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.
49,399 skills from 2,375 repositories · updated 28 minutes ago
49,399 skills
- wiki_captureKaelio/ktxktx's knowledge base - wiki pages for durable, reusable business knowledge. Covers capture workflow for user preferences, metric definitions, organizational conventions, and cross-references between wiki pages and semantic-layer sources. Loaded by the post-turn memory-agent only. The research agent reads wiki via `wiki_read`/`wiki_search` but does not write it.ScienceNo warnings★ 1.6K↓ —
- looker_ingestKaelio/ktxExtract durable ktx knowledge and semantic-layer contribution proposals from staged Looker runtime dashboard, Look, and explore JSON. Load for WorkUnits whose raw files are under explores/, dashboards/, or looks/.OtherNo warnings★ 1.6K↓ —
- sl_captureKaelio/ktxHow to capture new reusable patterns into ktx's semantic layer - when a measure, segment, or join belongs in the catalog and how to write it generically so it stays small and useful over time. Loaded by the post-turn memory-agent only. The research agent does not write to the SL.ScienceNo warnings★ 1.6K↓ —
- ingest_triageKaelio/ktxClassify and resolve conflicts detected during bundle ingest (structural duplicates, definitional contradictions, near-duplicate clusters, re-ingest changes, evictions).OtherNo warnings★ 1.6K↓ —
- metabase_ingestKaelio/ktxConvert Metabase questions, models, and metrics into ktx Semantic Layer source definitions. Covers result-metadata to KSL column type mapping, FK/PK detection, near-duplicate deduplication, pre-aggregation decomposition, join-graph connectivity, and how to react to priorProvenance from earlier ingest syncs. Load when the WorkUnit contains `cards/<id>.json` files under a Metabase bundle.EngineeringNo warnings★ 1.6K↓ —
- lookml_ingestKaelio/ktxMap a LookML view/model/explore into ktx semantic layer sources. Covers the LookML to ktx primitive table, provenance tagging, and three worked examples (overlay, standalone from derived_table, standalone with sql_always_where). Load when the turn contains `.lkml` content.OtherNo warnings★ 1.6K↓ —
- slKaelio/ktxktx's semantic layer - a structured catalog of sources (tables/views), measures, joins, and segments expressed as YAML. Covers the schema and how to query it via `sl_query`. Use when the task involves querying pre-defined metrics (ARR, churn, retention, LTV, MAU) or reading SL source YAML to understand the catalog. Capture is handled by the `sl_capture` skill (memory-agent only).ProductivityNo warnings★ 1.6K↓ —
- dbt_ingestKaelio/ktxMap dbt `schema.yml` / `properties.yml` models and sources into ktx semantic-layer overlays and column notes. Covers `sources:` vs `models:`, column `data_tests` (not_null, unique, accepted_values, relationships), and how bundle-time writes complement manifest backfill from git sync. Load when the WorkUnit's `skillNames` includes `dbt_ingest` or when raw files are dbt YAML under `models/` / `sources/`.DataNo warnings★ 1.6K↓ —
- bug-receiptgithub/awesome-copilotClose defects and incidents with a BUG RECEIPT and VERIFIED, PARTIAL, or BLOCKED status after diagnosis, repair, or recovery.OtherNo warnings★ 40.8K↓ —
- ktx-analyticsKaelio/ktxUse when answering a question that needs data from a ktx-connected database - investigating, analyzing, "how many", "show me", "what's the breakdown of", finding records by value, exploring tables, comparing periods, explaining metrics, or any data-analysis request. Triggers even when the user does not say "analytics"; if the answer requires querying a configured ktx connection, this skill applies.DataNo warnings★ 1.6K↓ —
- hivemind-memoryactiveloopai/hivemindGlobal team and org memory powered by Activeloop. ALWAYS check BOTH built-in memory AND Hivemind memory when recalling information.ProductivityNo warnings★ 1.6K↓ —
- respond-to-evalpedrohcgs/claude-code-my-workflowTurn student course evaluations (free-text + numeric) into an actionable teaching-improvement plan — the teaching analogue of /respond-to-referees. Clusters comments into themes, separates signal from noise, classifies each theme Keep / Change / Investigate / Out-of-scope, and drafts concrete changes mapped to the syllabus and slide decks. Use when user says "respond to my evals", "what do these course evaluations tell me", "turn my teaching feedback into a plan", or after a semester's evals arrive.DocumentsNo warnings★ 1.7K↓ —
- lueckenKlotzkette/claude-fuer-deutsches-rechtFür Aufsichtsrechtlichen Gap-Tracker führen: ordnet Akte, Belege und Lücken; Ergebnis: Prüfprodukt mit Risiko und nächstem Schritt.OtherNo warnings★ 1.8K↓ —
- hivemindactiveloopai/hivemindGlobal team and org memory powered by Activeloop. ALWAYS check BOTH built-in memory AND Hivemind memory when recalling information.ProductivityNo warnings★ 1.6K↓ —
- hivemind-goalsactiveloopai/hivemindCreate, track and update team goals via the Deeplake virtual filesystem at memory/goal/. 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.EngineeringNo warnings★ 1.6K↓ —
- seedance-3d-cartoonLearnPrompt/awesome-seedanceOne small anthropomorphic character carries the whole film. Spell out its looks part by part, write the style as render settings you can measure, and cut the timeline so each block holds a single action goal. Use when the user wants a Seedance 2.5 / 2.0 video prompt for this kind of clip (3D cartoon character short; 中文触发词: 3D 卡通角色短片, 3D 卡通, 皮克斯, 黏土, 卡通角色), or asks how published cases of this type were prompted. Read references/cases.md before drafting.MediaNo warnings★ 2.4K↓ —
- dev-implementationcodeaholicguy/ai-devkitAI DevKit · Implementation phase guidance for executing feature plans and checking implementation against design. Use when the user wants to implement planned tasks, update implementation docs, verify code matches design, or run dev-lifecycle phases 5 and 7.DesignNo warnings★ 1.7K↓ —
- copilot-pr-autopilotgithub/awesome-copilotCopilot left 14 review comments on your PR — half are nits. Hours of fix → reply → resolve → re-request, and each round lands MORE comments. This skill runs loop engineering: auto-triggers Copilot Code Review via GraphQL (no @copilot mention), triages every open thread (Copilot, humans, advanced-security) with a fix / decline / escalate rubric, dispatches parallel fix sub-agents that obey the repo build/test/lint conventions, commits per iteration, replies+resolves citing the pushed SHA, then re-triggers until HEAD is reviewed with zero threads awaiting the agent's reply (remaining open threads are explicit hand-offs to the human — escalated declines, design tradeoffs). You merge a clean PR; the bot runs it. Trigger phrases: "address copilot comments", "run a copilot review loop", "fix this PR", "iterate on copilot feedback". Repo-agnostic, gh CLI + PowerShell. Full autopilot needs repo Triage/Write; external PR authors get single-iteration mode plus manual re-trigger (UI 🔄 or substantive-commit push).SecurityNo warnings★ 40.8K↓ —
- statistical-powerK-Dense-AI/scientific-agent-skillsCalculates sample sizes and statistical power for study planning. Applies when someone asks "how many subjects/samples/replicates do I need", wants an a priori power analysis, a minimum detectable effect (MDE), a power curve, or needs to justify a sample size for a grant, IRB protocol, or pre-registration. Covers closed-form power for t-tests, ANOVA, proportions, correlations, chi-square, and regression, plus simulation-based (Monte Carlo) power for complex designs — logistic/Poisson regression, mixed models, cluster-randomized trials, survival, and interactions. Also handles requests that only mention an effect size, alpha, or "80% power" without saying "power analysis" explicitly. For laying out the study (randomization, blocking, factorial/DOE, crossover, sequential designs) use experimental-design; for analyzing data already collected and reporting it use statistical-analysis.TestingNo warnings★ 56K↓ —
- obviously-awesomewondelai/skillsDefine product positioning by mapping competitive alternatives, unique attributes, and best-fit customers to the right market category. Use when the user mentions "positioning", "competitive alternatives", "how to position", "market category", "positioning canvas", "repositioning", "category creation", "what category are we in", or "why prospects dont get what we do". Also trigger when launching a new product, entering a crowded market, or diagnosing why prospects dont grasp the product's value. Covers the positioning canvas and team workshops. For customer jobs analysis, see jobs-to-be-done. For go-to-market, see crossing-the-chasm.OtherNo warnings★ 2.6K↓ —Also in 2 other repos
- agent-master-skillsUpdatedLeoYeAI/agent-master-skillsA curated collection of 3003+ best agent skills — AI tools, productivity, marketing, frontend, mobile, backend, DevOps and more. Harness-agnostic, refreshed daily by MyClaw.ai — Powered by MyClaw.aiDevOpsNo warnings★ 2.2K↓ —
- r-package-checkpedrohcgs/claude-code-my-workflowRun the full R package release gate — regenerate docs, run the test suite, run R CMD check --as-cran, and triage every ERROR / WARNING / NOTE against CRAN policy before a release or submission. Use when the user says "check my R package", "R CMD check", "is this package CRAN-ready", "run devtools::check", "prepare for CRAN submission", or points at a directory containing a DESCRIPTION file. Produces a check report + CRAN-submission checklist in `quality_reports/`.TestingNo warnings★ 1.7K↓ —
- simulation-studypedrohcgs/claude-code-my-workflowScaffold and run a reproducible Monte Carlo simulation study in R — a declared assumption regime, a parameterized DGP, an estimator grid, a seeded replication loop, and a summary of bias, RMSE, empirical SE, coverage, size/power with Monte Carlo standard errors. Use when the user says "run a Monte Carlo simulation", "simulation study", "check the bias/coverage of an estimator", "compare estimators in simulation", "size and power simulation", "Monte Carlo experiment", or wants to demonstrate an estimator's finite-sample properties. Produces a numbered R script in `scripts/R/` and saves per-replication raw results + a summary table to `output/`.ProductivityNo warnings★ 1.7K↓ —
- compress-sessionpedrohcgs/claude-code-my-workflowDistill the current conversation into a structured note (decisions made, open questions, file pointers with line numbers, next 1–3 actions) and save to `quality_reports/session_logs/` before auto-compression. Differs from `/checkpoint` (explicit stop-point snapshot) and from auto-compaction (which truncates rather than distills). Use when context is approaching auto-compact threshold, when a long pipeline has accumulated many decisions, or when the user says "compress", "distil this session", "before we hit auto-compact", "structured handoff before context resets".ProductivityNo warnings★ 1.7K↓ —
canvas
LeoYeAI/agent-master-skills
OtherUpdated 19 hours ago
From the author
Display HTML content on connected OpenClaw nodes (Mac app, iOS, Android).
Install
npx skills add LeoYeAI/agent-master-skills --skill canvas -a claude-code
Installs to .claude/skills/canvas/ · 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 onlyUses the networkLicence: MIT
Safety checks on SKILL.md
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Why 89
Security100
Provenance80
Maintenance100
Licence100
Adoption53
Files (1)
- SKILL.md