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.
45.213 skill-uri din 2.196 de depozite · actualizat acum 33 de minute
45.213 skill-uri
- customizeaipoch/open-scienceUse when the user wants to create or manage a Specialist agent or create, revise, publish, or delete a Skill through the conversational `/Customize` entry. Routes Skill work to the internal skill-creator and handles Specialist work through the JavaScript host.agents SDK.PlanificareFără avertismente★ 5,4 K↓ —
- cloud-integrationsgrafana/skillsSet up, configure, and troubleshoot Grafana Cloud integrations for AWS, Azure, and other cloud providers. Use when the user asks to connect AWS CloudWatch, set up Azure Monitor, configure Confluent Cloud observability, install a Grafana integration, set up hosted exporters, use AWS Firehose for CloudWatch logs, or troubleshoot a cloud integration. Triggers on phrases like "AWS CloudWatch", "Azure Monitor", "Confluent integration", "cloud integration", "hosted exporter", "AWS Firehose", "install integration", "cloud metrics", or "cloud logs".OficialDevOpsFără avertismente★ 290↓ —
- mirrord-chaosmetalbear-co/mirrordHelp users chaos test their app with mirrord: inject artificial latency or connection errors into a mirrord session's outgoing traffic via per-session chaos rules managed with the `mirrord chaos` CLI. Use when a user wants to add latency or delay to outgoing connections or a dependency (e.g. a slow database), simulate connection failures (reset, timed out, refused), test app behavior under degraded network conditions, or wire chaos rules into CI test runs. Always use this skill instead of the deprecated _experimental_.latency mirrord config option.TestareFără avertismente★ 5,4 K↓ —
- mongodb-natural-language-queryingmongodb/agent-skillsGenerate read-only MongoDB queries (find) or aggregation pipelines using natural language, with collection schema context and sample documents. Use this skill whenever the user asks to write, create, or generate MongoDB queries, wants to filter/query/aggregate data in MongoDB, asks "how do I query...", needs help with query syntax, or discusses finding/filtering/grouping MongoDB documents. Also use for translating SQL-like requests to MongoDB syntax. Does NOT handle Atlas Search ($search operator), vector/semantic search ($vectorSearch operator), fuzzy matching, autocomplete indexes, or relevance scoring - use search-and-ai for those. Does NOT analyze or optimize existing queries - use mongodb-query-optimizer for that. Does NOT handle aggregation pipelines that involve write operations. Requires MongoDB MCP server.OficialDateFără avertismente★ 191↓ —Apare și în 3 alte depozite
- solublempnnaipoch/open-scienceInverse-fold a backbone with SolubleMPNN — ProteinMPNN retrained on a soluble-PDB subset (Dauparas et al. 2022) — for sequences biased toward cytosolic expression and reduced aggregation. Reach for this skill when designs from vanilla ProteinMPNN are aggregating or going to inclusion bodies, when redesigning a membrane-adjacent fold for soluble expression, or when an E. coli expression screen is the next step.AlteleFără avertismente★ 5,4 K↓ —
- reconciler-logicgrafana/skillsImplement reconcilers and watchers for grafana-app-sdk apps — write `TypedReconciler[*MyKind]` reconcile functions, apply generation-based skip patterns, do conflict-safe status updates via `resource.UpdateObject`, configure `BasicReconcileOptions` (namespace, label/field filters, finalizer management), use `Watcher` for event-style handling, reconcile `UnmanagedKinds` (resources your app doesn't own), and register the whole thing in `app.go`. Use when writing a reconciler, implementing the reconcile loop, adding async business logic, handling create/update/delete events, processing resource state changes, scheduling periodic resyncs with `RequeueAfter`, picking between Watcher and Reconciler, or wiring a controller into `app.go` — even when the user says "process this resource", "handle X events", or "write a controller" without saying "reconciler".OficialRedactareFără avertismente★ 290↓ —
- nansen-holder-analysisinternet-court/internet-court-skillIs this token held by quality wallets or retail noise? SM holder ratio, flow breakdown by label, and recent buyer quality.AlteleFără avertismente★ 7 K↓ —
- add-providerThinkInAIXYZ/deepchatAdd a DeepChat LLM provider through explicit reviewed source changes. Use when a developer asks Codex to add a provider, provider profile, upstream provider config, model catalog mapping, provider auth behavior, or a special provider adapter in this repository.PlanificareFără avertismente★ 6,4 K↓ —
- k6-managegrafana/skillsInteract with Grafana Cloud k6 (GCk6) — manage load tests, test runs, scripts, projects, schedules, env vars, fetch metrics or logs, and run scripts locally — using the `gcx` CLI (or direct curl when gcx is unavailable). Use this skill whenever the user mentions a k6 cloud test or run, asks to list/edit/create/start/abort k6 load tests, wants to fetch logs or metrics for a test run, manage k6 project limits or schedules, run a k6 script locally or via `k6 cloud run`, or needs to call any `/cloud/v6/`, `/cloud/v5/`, or k6-app Loki endpoint against a Grafana Cloud stack. Trigger even when the user doesn't explicitly say "gcx" or "API" — phrases like "why did my k6 test fail", "show me logs for run X", "bump VUh limit on project Y", or "update my k6 script" all qualify.OficialTestareFără avertismente★ 290↓ —
- dpm-findergrafana/skillsFind the Prometheus metrics that drive your Grafana Cloud bill. `dpm-finder` is a Grafana Professional Services CLI that ranks metrics by Data Points per Minute (DPM) with per-label-set breakdown, optional `--cost-per-1000-series` pricing, and a Prometheus-exporter mode. Use when investigating high Grafana Cloud spend, hunting noisy / high-cardinality metrics, comparing pre/post recording-rule cardinality, or feeding cost data into dashboards — even when the user says "why is my Mimir bill so high?", "find the biggest metrics", "cardinality offenders", or "optimize Prometheus cost" without naming dpm-finder.OficialFinanțeFără avertismente★ 290↓ —
- pyroscopegrafana/skillsContinuously profile applications with Grafana Pyroscope and read the result as flame graphs. Covers three instrumentation paths — language SDK push (Go / Java / Python / Ruby / Node / .NET / Rust), Alloy eBPF auto-instrumentation (no code change, requires kernel 5.8+ with BTF), and SDK → Alloy receiver — plus ProfileQL queries, profile types (CPU / memory / allocations / goroutines / mutex), Grafana Cloud Profiles endpoint, and Span Profiles trace-to-profile linking. Use when adding profiling to a service, deploying Alloy as a cluster-wide eBPF profiler, hunting CPU / memory hotspots from a flame graph, comparing two profiles to find a regression, or correlating a slow Tempo trace to its profile — even when the user says "find what's burning CPU", "flame graph this app", "continuous profiling", "heap hotspots", or "why is allocation so high" without naming Pyroscope.OficialDevOpsFără avertismente★ 290↓ —
- promqlgrafana/skillsWrite, validate, and optimize PromQL for Prometheus / Grafana Mimir / Grafana Cloud Metrics. Covers `rate` vs `irate` vs `increase`, label matchers and regex, `sum / avg / topk / by / without` aggregation, classic + native `histogram_quantile`, ratios with divide-by-zero guards, `absent` / `changes` for staleness, time offsets and `predict_linear`, recording-rule naming, SLO + burn-rate math, and a cardinality-hunting playbook. Use when writing a metric query, fixing wrong p95s, building an error-budget alert, debugging "query is slow", finding the noisy label that blew up cardinality, or migrating a dashboard query to a recording rule — even when the user says "calculate the error rate", "p99 latency", "sum by service", "why is this query slow", or "what's filling Mimir" without naming PromQL.OficialRedactareFără avertismente★ 290↓ —
- melonjs-tilemapsmelonjs/melonJSUse this skill for Tiled maps in melonJS — loading TMX/TSX levels, spawning entities from Tiled objects, collision shapes authored in Tiled, isometric and hexagonal maps, and image layers. Covers registerTiledObjectClass and the pool.register name contract, camera bounds, compressed maps needing the inflate plugin, and the level director API. Triggers on: Tiled, TMX, TSX, tilemap, level.load, level.load async, await level.load, tileset, ImageLayer, isometric, hexagonal, staggered, pool.register, Collectable, Trigger, object layer, collision layer, parallax, registerTiledObjectClass, registerTiledObjectFactory, TMXObject, resolvedProperty, resolvedProperties, tile class, tile properties.MediaFără avertismente★ 6,4 K↓ —
- synthetic-monitoring-checksgrafana/skillsAuthor Grafana Cloud Synthetic Monitoring checks, with deep coverage of k6 scripted and browser checks: SM's single-VU/single-iteration execution model, assertions that actually fail probe_success (expect() and fail() vs bare check()), secrets, deterministic scripts, robust browser locators, local validation with k6 run, deployment via UI/API/Terraform, verifying probe_success, and rollback. Also helps choose the simplest sufficient check type (HTTP/ping/DNS/TCP, MultiHTTP, scripted, browser). Use when writing a synthetic check, monitoring a login/checkout/signup flow in production, converting a k6 script or an OpenAPI spec into a check, authoring a browser check, validating a user journey, or asking "is my site up from multiple regions". NOT for load, stress, or performance testing — SM runs one iteration per execution; for load tests use the grafana-k6 plugin or Grafana Cloud k6. For the broad Grafana Cloud Testing overview (SM + k6 Cloud + Faro), use the testing skill.OficialTestareFără avertismente★ 290↓ —
- indication-dossieraipoch/open-scienceGenerate a therapeutic indication dossier. Covers the patient population, epidemiology, disease biology, standard of care, regulatory precedent, and landmark clinical trials.ȘtiințăFără avertismente★ 5,4 K↓ —
- fair-esm2aipoch/open-scienceEmbed proteins with Meta AI's ESM-2 (`fair-esm` package). Use this skill when: (1) Extracting per-residue or per-sequence embeddings for downstream ML, (2) Masked-LM likelihood / mutation effect scoring, (3) Contact prediction from a sequence.AlteleFără avertismente★ 5,4 K↓ —
- openfold3aipoch/open-scienceStructure prediction using OpenFold3, an open-weights PyTorch reproduction of AlphaFold3 from the AlQuraishi Lab. Use this skill when predicting protein/nucleic-acid/ligand complex structures with an Apache-2.0-licensed AF3 reimplementation.ȘtiințăFără avertismente★ 5,4 K↓ —
- melonjs-renderablesmelonjs/melonJSUse this skill when subclassing Renderable, writing a custom draw() method, handling pointer or click events on game objects, applying post effects and shaders, or controlling draw order. Covers the pos-relative draw contract, isKinematic and input, addPostEffect vs the deprecated shader property, and z-ordering. Triggers on: Tween, easing, animate, Renderable, extends Renderable, draw(, update(, isKinematic, pointerEvent, registerPointerEvent, onClick, addPostEffect, removePostEffect, ShaderEffect, addChild, z-order, anchorPoint, floating.MediaFără avertismente★ 6,4 K↓ —
- deepchat-sddThinkInAIXYZ/deepchatUse before substantial DeepChat code, configuration, documentation, test, build, feature, issue, refactor, or architecture changes that need a durable RFC and an explicit execution path. Skip trivial style fixes, small localized logic changes, routine docs edits, and simple bugs unless the developer asks for SDD. Use plan.md as the only separate tracker when needed, default to implementation-first validation, and ask before optional GitHub issue sync unless the developer explicitly requested sync.TestareFără avertismente★ 6,4 K↓ —
- deepchat-cliThinkInAIXYZ/deepchatUse DeepChat's bundled CLI control plane for model inference, image/video/speech generation, transcription, OCR, artifact inspection, public configuration, Skills, and MCP operations. Activate when a user asks to invoke DeepChat capabilities that are not already exposed as a more specific tool, compare models, run a benchmark, inspect DeepChat runtime state, or manage DeepChat through the CLI.InginerieFără avertismente★ 6,4 K↓ —
- lambda-labs-gpu-cloudOrchestra-Research/AI-Research-SKILLsReserved and on-demand GPU cloud instances for ML training and inference. Use when you need dedicated GPU instances with simple SSH access, persistent filesystems, or high-performance multi-node clusters for large-scale training.AlteleFără avertismente★ 14 K↓ —
- sentencepieceOrchestra-Research/AI-Research-SKILLsLanguage-independent tokenizer treating text as raw Unicode. Supports BPE and Unigram algorithms. Fast (50k sentences/sec), lightweight (6MB memory), deterministic vocabulary. Used by T5, ALBERT, XLNet, mBART. Train on raw text without pre-tokenization. Use when you need multilingual support, CJK languages, or reproducible tokenization.ProductivitateFără avertismente★ 14 K↓ —
- esmfold2aipoch/open-scienceBiohub ESMFold2 / ESMFold2-Fast all-atom co-folding (Candido et al. 2026, github.com/Biohub/esm). Single-sequence and MSA modes; protein, DNA, RNA, ligand (CCD/SMILES), modified residues. FoldBench Ab-Ag 50-55%, PPI 70-77% DockQ-pass. Also covers the ESMC-{300M,600M,6B} protein language models from the same release: masked-LM logits, hidden states, mutation scoring, contact prediction, and the SAE interpretability head. MIT-licensed weights on HuggingFace org `biohub`. Use this skill when: (1) Predicting complex structures with single-sequence input, (2) Validating designed binders with ESMFold2-Fast, (3) Running ESMFold2 with MSA input, (4) Getting ESMC embeddings or per-residue mutation scores, (5) Choosing kernel backend and sampling-step settings for paper-faithful throughput.ȘtiințăFără avertismente★ 5,4 K↓ —
- dowhybrycewang-stanford/Auto-Empirical-Research-SkillsCausal inference framework for answering "does X cause Y?" beyond correlation. DoWhy (Microsoft Research) provides the identify-estimate-refute loop: define a causal graph (DAG), identify the causal effect using backdoor/frontdoor/instrumental variable criteria, estimate treatment effects with multiple estimators, and validate results with automated refutation tests. Use when: distinguishing causation from correlation, estimating treatment effects (ATE, ATT, CATE), designing and analyzing A/B tests with confounders, using instrumental variables, performing counterfactual reasoning ("what would have happened if..."), validating causal claims with sensitivity analysis, working with observational data where randomization is impossible, or any analysis where the question is "what is the CAUSAL effect of X on Y" rather than just "how do X and Y relate?"TestareFără avertismente★ 4,8 K↓ —
humanize-redo
epoko77-ai/im-not-ai
RedactareActualizat 12 oct. 2026
De la autor
가장 최근 윤문 결과를 2차로 다시 다듬는다 — 특정 카테고리·문단·강도 조정도 가능. humanize-korean strict 윤문(Phase B)을 기존 run_id에 재실행해 잔존 finding을 처리한다. 트리거 — "/humanize-redo".
Instalare
npx skills add epoko77-ai/im-not-ai --skill humanize-redo -a claude-code
Se instalează în .claude/skills/humanize-redo/ · Fiecare comandă descarcă skill-ul direct de pe GitHub cu utilitarul open source skills.
Descarcă .zip
Identic octet cu octet cu commit-ul scanat, cu licența depozitului și un fișier SOURCE.txt.
La ce are acces
Doar instrucțiuniNu am văzut acces la rețeaLicență: MIT
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De ce 91
Securitate100
Proveniență85
Întreținere100
Licență100
Adopție60
Fișiere (1)
- SKILL.md