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.
49,492 skills from 2,391 repositories · updated 18 hours ago
49,492 skills
- consulting-agbKlotzkette/claude-fuer-deutsches-rechtFür Consulting AGB: ordnet Norm, Beweislast und Gegenargument; Ergebnis: Prüfprodukt mit Risiko und nächstem Schritt.OtherNo warnings★ 1.8K↓ —
- fundraising-and-investor-relationscbrock84/headcountRaises capital and manages the relationship afterward — deciding how much and why, understanding what dilution and preferences actually cost, running a process with real competitive tension, preparing for diligence before it starts, and reporting to investors and a board in a way that keeps support when results are bad. Use this to plan a raise, evaluate a term sheet beyond the valuation, prepare a data room, or fix reporting that is producing surprises.FinanceNo warnings★ 2.6K↓ —
- lockup-daysKlotzkette/claude-fuer-deutsches-rechtFür Lock-up Days: ordnet Norm, Beweislast und Gegenargument; Ergebnis: Prüfprodukt mit Risiko und nächstem Schritt.OtherNo warnings★ 1.8K↓ —
- ddia-systemswondelai/skillsDesign data systems by understanding storage engines, replication, partitioning, transactions, and consistency models. Use when the user mentions "database choice", "which database should I use", "SQL or NoSQL", "replication lag", "partitioning strategy", "consistency vs availability", "stream processing", "ACID transactions", "eventual consistency", "my queries are slow at scale", or "data is inconsistent across replicas". Also trigger when choosing a datastore, designing data pipelines, or debugging distributed-system consistency issues. Covers data models, batch/stream processing, and distributed consensus. For system design, see system-design. For resilience, see release-it.DataNo warnings★ 2.6K↓ —Also in 2 other repos
- konzernklauselKlotzkette/claude-fuer-deutsches-rechtFür Konzernklausel: ordnet Norm, Beweislast und Gegenargument; Ergebnis: Prüfprodukt mit Risiko und nächstem Schritt. Fachgebiet: AGB-Recht-Prüfer. Route: konzernklausel.OtherNo warnings★ 1.8K↓ —
- stripeLeoYeAI/agent-master-skillsWith CreditClaw and backed by Stripe, you can shop anywhere.OtherNo warnings★ 2.2K↓ —
- reseller-agbKlotzkette/claude-fuer-deutsches-rechtFür Reseller AGB: ordnet Norm, Beweislast und Gegenargument; Ergebnis: Prüfprodukt mit Risiko und nächstem Schritt.OtherNo warnings★ 1.8K↓ —
- service-level-managementcbrock84/headcountDefines and manages service levels — setting targets that reflect what customers need, measuring honestly, and handling breaches. Use this to write or negotiate an SLA, decide what to measure and at what threshold, respond to a missed service level, or work out why a service that meets its targets still has unhappy customers.WritingNo warnings★ 2.6K↓ —
- scenario-grok-imagine-imagescenario-labs/skillsUse when generating or editing images with Grok Imagine models on Scenario via MCP: text-to-image, instruction editing with up to three reference images, posters, packaging, ads, or infographics with exact in-image text, photoreal shots from camera and lighting cues, extended aspect ratios up to 20:9 and 9:20, quality and resolution tiers, or batches of variations. Keywords: Grok Imagine Image 2.0, Image Quality, xAI, Aurora, Grok, txt2img, img2img, typography, 2K.MediaNo warnings★ 2.2K↓ —
- neuropixels-analysisK-Dense-AI/scientific-agent-skillsAnalyzes Neuropixels extracellular recordings end-to-end with SpikeInterface. Covers loading SpikeGLX/Open Ephys/NWB data, preprocessing, drift/motion correction, Kilosort4 (and CPU) spike sorting, quality metrics, and unit curation (threshold-based, model-based UnitRefine, and AI-assisted visual review). Use when working with Neuropixels 1.0/2.0 recordings, spike sorting, or extracellular electrophysiology analysis.EngineeringNo warnings★ 56.1K↓ —
- hypothesis-generationK-Dense-AI/scientific-agent-skillsFormulates evidence-bounded scientific questions, candidate hypotheses, rival explanations, causal or associational claims, discriminating predictions, measurements, and preregistration-ready analysis plans. Used when turning observations or preliminary findings into transparent, testable research plans without treating hypotheses as facts.ScienceNo warnings★ 56.1K↓ —
- hgb-red-teamKlotzkette/claude-fuer-deutsches-rechtFür HGB Red-Team: prüft Ergebnis, Beweislast und Gegenposition; Ergebnis: Gegenprüfung mit Beweis- und Fristencheck.OtherNo warnings★ 1.8K↓ —
- portaljs-migratedatopian/portaljsMigrate (harvest) datasets between open-data platforms. Reads CKAN, a DCAT-US /data.json catalog (DKAN, ArcGIS Hub, data.gov), a DCAT / DCAT-AP RDF feed (JSON-LD, Turtle, or RDF/XML), Socrata, OpenDataSoft, or an ArcGIS FeatureServer, and writes them to a static PortalJS catalog or pushes them into a CKAN instance over its API. Use when moving datasets from an external open-data platform into a PortalJS portal, or bridging one CKAN instance to another.WritingNo warnings★ 2.4K↓ —
- detection-and-monitoringcbrock84/headcountBuilds the capability to notice an attack in progress — deciding what to log and retain, centralizing it somewhere tamper-resistant, writing detections that fire on attacker behavior rather than on individual events, tuning out the noise that trains people to ignore alerts, and defining what happens when something fires. Use this to design or assess monitoring coverage, work out why an incident went unnoticed, cut alert volume without losing signal, or decide what a detection capability should cost.DesignNo warnings★ 2.6K↓ —
- transformersK-Dense-AI/scientific-agent-skillsHugging Face Transformers for loading Hub models, running pipeline inference, text generation, and Trainer fine-tuning on NLP, vision, audio, and multimodal tasks. Applies when working with AutoModel, pipelines, tokenizers, generation configs, or TrainingArguments within Transformers.MediaNo warnings★ 56.1K↓ —
- networkxsickn33/agentic-awesome-skillsNetworkX is a Python package for creating, manipulating, and analyzing complex networks and graphs.EngineeringNo warnings★ 49.1K↓ —
- scenario-video-to-motionscenario-labs/skillsUse when turning a reference video into 3D character animation through the Scenario MCP: markerless motion capture from a phone clip or a generated performance, auto-rigging an uploaded FBX, GLB, or OBJ character and applying the captured motion, raw motion on a Mixamo or MetaHuman skeleton to retarget yourself, in-place locomotion, looping clips, facial capture, or FBX exports for Blender, Maya, Unreal, Unity, or Roblox. Keywords: mocap, video to motion, video23d, animation from video.MediaNo warnings★ 2.2K↓ —
- scenario-moderationscenario-labs/skillsUse when a Scenario generation is blocked, refused, or returns a moderation or sensitive-content error, when a video is rejected after rendering or its audio track is flagged, when an output is flagged for likeness to a real person or for IP and copyright detection, when the same prompt passes on one model and fails on another, or when a team's own characters, weapons, or props get flagged. Keywords: blocked prompt, content moderation, refused generation, provider filter, false positive.MediaNo warnings★ 2.2K↓ —
- iam-helper-for-troubleshootinggoogle/skillsDiagnoses, remediates, and manages Identity and Access Management (IAM) access issues on Google Cloud. Supports two distinct operational modes: (1) Requester Flow for developers encountering access denials (capturing error context, self-service PAM JIT activations, elevated developer self-remediation, or logging structured tickets), and (2) Resolver Flow for privileged administrators (authoritative Policy Troubleshooter analysis, deny policy exemptions, least-privilege role discovery, and PAM/IAM provisioning).OfficialProductivityNo warnings★ 21.9K↓ —
- uncertainty-and-unitsK-Dense-AI/scientific-agent-skillsTracks physical units and propagates measurement uncertainty in scientific calculations using pint and uncertainties. Use for unit conversion and dimensional checking, GUM uncertainty budgets, Type A and Type B evaluation, coverage factors and expanded uncertainty, Monte Carlo propagation, significant-figure and plus-minus reporting, error propagation through curve fits, CODATA constants, auditing Python code for stripped units or broken uncertainty propagation, and order-of-magnitude plausibility checks using dimensionless groups (Reynolds, Peclet, Damkohler, Knudsen, Biot, Womersley), characteristic scales such as diffusion time or Debye length, and observed magnitude ranges. Trigger on "is this number physically reasonable", "sanity check these units", "what regime is this flow in", or a result that looks off by orders of magnitude.ScienceNo warnings★ 56.1K↓ —
- new-skillpedrohcgs/claude-code-my-workflowScaffold a new skill that follows this repo's conventions — interviews for purpose, trigger phrases, and tool needs, then writes `.claude/skills/<name>/SKILL.md` from the skill template with frontmatter and body that pass the integrity gates on first try. Use when user says "write a skill", "scaffold a skill", "create a new skill", "I keep doing X, make it a skill", "new slash command", or "turn this workflow into a skill". NOT for capturing a one-off session discovery — that is `/learn`.WritingNo warnings★ 1.7K↓ —
- pentest-checklistzebbern/claude-code-guideThis skill should be used when the user asks to "plan a penetration test", "create a security assessment checklist", "prepare for penetration testing", "define pentest scope", "follow security testing best practices", or needs a structured methodology for penetration testing engagements.SecurityNo warnings★ 4.7K↓ —
- daskK-Dense-AI/scientific-agent-skillsScales pandas, NumPy, and custom Python research workflows beyond memory or across clusters with Dask. Covers DataFrames, Arrays, Bags, Futures, chunking, schedulers, and distributed diagnostics. Use for partitioned file processing, scientific array computation, or parallel tasks whose memory and dependency structure require Dask.DataNo warnings★ 56.1K↓ —Also in 2 other repos
- get-available-resourcesK-Dense-AI/scientific-agent-skillsDetects host inventory and effective CPU, memory, disk, scheduler, container, and accelerator limits when a user asks for resource-aware planning or before a clearly resource-sensitive local workload. Produces a redacted JSON snapshot and conservative planning helpers without stress tests or assuming visible host hardware is usable.TestingNo warnings★ 56.1K↓ —
hivemind-goals
activeloopai/hivemind
EngineeringUpdated yesterday
From the author
Create, track and update team goals in Hivemind via the `hivemind` CLI. Use whenever the user mentions a goal, objective, target, milestone, or asks to track progress on something measurable. ALSO use when the user says "task", "todo", "work item", "remind me to", "fix X", or any actionable work item — the goal system replaced the legacy `hivemind tasks` CLI and now covers both objectives and tasks.
Install
npx skills add activeloopai/hivemind --skill hivemind-goals -a claude-code
Installs to .claude/skills/hivemind-goals/ · 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 89
Security100
Provenance79
Maintenance100
Licence100
Adoption52
Files (1)
- SKILL.md