Agent skills you can check before you install
Public SKILL.md skills from GitHub repositories with 50+ stars and from the vendors themselves, scanned and scored. Install any of them with one command in Claude Code, Cursor, Codex, GitHub Copilot or Gemini CLI.
45,100 skills from 2,196 repositories · updated 7 hours ago
44,137 skills
- bio-database-evidenceforyourhealth111-pixel/Vibe-SkillsUnified biological database evidence owner. Use for gene annotation, variant clinical significance, cancer mutation evidence, GWAS trait associations, pathway mapping, target-disease evidence, protein structures, protein interaction networks, reference single-cell census queries, and cross-database biological ID mapping. Do not use for full single-cell analysis, bulk RNA-seq differential expression, BAM/VCF processing, protein embedding models, metabolic flux modeling, genomic interval ML, or flow-cytometry file parsing.DataNo warnings★ 3.8K↓ —
- doca-dpaNVIDIA/skillsUse this skill when the user is doing hands-on DOCA DPA host-side work on a BlueField — creating the `doca_dpa` Core context, loading a DPACC-compiled DPA app image (`doca_dpa_app`), creating DPA threads, launching kernels via `doca_dpa_kernel_launch_update_*`, draining `doca_dpa_completion`, running `doca_dpa_cap_*` discovery, choosing between the DPA comm component (inter-DPA messaging) and the DPA verbs component (in-kernel RDMA), or debugging `DOCA_ERROR_*` from `doca_dpa_*`. Trigger even without "DOCA DPA" or "Data-Path Accelerator": "run compute on the DPA from my host", "DPA kernel hangs, no completion", "DOCA_ERROR_DRIVER on launch", "DOCA/DPACC version skew", or "does this BlueField expose a DPA". Route elsewhere for DPA-side kernel programming itself, DPACC compiler internals, host↔DPU messaging (doca-comch), host-side RDMA (doca-rdma), and GPU-initiated networking (doca-gpunetio).MediaNo warnings★ 3.7K↓ —
- ml-data-leakage-guardforyourhealth111-pixel/Vibe-SkillsDetects and prevents data leakage in machine learning and mathematical modeling. Use after ML tasks involving data cleaning, feature engineering, data augmentation, algorithm development, normalization, missing value imputation, dimensionality reduction, feature selection, or time series modeling. Checks if features/statistics would be available at prediction time.ProductivityNo warnings★ 3.8K↓ —
- brainstormingforyourhealth111-pixel/Vibe-SkillsYou MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.PlanningNo warnings★ 3.8K↓ —Also in 1 other repo
- gsd:updatedavepoon/buildwithclaudeUpdate GSD to latest version with changelog displayOtherNo warnings★ 3.7K↓ —
- scholarly-publishingforyourhealth111-pixel/Vibe-SkillsUse when planning an end-to-end scholarly publishing workflow, including manuscript source-of-truth, submission assets, revision/rebuttal files, camera-ready checks, reproducible build expectations, and publication package structure.PlanningNo warnings★ 3.8K↓ —
- structured-content-storageforyourhealth111-pixel/Vibe-SkillsEnforces structured, highly documented storage for code and data projects. Use when working on machine learning scripts, data processing, code creation, or script modification that should preserve clear structure and documentation.EngineeringNo warnings★ 3.8K↓ —
- positioning-truth-traceraaron-he-zhu/aaron-marketing-skillsUse when the user asks to "check our positioning against what we can actually ship", "trace which differentiators we can defend", or "reconcile the positioning canvas with the claims ledger"; reconciles the reused positioning canvas against the shippable stage and the claims ledger to produce a differentiation truth set — every differentiating claim verifiable or marked '[needs source]' — that TALE-T1 is judged against. Not for building the canvas — use positioning-mapper; not for adjudicating claims — use offer-claims-registry; not for authoring the message house — use message-system-architect. 定位真相/差异化校准/可交付现实/主张核对PlanningNo warnings★ 3K↓ —
- torchdrugforyourhealth111-pixel/Vibe-SkillsGraph-based drug discovery toolkit. Molecular property prediction (ADMET), protein modeling, knowledge graph reasoning, molecular generation, retrosynthesis, GNNs (GIN, GAT, SchNet), 40+ datasets, for PyTorch-based ML on molecules, proteins, and biomedical graphs.ScienceNo warnings★ 3.8K↓ —
- hubspot-automationdavepoon/buildwithclaudeAutomate HubSpot CRM operations (contacts, companies, deals, tickets, properties) via Rube MCP using Composio integration.OtherNo warnings★ 3.7K↓ —
- LQF_Machine_Learning_Expert_Guideforyourhealth111-pixel/Vibe-SkillsLQF Machine Learning Expert Guide - Routed skill for ML/Statistical Modeling with Critical Discussion Mode. Triggers on: machine learning, modeling, prediction, training, classification, regression, clustering, deep learning, neural network, model evaluation, feature engineering, hyperparameter tuning, overfitting, underfitting, baseline, ablation study, critique my approach, review my model, is this a good idea, should I use, what's wrong with, evaluate my solution, challenge my assumptions, discuss my approach Engages in critical discussion with minimum 3 rounds of iterative refinement. Challenges both user proposals and own suggestions with fact-based critique. Demands evidence and baselines before accepting solutions.EngineeringNo warnings★ 3.8K↓ —
- offensive-data-exfiltrationSnailSploit/Claude-RedDense methodology covering DNS exfiltration (dnscat2, iodine, dns2tcp), HTTPS tunneling (domain fronting, CDN abuse, legitimate service channels), ICMP tunneling (icmpsh, ptunnel-ng), cloud storage dead drops (S3 presigned URLs, Azure Blob SAS tokens, GCS signed URLs), email-based exfil (SMTP, EWS, draft method), steganography (image, audio, document metadata), encoding/encryption (base64 chunking, XOR, AES), covert channels (custom protocol tunneling, HTTP header encoding, timing channels), and data staging (compression, splitting, encryption). Tools: dnscat2, iodine, dns2tcp, PacketWhisper, chisel, stunnel, icmpsh, ptunnel-ng, steghide, zsteg, OpenStego. MITRE ATT&CK: T1048 (Exfiltration Over Alternative Protocol), T1041 (Exfiltration Over C2 Channel), T1567 (Exfiltration Over Web Service), T1029 (Scheduled Transfer), T1030 (Data Transfer Size Limits), T1132 (Data Encoding), T1001 (Data Obfuscation). Use when planning or executing data exfiltration during authorized red team engagements or post-exploitationDevOpsNo warnings★ 9.3K↓ —
- offensive-advanced-redteamSnailSploit/Claude-RedComprehensive red team operations methodology covering full engagement lifecycle from planning through reporting. Addresses engagement scoping and rules of engagement negotiation, multi-tier C2 infrastructure design with redirectors and domain fronting, malleable traffic profiles and beacon tradecraft, OPSEC discipline including attribution avoidance and indicator management, EDR and AMSI evasion techniques using direct syscalls and unhooking, data collection with chain-of-custody controls, and structured reporting with purple team debrief workflows. Covers assumed-breach, external-to-internal, insider threat, and hybrid physical-cyber engagement scenarios with MITRE ATT&CK mapping throughout. Targets operators planning or executing adversary simulation engagements against mature defenders.DevOpsNo warnings★ 9.3K↓ —
- offensive-exploit-developmentSnailSploit/Claude-Redclaude-red is a curated library of offensive security skills designed for the Claude skills system. Each skill is a structured SKILL.md file that primes Claude with expert-level methodology for a specific attack surface — from SQLi to shellcode, EDR evasion to exploit development.SecurityNo warnings★ 9.3K↓ —
- offensive-krack-fragattacksSnailSploit/Claude-RedKRACK (CVE-2017-13077..082) and FragAttacks (CVE-2020-24586..588 + 26139-26147) — key reinstallation, fragmentation, and aggregation attacks against WPA2 supplicants. Covers Vanhoef's test scripts, viability against modern patched stacks (mostly mitigated post-2021), residual unpatched embedded devices and IoT vendors, and the practical limitations of these attacks in modern engagements. Use when assessing legacy supplicants, embedded clients, or vendors with poor patch cadence.SecurityNo warnings★ 9.3K↓ —
- offensive-bluetooth-classicSnailSploit/Claude-RedBluetooth Classic (BR/EDR) attack methodology — device discovery, service enumeration via SDP, LMP/L2CAP layer attacks, legacy PIN cracking (BlueBorne / KNOB), Bluetooth file-transfer abuse (BlueSnarfing legacy), unauthenticated profile abuse (HSP, HFP, OPP), and modern relevance against older industrial / automotive / accessory targets. Use when in-scope devices use Bluetooth Classic (Bluetooth ≤ 4.0 BR/EDR) — common in legacy car kits, industrial sensors, older medical devices, and audio accessories.MediaNo warnings★ 9.3K↓ —
- poster-heroZJU-REAL/Easel生成 1080×1920 竖版营销海报，包含大标题、核心卖点和可选二维码，适合产品发布、活动宣传与朋友圈传播。当用户说“做竖版营销海报、活动宣传图、朋友圈海报、产品发布海报”时使用。横版金句卡用 card-quote，小红书知识卡用 card-xiaohongshu。OtherNo warnings★ 4.1K↓ —
- offensive-deauth-disassocSnailSploit/Claude-RedDeauthentication and disassociation attacks against 802.11 networks — targeted single-client deauth for handshake capture, broadcast deauth for DoS (with authorization), action-frame attacks bypassing 802.11w (PMF), beacon flooding, mdk4 / aireplay-ng tooling, and rate-limit / PMF-aware operation. Use to coerce client reconnection (handshake capture, evil-twin roaming), as targeted DoS, or to test PMF posture.TestingNo warnings★ 9.3K↓ —
- offensive-wpa3-saeSnailSploit/Claude-RedWPA3 / SAE (Simultaneous Authentication of Equals) attack methodology — transition-mode (mixed WPA2/WPA3) downgrade, Dragonblood side-channel attacks (CVE-2019-9494, 9495, 13377, 13456), SAE auth flooding for AP CPU exhaustion, Hash-to-Element (H2E) timing analysis, group downgrade, and 6 GHz / Wi-Fi 6E spec implications (PMF mandatory, no transition mode allowed). Use when target advertises WPA3-SAE or WPA3-Personal/Enterprise, or operates in 6 GHz where WPA3 + PMF are required by spec.SecurityNo warnings★ 9.3K↓ —
- offensive-wpa2-pskSnailSploit/Claude-RedWPA/WPA2-PSK attack methodology — four-way handshake capture via targeted deauthentication, PMKID attacks (no client required), hcxdumptool / hcxpcapngtool conversion to hashcat hc22000 format, GPU-accelerated cracking with dictionary, mask, and rule-based attacks, vendor default-PSK generators (UPC, Sky, BT, etc.), 802.11r FT key cracking, opportunistic key cache analysis, and signal-level optimization. Use when the in-scope network is WPA/WPA2 Personal — the most common consumer/SMB encryption mode.MarketingNo warnings★ 9.3K↓ —
- offensive-cicd-secretsSnailSploit/Claude-RedComprehensive secrets extraction methodology targeting CI/CD environments across all major platforms. Covers environment variable extraction from build contexts, exploitation of vault and secrets-manager misconfigurations (HashiCorp Vault, AWS Secrets Manager, Azure Key Vault, GCP Secret Manager), runner and agent token abuse for lateral movement, OIDC federation attacks exploiting trust relationships between CI/CD providers and cloud platforms, build log leakage analysis for inadvertently exposed credentials, cache poisoning techniques for credential exfiltration, platform-specific credential store exploitation (GitHub Actions secrets, GitLab CI variables, Jenkins credential providers), service connection and service account abuse in Azure DevOps and GCP, and Docker registry credential theft from build environments. Maps to MITRE ATT&CK T1552 (Unsecured Credentials) and its sub-techniques. Each section provides enumeration procedures, extraction techniques, and post-exploitation pivoting guidance for using rDevOpsNo warnings★ 9.3K↓ —
- offensive-evil-twinSnailSploit/Claude-RedEvil Twin / KARMA / Mana access point methodology — rogue AP construction with hostapd-mana / wifiphisher / airgeddon, KARMA universal probe response, Mana selective probe response, captive portal phishing, deauth-driven client coercion to attacker AP, MAC randomization defeat via PNL leak analysis, post-association MITM (DNS, ARP, transparent proxy), credential capture for portal/web/SMB, and detection-evasion tactics. Use to coerce client devices onto an attacker-controlled AP, intercept their traffic, harvest credentials, or deliver payloads via captive portal.OtherNo warnings★ 9.3K↓ —
- skill-content-repurposingZJU-REAL/Easel将一篇内容拆解改编到小红书、抖音、B站、微博等多平台，适配各平台原生格式和风格。 当用户说"一稿多发""改编到各平台""内容复用""多平台适配""这篇改成小红书/抖音""转成其他平台""一鱼多吃"时使用。 和 skill-cross-platform-diff 的区别：diff 做平台差异洞察分析（发现层）；本 SKILL 实际改写内容到各平台。 和 skill-cross-platform-publish 的区别：本 SKILL 只改写适配内容，cross-platform-publish 负责实际一键分发到多平台。OtherNo warnings★ 4.1K↓ —
- offensive-fuzzingSnailSploit/Claude-RedPractical offensive fuzzing methodology covering target identification, fuzzer selection (AFL++, libFuzzer, Honggfuzz, Boofuzz, syzkaller), harness writing, corpus curation, mutation strategies, coverage measurement, and crash triage. Use when setting up or running fuzz campaigns against any target: file parsers, network protocols, kernel drivers, EDR engines, embedded firmware, or language runtimes.WritingNo warnings★ 9.3K↓ —
tao-train-mask-auto-label
NVIDIA/skills
OtherUpdated 17 hours ago
From the author
MAL (Mask Auto-Label) for weakly-supervised segmentation. Produces segmentation masks from minimal annotations (point or box annotations) using a ViT-MAE backbone. Use when training, evaluating, or running inference for a TAO MAL model. Trigger phrases include "train MAL", "Mask Auto-Label", "weakly-supervised segmentation", "box-prompted segmentation", "minimal-annotation mask prediction".
Install
npx skills add NVIDIA/skills --skill tao-train-mask-auto-label -a claude-code
Installs to .claude/skills/tao-train-mask-auto-label/ · Each command fetches the skill straight from GitHub with the open skills CLI.
Download .zip
Byte-identical to the scanned commit, with the repository’s licence and a SOURCE.txt.
What it can touch
Instructions onlyNo network use seenLicence: APACHE-2.0
Safety checks on SKILL.md
No warnings.
Why 90
Security100
Provenance83
Maintenance100
Licence100
Adoption57
Files (14)
- BENCHMARK.md
- SKILL.md
- config/skillspector-baseline.yaml
- evals/evals.json
- references/skill_info.yaml
- references/spec_template_evaluate.yaml
- references/spec_template_inference.yaml
- references/spec_template_train.yaml
- schemas/evaluate.schema.json
- schemas/inference.schema.json
- schemas/manifest.json
- schemas/train.schema.json
- +2 more