Genel Claude Code pazaryerlerinden 43 eklenti. Her biri birlikte yüklenen beceriler, eğik çizgi komutları, ajanlar, kancalar veya MCP sunucuları içerir.
Bu filtrelere uyan 43 eklenti gösteriliyor.
claude-code-workflowswshobson/agents2
claude-code-skillsalirezarezvani/claude-skills2
research-agent-skillsKalarisLabs/research-agent-skills2
medsci-skillsAperivue/medsci-skills1.claude-plugin/marketplace.json içeren depoyu gönderin, biz de her plugini listeleyelim.
Marketplace gönderEval-gated LLM fine-tuning lifecycle (LoRA/QLoRA, DPO, GRPO/RLVR, vision SFT, quantized export). Use when fine-tuning an open-weights model end-to-end: building the eval harness first, selecting method and model, preparing data, training with Unsloth or TRL, gating checkpoints, and exporting GGUF/FP8.
ML model training pipelines, hyperparameter tuning, model deployment automation, experiment tracking, and MLOps workflows
Study companion for the Deep Learning textbook by Goodfellow, Bengio & Courville (MIT Press, 2016), free to read at deeplearningbook.org. Twenty chapter files, a glossary, patterns and a cheatsheet index the whole book, and a delta reference dates it against 2026 practice with per-claim confidence levels: double descent qualifying the U-curve, AdamW splitting weight decay from L2, transformers displacing Chapter 10's recurrence, diffusion growing out of Chapter 18's score matching. Four stdlib tools make it executable — a prerequisite-closed reading-path planner that refuses goals the book does not cover, a training diagnostic running Chapter 11's rules in priority order so a NaN is never reported as overfitting, a capacity planner that ranks 'shrink the model' last when overparameterized, and a parameter/FLOP/activation-memory calculator that refuses a stack whose shapes do not connect. A companion, not a compilation: the book is copyrighted, so nothing here reproduces its text.
Machine learning experiments, training, evaluation and reproducible papers.
researchModel architectures, tokenization, fine-tuning, post-training, distributed training, optimization.
research30 skills for L&D and corporate training, from the first training request to proof of what changed: finding the real need, getting it out of the experts, designing the programme, building the materials, delivering it live, making it stick, proving what changed, and running the L&D desk.
productivityThe Complete Claude Guide for L&D: 40 Claude skills for the whole L&D job, in eight stages: diagnose the need with the request triage, the performance gap and the action map; work with the experts through the interview, the task breakdown and the review plan; design the learning with objectives, an evaluation plan, a design document and a curriculum map; build and test scenarios, a storyboard, a knowledge check, the check of an AI draft and a learner walkthrough; run the room with a session plan, slides, a facilitator guide and a pilot; carry it into work with a job aid, spaced practice and a manager briefing; measure what changed with a feedback form, an ROI estimate and the one-page report; and lead L&D through the year plan and the business case. For L&D managers and specialists, instructional designers, trainers and facilitators, and L&D leaders. Claude drafts and checks, people decide: nothing a learner does in training is used to grade, rank or build a case against them.
productivityClaude Guide for Training Impact: 30 Claude skills for proving what a training programme changed, from the request to the decision about what comes next, in seven stages: question the request with a request brief, sponsor discovery questions and a performance problem diagnosis; agree what proof looks like with a critical behaviours map, a success measures agreement and a baseline plan; collect the evidence with two surveys, an observation checklist, an interview guide and a data request; read it honestly in a before-and-after results table and a contribution analysis; tell the sponsor with success cases, a return as a range and a one-pager; report to the business with a metrics set, a quarterly report and an overclaim check; then feed it back through a review meeting, a decision record and a business case. For L&D managers, L&D specialists and L&D leaders. Claude plans the evaluation and drafts the report from your data; it never invents a result, a benchmark or a quote, and a named pe
productivityClinical AI model research engineering: choose a paper-grounded architecture, scaffold a reproducible PyTorch training repo, validate the model's split and validation design, document it (Model Card / Datasheet), and evaluate models or LLMs/MLLMs on clinical tasks. Integrates MONAI / nnU-Net, never replaces them.
Chief Data Officer advisory for startups: AI training data audit (origin × class × use-case matrix with GDPR Art. 6 + EU AI Act citations), data product strategy picker (warehouse vs lakehouse vs mesh + 6-layer build-vs-buy + 12-month sequencing), data asset valuator (strategic value 0-10 + M&A multiplier with carve-out penalties + 3 ranked productization paths). 4 references answering one decision each: training rights, data product strategy, customer-data-as-asset, data team org evolution. Standalone-installable; also bundled in c-level-skills. Strategic only — does not duplicate engineering data skills.
The restaurants & hospitality pack: menu costing and pricing, health-inspection self-audits, demand-matched shift schedules, front-of-house training, and defensible guest-incident logs
All 30 maintainer skills and 9 agent profiles: humanize and localize prose, triage, reproduce and verify bugs, review PRs, debug ML training, profile performance, bisect regressions, audit CVEs, generate changelogs, evaluate SemVer version bumps, isolate scratchpads, fix type errors, isolate faults, conduct adversarial reviews, route architect-editor edits, generate conventional commits, resolve merge conflicts, prune context, and prepare releases.
productivitySelf-improving robotics expertise for AI agents: stack selection, simulation, visualization, data, testing, and training skills for physical-AI applications
Agent skills for Ultralytics YOLO and Platform: model selection, dataset annotation, training, tuning, inference, export, and deployment through the Platform web UI, the Python package, and the yolo and ul CLIs.
developmentML pipeline design, experiment tracking, model evaluation, training debugging, and reproducibility.
developer toolsSplit datasets for training, validation, and testing
Jurisdiction-aware review of technology and commercial contracts — MSA, DPA, SOW, LOE, NDA, SLA, EULA, IP assignment, vendor/supplier, and API/platform ToS. Classifies every clause (acceptable / negotiate / red-flag / missing), proposes redline replacements, checks governing law and data-protection obligations (GDPR, UK GDPR, India DPDP Act, CCPA), and outputs a structured risk summary. Built for tech and AI deals where training-data, IP-ownership, and liability terms need heightened scrutiny. Not a substitute for qualified legal counsel.
Generates standalone interactive HTML "deal cards" that translate complex regulations into negotiation-ready reference tools, systematically distinguishing mandatory obligations from negotiable implementation choices. Use when the user needs an interactive regulatory guide for (1) contract negotiation support, (2) client education or internal training, (3) regulatory briefings for commercial stakeholders, or (4) structured comparison between required and flexible compliance paths. Primary focus on EU digital regulation (Data Act, AI Act, CRA, DORA, NIS2, GDPR) but the structural pattern transfers to any regulation where separating hard obligations from implementation choice is the point. Supports bilingual output where the jurisdiction calls for it.
Framework for demonstrating AI capabilities in legal contexts. Provides detailed personas across tenant law, business contracts, startup disputes, employment claims, and consumer protection with progressive complexity scenarios. Use when: (1) Demonstrating AI-powered legal triage or intake systems, (2) Showcasing responsible AI-assisted client interactions, (3) Training staff on appropriate AI use in legal contexts, (4) Creating realistic scenarios for legal tech presentations, (5) Developing educational materials about AI in legal services, or (6) Testing AI-powered legal information systems in controlled environments.
Builds a high-fidelity interactive legal assessment as a single self-contained HTML artifact. Output includes a live countdown timer, contract review tasks with hover-annotated problem clauses, candidate answer textareas, model answers hidden behind reveal blocks, scenario-based legal memo tasks, strategy and function-building questions, and a pre-submission checklist that encodes the marking criteria. Use when the user needs to (1) assess a legal candidate with a realistic timed exercise, (2) train or onboard junior lawyers using problem sets rather than doctrine, (3) help a candidate prepare for a real take-home assessment they are facing, (4) build educational materials for law students, in-house teams, or compliance training, or (5) produce scenario-based training modules on specific legal topics. Triggers on "legal test", "take-home", "mock exam", "contract redline exercise", "candidate assessment", "legal training exercise", "practice test", or similar phrasing even when informal
