain co v erage (CS, Chemistry , Math, Physics) [6] Reasoning Lo op Planner-Coder F eedback Defines workflo w for visual story- telling skills [6] Refinemen t Visual-Fix Co de F eedback Implemen ts visual debugging skill pattern [6] Scaling Scene/T opic Concurrency Pro vides patterns for high- throughput generation [6] 4.2 Co de2Video: Co de-Centric Educational F ramew ork Co de2Video extends b eyond individual theorem explanations to implemen t a comprehensive framew ork for educational video generation [7]. The system positions executable code as the unifying medium for b oth temp oral sequencing and spatial organization [7]. 4.2.1 T ri-Agen t Arc hitecture Co de2Video implements a mo dular three-agent design: 6 1. Planner : Structures lecture con tent into temp orally coherent flows and retriev es visual assets from curated databases [7] 2. Co der : Conv erts storyboards into Python implementations with scop e-guided auto-fix mec hanisms [7] 3. Critic : Utilizes Vision-Language Mo dels (VLMs) to refine spatial lay out and visual clarit y [7] 4.2.2 Visual Anchor Prompting The Critic agent implements “Visual Anc hor Prompting,” a nov el technique that conv erts contin- uous visual information into discrete grid references to facilitate spatial reasoning by VLMs [7]. The pro cess ov erla ys a 10 × 10 grid on rendered frames, enabling precise identification of element p ositions and p oten tial o cclusions. When spatial ov erlap exceeds defined thresholds, the Critic generates refactoring suggestions for Python p ositioning co de [7]. 4.2.3 T eac hQuiz Ev aluation Metric Co de2Video in tro duces T eachQuiz, a metric quantifyin g kno wledge transfer effectiveness [7]. The ev aluation proto col in volv es: 1. T raining a VLM to “unlearn” domain-specific facts 2. Exp osing the mo del to generated educational videos 3. Measuring fact recov ery through targeted quizzes Empirical results demonstrate that agent-generated videos achiev e 40% gains in knowledge transfer efficiency compared to baseline co de generation mo dels, with certain categories surpass- ing human-crafted tutorials [7]. 5 Demonstrating Skill A cquisition Applying the extraction metho dology to TEA and Co de2Video rep ositories yields a suite of reusable skills for next-generation “Visual T utor” agen ts. This section presen ts tw o exemplar skills demonstrating the transformation from rep ository-sp ecific co de to standardized skill arti- facts. 5.1 Skill 1: Visual Theorem W alkthrough This skill enables agents to generate Manim-based animations explaining mathematical or physics theorems through step-by-step visual narratives. 5.1.1 F ron tmatter Sp ecification name: visual-theorem-walkthrough description: Generate Manim animation explaining STEM theorems with synchronized narration and visual proofs version: 1.0.0 trigger: ["visualize theorem", "animate proof", "mathematical explanation video"] dependencies: ["manim", "manim-voiceover"] 7 5.1.2 Lev el 2 Instructions (Excerpt) The extracted pro cedural logic mandates: 1. Generate “Scene Plan” defining co ordinate plane lay out, mathematical ob jects ( Mobjects ), and narrative script [6] 2. Implemen t temp oral sync hronization b et w een visual transitions and narration using manim-voiceover [6] 3. Apply error-correction lo op for Manim API compliance 4. V alidate scene coherence through storyb oard-co de consistency chec ks 5.1.3 Lev el 3 Resources Bundled resources include: • T emplate scripts for common theorem t yp es (geometric pro ofs, algebraic deriv ations) • Reference guide for Manim lay out b est practices • Example storyb oards demonstrating effective visual sequencing This skill encapsulates TEA’s core visualization metho dology in a p ortable, reusable format [6, 16]. 5.2 Skill 2: Visual La yout Critic This skill implements automated quality assessment for visual outputs, enabling agent s to iter- ativ ely refine spatial organization. 5.2.1 F ron tmatter Sp ecification name: visual-layout-critic description: Evaluate rendered visuals for spatial clarity, text readability, and element occlusions version: 1.0.0 trigger: ["review layout", "check visual quality", "refine positioning"] dependencies: ["vision-language-model", "PIL"] 5.2.2 Lev el 2 Instructions (Excerpt) The Visual Anchor Prompting workflo w: 1. Ov erlay 10 × 10 co ordinate grid on screenshot 2. Iden tify grid p ositions of primary visual elements 3. Calculate pairwise spatial ov erlap using grid co ordinates 4. If ov erlap exceeds threshold τ ov erlap , generate p ositioning refactoring suggestions 5. Apply suggestions and re-render for v alidation 8 5.2.3 Refactoring T emplates The skill includes co de templates for common lay out adjustments: # Template: Shift overlapping label original: label.next_to(object, UP) refactored: label.next_to(object, RIGHT) This skill op erationalizes Co de2Video’s Critic metho dology , enabling any agent to p erform sophisticated visual quality assessmen t [7]. 6 Benc hmarking and Ev aluation F ramew ork Rigorous assessmen t of acquired skills requires multi-dimensional ev aluation framew orks encom- passing safety , completeness, executability , main tainability , and p edagogical effectiv eness [1, 18]. 6.1 Multi-Dimensional Ev aluation Metrics T able 3 presents a comprehensive metric taxonomy for skill assessment. T able 3: Multi-Dimensional Ev aluation Metrics for Agent Skills Dimension Metric Description Benc hmark Safet y V ulnerabilit y Rate P ercentage of skills with injec- tion or filesystem abuse risks [1, 3] Static Analysis Completeness F eature Cov erage Exten t of API parameter do c- umen tation co verage [18, 20] Do c Mapping Executabilit y Success Rate Probabilit y of successful task completion [3, 6] TEB / MMMC Main tainability Schema Drift Robustness to API changes [15, 18] Regression T ests P edagogy T eac hQuiz Score Kno wledge transfer effective- ness [7] T eac hQuiz 6.2 Empirical P erformance Results Application of these metrics to the Co de2Video pip eline rev ealed that the complete Planner- Co der-Critic architecture achiev es 40% improv emen t in knowledge transfer efficiency compared to baseline co de generation mo dels [7]. The o3-mini agent implementati on in TEA demonstrated an ov erall score of 0.77 on TheoremExplainBenc h, establishing state-of-the-art p erformance for m ultimo dal scientific reasoning [6]. 6.3 Skill Consolidation through SkillNet As skill libraries scale to h undreds of thousands of artifacts, unified consolidation mechanisms b ecome essential [18, 19]. SkillNet structures skills within an ontological framework establishing relational connections such as “is-a-subset-of ” and “requires-output-from” [18, 19]. This consoli- dation enables: • 30% reduction in execution steps through skill comp osition 9 • 40% improv emen t in av erage task rewards across diverse backbone mo dels • Automated detection of redundant or o verlapping skills The on tological approach transforms skill libraries from flat collections into hierarc hical kno wledge graphs supp orting sophisticated reasoning and planning [18, 19]. 7 Securit y and Go v ernance Automated skill extraction from public rep ositories introduces significant securit y risks, as the pro cess ma y inadverten tly incorporate malicious code or insecure patterns [3, 16]. A compre- hensiv e survey of communit y-distributed skills iden tified vulnerabilities in 26.1% of analyzed artifacts, including data exfiltration attempts and privilege escalation vectors [3]. 7.1 F our-Stage V erification Pip eline T o mitigate these risks, we prop ose a tiered v erification framework categorizing skills in to trust lev els [1, 3]: 7.1.1 G1: Static Analysis Initial automated scanning for: • Suspicious string patterns (e.g., eval() , exec() ) • Unauthorized netw ork calls • Destructive filesystem op erations • Obfuscated co de segments 7.1.2 G2: Seman tic Classification LLM-based analysis verifying: • Instruction-purp ose alignmen t • Absence of hidden prompt injections • Consistency b et ween metadata and implementation 7.1.3 G3: Beha vioral Sandb o xing Execution of bundled scripts in isolated containers with: • Netw ork isolation • Restricted filesystem access • Resource usage monitoring • Pre-configured dep endency environmen ts 10 7.1.4 G4: P ermission V alidation V erification against p ermission manifests ( allowed-tools ) ensuring skills access only required resources [1, 10]. This graduated v erification framework enables skills to ev olv e through trust tiers based on successful, audited runtime performance [3]. T reating skill installation with securit y rigor comparable to softw are pack age managemen t is essential for pro duction deploymen t [3, 16, 21]. 8 The F uture Agen tic Stac k The agen t skills paradigm constitutes a critical la yer in an emerging agen tic tec hnology stac k [1, 2]. This stack architecturally distinguishes b et ween pro cedural intelligence (Skills) and system connectivit y (Mo del Context Proto col) [2]. T able 4 compares these complementary architectural lay ers. T able 4: The Agen tic Stack—Comparison of Complementary La yers Dimension Agen t Skills Mo del Con text Protocol Primary Role Pro cedural Knowledge (“What to do”) T o ol Connectivity (“Ho w to con- nect”) [1, 2] Storage Unit Directory with SKILL.md Serv er with JSON-RPC end- p oin ts [2, 10] State Mo difica- tion Con text + System Permissions A v ailable T o ols + External Data [2] P ersistence Filesystem-based (Durable) Session-based (Runtime) [2] Op erational Na- ture Kno wledge / Pro cedural Connectivit y / Action [10] This arc hitectural orthogonality enables skills to pro vide domain intelligence for Mo del Con- text Proto col to ols [2]. F or example, a “Presentation Skill” might define b est practices for slide rh ythm and lay out while utilizing a “Po w erPoin t MCP Serv er” for actual do cumen t manipula- tion [2, 22]. 8.1 Ev olution Agen ts and Con tin uous Impro v ement The ecosystem tra jectory suggests emergence of “Evolution Agen ts” that autonomously mine con versation logs and execution traces to refine existing skills [13, 22]. By extracting user prefer- ences and identifying recurring failure patterns, these agen ts will augmen t extracted skills with p ersonalized adaptations [22]. The Visual T utor deriv ed from TEA and Co de2Video can th us con tinuously adapt to sp ecific learner needs and educational contexts. The transition from monolithic, static intelligence to ward mo dular, evolving exp ertise repre- sen ts a fundamental shift in AI system design, with automated mining of op en-source rep ositories serving as the primary scalability mechanism [2, 20]. 9 F requen tly Ask ed Questions 9.1 Ho w do es skill extraction differ from mo del fine-tuning? Skill extraction separates pro cedural kno wledge from mo del parameters, enabling capability up- dates without retraining. This approach reduces computational costs b y 2-3 orders of magnitude while maintaining up date flexibility . 11 9.2 Can extracted skills work across differen t LLM pro viders? Y es. The SKILL.md standard is provider-agnostic, con taining natural language instructions in terpretable b y an y sufficien tly capable language model. Provider-specific optimizations ma y b e included as optional metadata. 9.3 What prev en ts skills from con taining malicious co de? The four-stage verification pip eline (G1-G4) implemen ts m ultiple securit y la yers including static analysis, semantic v erification, sandb o xed execution, and p ermission v alidation. Skills adv ance through trust tiers based on verified safe op eration. 9.4 Ho w are skill conflicts resolv ed when m ultiple skills match a query? Agen t orc hestration framew orks typically implement priority systems based on skill sp ecificit y , historical success rates, and explicit user preferences. Some systems use meta-reasoning to select optimal skill combinations. 9.5 What is the practical upp er limit for skill library size? Progressiv e disclosure architecture enables agents to maintain aw areness of 10,000+ skills while loading only activ ated instructions in to con text. The primary constrain t is organizational rather than technical—effectiv e skill discov ery requires robust ontological structuring. 10 Conclusion This rep ort has demonstrated that systematic extraction of pro cedural knowledge from GitHub’s op en-source agentic rep ositories enables scalable acquisition of high-quality agent skills. By implemen ting structured framew orks encompassing rep ository analysis, semantic iden tification through dense retriev al, and standardized translation to the SKILL.md format, the AI com- m unity can construct mo dular systems combining the general reasoning capabilities of large language mo dels with sp ecialized domain exp ertise. The detailed analysis of TheoremExplainAgent and Co de2Video establishes that executable co de serves as an optimal substrate for enco ding b oth visual and p edagogical exp ertise. Through rigorous b enc hmarking demonstrating 40% kno wledge transfer improv emen ts and multi-dimensional ev aluation frameworks ensuring safety and maintainabilit y , we ha ve shown that extracted skills can match or exceed human-authored conten t quality while dramatically improving scalability . The future of artificial intelligence lies not in ever-larger monolithic mo dels but in comp os- able, go vernable, and contin uously evolving skill ecosystems. Automated mining of op en-source rep ositories, com bined with robust security gov ernance and on tological organization, provides the foundation for this arc hitectural transition. As the agen tic stac k matures through integra- tion of complementary technologies such as Mo del Context Proto col and Evolution Agents, the vision of truly autonomous, exp ert-level AI systems approaches practical realization. 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