Skill-uri pentru agenți pe care le poți verifica înainte să le instalezi
Skill-uri SKILL.md publice din depozite GitHub cu peste 50 de stele și de la producători, scanate și evaluate. Instalează oricare dintre ele cu o singură comandă în Claude Code, Cursor, Codex, GitHub Copilot sau Gemini CLI.
48.015 skill-uri din 2.309 depozite · actualizat acum 31 de minute
6.173 de skill-uri
- summer-apiNouSummerEngine/summerSummer API game assets: models, estimate, one job or a batch, download the files.InginerieFără avertismente★ 90↓ —
- code-pattern-extractorArabelaTso/Skills-4-SEAnalyze codebases to identify reusable code patterns, duplications, and implementation patterns for future development. Use when refactoring code, identifying technical debt, finding opportunities for abstraction, or documenting common patterns in a directory or module. Outputs pattern catalogs, refactoring suggestions, and reusable template code.InginerieFără avertismente★ 260↓ —
- code-explainerAdyen/agent-marketplaceExplains code snippets in plain languageInginerieFără avertismente★ 85↓ —
- github-repo-statscxcscmu/SkillLearnBenchHow to gather GitHub repository statistics (PRs, issues, contributors) using the GitHub REST API via curl and Python. Use this skill whenever the user asks for a community pulse, activity summary, monthly report, or repository metrics for any GitHub repo — even if they don't explicitly mention the API.InginerieFără avertismente★ 91↓ —
- python-pillow-graphicscxcscmu/SkillLearnBenchCreate technical graphics, diagrams, and posters using Python Pillow library with precise color control and typography.InginerieFără avertismente★ 91↓ —
- template-code-generatorArabelaTso/Skills-4-SEGenerate boilerplate code and project templates/skeletons automatically. Use when: (1) Creating new projects from scratch (React app, FastAPI backend, Express API), (2) Generating repetitive code patterns (CRUD endpoints, models, controllers), (3) Scaffolding components, services, or modules, (4) Creating test boilerplate, (5) Setting up monorepo structures. Provides project templates and code generation patterns for common development tasks.InginerieFără avertismente★ 260↓ —
- jq-data-processingcxcscmu/SkillLearnBenchProcess JSON data from GitHub CLI using jq to calculate metrics like average merge times, counts, and top contributors.InginerieFără avertismente★ 91↓ —
- c-cpp-to-lean4-translatorArabelaTso/Skills-4-SETranslate C or C++ programs into equivalent Lean4 code, preserving program semantics and ensuring the generated code is well-typed, executable, and can run successfully. Use when the user asks to convert C/C++ code to Lean4, port C/C++ programs to Lean4, translate imperative code to functional Lean4, or create Lean4 versions of C/C++ algorithms.InginerieFără avertismente★ 260↓ —
- weekly-reviewaAAaqwq/AGI-Super-TeamAn installable, cross-framework AI organization: C-suite agents, expert subagents, curated skills, independent review, and one-command setup across 18 AI client/runtime adapters.InginerieFără avertismente★ 112↓ —
- full-output-enforcementadriannoes/awesome-agentic-aiOverrides default LLM truncation behavior. Enforces complete code generation, bans placeholder patterns, and handles token-limit splits cleanly. Apply to any task requiring exhaustive, unabridged output.InginerieFără avertismente★ 91↓ —
- cpp-to-dafny-translatorArabelaTso/Skills-4-SETranslate C/C++ programs to equivalent Dafny code while preserving semantics and ensuring verification. Use when users ask to convert, translate, or port C/C++ code to Dafny, or when they need to formally verify C/C++ algorithms using Dafny's verification capabilities. Handles functions, structs, pointers, arrays, memory management, and ensures the generated Dafny code is well-typed, executable, verifiable, and can successfully run.InginerieFără avertismente★ 260↓ —
- cli-ghPaulRBerg/agent-skillsUse for GitHub CLI automation: repository reads, workflow runs, search, codespaces, releases, configuration, or gh command syntax. Use yeet for contribution writes to PRs, issues, comments, or discussions.InginerieFără avertismente★ 103↓ —
- change-log-generatorArabelaTso/Skills-4-SEAutomatically generates change logs from git commits, patches, and pull requests. Use when preparing software releases, creating version summaries, or maintaining CHANGELOG.md files. Analyzes commit messages (including conventional commits), diff/patch files, and PR data to produce categorized Markdown change logs organized by type (Features, Bug Fixes, Breaking Changes, etc.). Ideal for release notes, version updates, and automated changelog maintenance.InginerieFără avertismente★ 260↓ —
- aai-openpanelaai-labs/agent-barnUse aai-cli to read OpenPanel projects, raw event exports, aggregated insights (metrics, pages, referrers, devices, geo), and user profiles.InginerieFără avertismente★ 110↓ —
- comic-asset-review-loopwanshuiyin/ARIS-Movie-DirectorPhase-1 (S5) UPSTREAM ref-asset gate — the bounded cross-model adversarial loop that LOCKS one reusable identity-locked asset (character sheet / location plate / prop cutout / text-panel / logo-free symbol) BEFORE it can be composited into any panel. This is NOT the panel_gate (composed-output) or the assembly_gate (cross-panel) — it gates the refs that FEED both, so identity drift is caught at the source, not downstream. Two layers: (1) a MANDATORY static single-source collision check (check_asset_collisions.py — 'one visual dialect, never two', enforced by tool) + zero-text/literal build-asserts; (2) a per-round 3-reviewer panel (Claude narrative ‖ Gemini visual ‖ Codex synth) that blind-scores 5 dims and LOCKS only at 准×3 unanimity (prior_lock_count>=3 approvals in the SAME round) — else regenerate (route to comic-asset-ref-generator) or, at MAX_REVIEW_ROUNDS, escalate the asset REQUIREMENT back to outline/storyboard. Hard IP veto. Use when you say 'lock this character ref', 'asset gate', 'ref review', 'isInginerieFără avertismente★ 74↓ —
- counterexample-debuggerArabelaTso/Skills-4-SEDebug proof failures using counterexamples from Nitpick (Isabelle) or QuickChick (Coq) to identify specification errors, missing preconditions, and proof strategy issues. Use when: (1) A proof attempt fails and you need to understand why, (2) Counterexamples are generated by Nitpick or QuickChick, (3) Specifications may be incorrect or incomplete, (4) Theorems need validation before proving, (5) Missing preconditions or lemmas need identification, or (6) Proof failures need explanation and correction suggestions. Supports both Isabelle/HOL and Coq equally.InginerieFără avertismente★ 260↓ —
- scala-adt-enumscxcscmu/SkillLearnBenchTranslating Python Enums and Protocols to Scala sealed traits and algebraic data typesInginerieFără avertismente★ 91↓ —
- principle-fix-root-causesadriannoes/awesome-agentic-aiApply when debugging. Trace each symptom to its root cause and fix it there; reproduce first, ask why until you reach it, resist nil-check guards that silence crashes.InginerieFără avertismente★ 91↓ —
- example-threejsBintzGavin/heliosPatterns for using Helios with Three.js and React Three Fiber. Use when creating 3D animations or integrating WebGL scenes.InginerieFără avertismente★ 123↓ —
- react-state-and-effects-disciplineTheGoat395/Codex-SkillsRepair state ownership, effects and async races.InginerieFără avertismente★ 135↓ —
- run2_gh-search-apicxcscmu/SkillLearnBenchHow to query the GitHub Search API for PRs and issues with date filtering, handling pagination and unauthenticated access via Python urllib.InginerieFără avertismente★ 91↓ —
- slackhyperfx-ai/marketing-skillsSlack messaging, file sharing, Block Kit formatting, and channel management. Use when the user wants to send Slack messages, post rich Block Kit layouts, share files, react, or manage channels and members.InginerieFără avertismente★ 107↓ —
- example-vanillaBintzGavin/heliosPatterns for using Helios with Vanilla JavaScript/TypeScript. Use when building compositions without a framework, or for simple DOM/Canvas manipulations.InginerieFără avertismente★ 123↓ —
- code-instrumentation-generatorArabelaTso/Skills-4-SEAutomatically instruments source code to collect runtime information such as function calls, branch decisions, variable values, and execution traces while preserving original program semantics. Use when users need to: (1) Add logging or tracing to code for debugging, (2) Collect runtime execution data for analysis, (3) Monitor function calls and control flow, (4) Track variable values during execution, (5) Generate execution traces for testing or profiling. Supports Python, Java, JavaScript, and C/C++ with configurable instrumentation levels.InginerieFără avertismente★ 260↓ —