RADAR DES SKILLS · GITHUB
Des Agent Skills utiles, classés selon leur usage réel.
Comparez les Skills vérifiés par fonction, contenu, installations et fiabilité de la source avant de les ajouter.
- PÉRIMÈTRE
- VÉRIFIÉ
- Skills
- 10 772
- dépôts
- 1711
- dernière synchronisation
- 23 sept. 2026
mblodeseo
Audits and fixes technical SEO, researches search demand, creates content briefs, and measures SEO/AEO performance. Use when asked to "audit SEO", "fix indexing", "improve AI visibility", "check keyword volume", "write an SEO brief", or "why did organic traffic drop". For Mintlify Agent Score, AFDocs, or Is Agentic reports use agent-ready. For writing the article use ghostwriter; for visual redesign use ui-design.
- installations
- 1
- GitHub Stars
- 126
- Mis à jour
- 19 sept.
mblodetidy
Applies diff-scoped simplifications using reuse, ownership, efficiency, and test-value checks, including actionable review findings. Use when asked to "tidy this", "simplify my diff", or "apply the review findings". For a read-only report use pr-reviewer; for repository architecture use codebase-architecture.
- installations
- 1
- GitHub Stars
- 126
- Mis à jour
- 19 sept.
madteacherflutter-animations
- Add, fix, refactor, debug, test, or explain Flutter animations and motion effects. Use when working with implicit animations such as AnimatedContainer, AnimatedOpacity, AnimatedSwitcher, and TweenAnimationBuilder; explicit animations using AnimationController, Tween, CurvedAnimation, AnimatedWidget, AnimatedBuilder, and built-in transitions; Hero/shared-element route transitions; staggered or sequenced animations; physics-based motion, gestures, springs, flings, scroll physics, curves, performance, accessibility, reduced motion, and animation lifecycle bugs.
- installations
- 1
- GitHub Stars
- 109
- Mis à jour
- 1 mai
jonathimerdocs-as-marketing
Transform documentation into a powerful marketing channel that attracts, converts, and retains developers. This skill covers creating documentation that ranks in search, converts visitors into users, and accelerates adoption through exceptional information architecture and developer-focused writing. Trigger phrases: "documentation strategy", "docs that convert", "quickstart optimization", "API reference best practices", "developer documentation", "docs SEO", "information architecture for docs", "technical writing for marketing", "docs-led growth"
- installations
- 1
- GitHub Stars
- 87
- Mis à jour
- 3 mars
jonathimerfree-tier-strategy
Design free tiers that convert to paid without creating resentment or abuse. Trigger phrases: free tier design, freemium model, free trial strategy, free tier limits, developer free plan, open source commercial, feature gating, upgrade triggers, free tier conversion
- installations
- 1
- GitHub Stars
- 87
- Mis à jour
- 3 mars
magnus919frontend-engineering
Build and maintain web frontends — component architecture, state management, API integration, responsive layout, client-side performance, and frontend testing patterns. Framework agnostic, focused on web frontend implementation. Do not use for backend service implementation, data engineering, or platform infrastructure work.
- installations
- 1
- GitHub Stars
- 87
- Mis à jour
- 20 sept.
magnus919qa-methodology
- Design and apply QA methodology for software teams: test strategy, regression testing, CI failure triage, test automation, quality gates and metrics, risk-based testing, exploratory testing, test design techniques, AI code quality gates (independent verification, artifact provenance, spec-first oracles, AI-review-comment triage, acceptance-criteria testability for Spec-Driven Development), mutation-guided test hardening and review evidence (surviving mutants, weak assertions, diff-aware mutation testing), agentic eval design (dataset test design, judge-as-system-under-test, flaky-eval discipline), QA career levels (Senior/Staff/Principal), and SDET engineering (test infrastructure, gTAA, CI/CD integration). Do not use for root-cause debugging of production incidents, security implementation or threat modeling, or evaluation framework governance and statistical analysis — route those to systematic-debugging, secure-software-engineering, and agent-evals-and-observability respectively.
- installations
- 1
- GitHub Stars
- 87
- Mis à jour
- 20 sept.
magnus919systematic-debugging
- Diagnose root causes with a four-phase debugging protocol. Use for ANY technical issue — test failures, production bugs, unexpected behavior, performance problems, build failures, or integration issues. ESPECIALLY when under time pressure, when "one quick fix" seems obvious, or when previous fix attempts have failed. Do not use this skill for unrelated requests; route to the nearest named specialist.
- installations
- 1
- GitHub Stars
- 87
- Mis à jour
- 20 sept.
itgoyogsap
GSAP animation reference for HyperFrames. Covers gsap.to(), from(), fromTo(), easing, stagger, defaults, timelines (gsap.timeline(), position parameter, labels, nesting, playback), and performance (transforms, will-change, quickTo). Use when writing GSAP animations in HyperFrames compositions.
- installations
- 1
- GitHub Stars
- 58
- Mis à jour
- 22 avr.
itgoyohyperframes-registry
Install and wire registry blocks and components into HyperFrames compositions. Use when running hyperframes add, installing a block or component, wiring an installed item into index.html, or working with hyperframes.json. Covers the add command, install locations, block sub-composition wiring, component snippet merging, and registry discovery.
- installations
- 1
- GitHub Stars
- 58
- Mis à jour
- 22 avr.
itgoyokarpathy-guidelines
Behavioral guidelines to reduce common LLM coding mistakes. Use when writing, reviewing, or refactoring code to avoid overcomplication, make surgical changes, surface assumptions, and define verifiable success criteria.
- installations
- 1
- GitHub Stars
- 58
- Mis à jour
- 22 avr.
walrusquantbaseline-models
Design and evaluate simple sports prediction baselines before accepting more complex models. Use for constant-rate, home-advantage, logistic, Elo-style, or market-reference comparisons.
- installations
- 1
- GitHub Stars
- 49
- Mis à jour
- 9 sept.
walrusquantcalibration-check
Evaluate whether sports-model probabilities match observed frequencies. Use for Brier score, log loss, reliability bins, ECE, segment checks, and recalibration decisions.
- installations
- 1
- GitHub Stars
- 49
- Mis à jour
- 9 sept.
walrusquanteda-sports
Exploratory data analysis for user-provided sports data: grain, key integrity, coverage, missingness, entity balance, base rates, outliers, structural breaks, and leakage red flags. Use before feature engineering or model fitting.
- installations
- 1
- GitHub Stars
- 49
- Mis à jour
- 9 sept.
walrusquantenvironment-setup
Create and verify a portable Python environment for sports analysis. Use for machine setup, onboarding, dependency diagnosis, or reproducibility checks.
- installations
- 1
- GitHub Stars
- 49
- Mis à jour
- 9 sept.
walrusquantfeature-rules
Define, review, and document point-in-time legal sports-model features. Use when creating rolling form, rest, matchup, rating, roster, injury, or contextual predictors.
- installations
- 1
- GitHub Stars
- 49
- Mis à jour
- 9 sept.
walrusquantmodel-card
Write a durable sports model card covering identity, intended use, target, decision time, data, features, baselines, validation, results, limits, maintenance, and kill conditions. Use when freezing or sharing a model.
- installations
- 1
- GitHub Stars
- 49
- Mis à jour
- 9 sept.
walrusquantnflreadpy
Load NFL schedules, play-by-play, rosters, and player or team statistics directly from nflverse with nflreadpy. Use for NFL acquisition, schema review, bounded snapshots, and preparing user-owned analysis artifacts.
- installations
- 1
- GitHub Stars
- 49
- Mis à jour
- 9 sept.
walrusquantpredictive-modeling
Build and evaluate predictive sports models from user-provided modeling data. Use for binary outcome models, feature/model selection, chronological backtests, probability scoring, and comparison to baselines.
- installations
- 1
- GitHub Stars
- 49
- Mis à jour
- 9 sept.
walrusquantpybaseball
Load MLB Statcast, batting, pitching, standings, and player lookup data directly with pybaseball. Use for bounded pitch-level pulls, season tables, schema checks, and user-owned baseball data artifacts.
- installations
- 1
- GitHub Stars
- 49
- Mis à jour
- 9 sept.
walrusquantresults-reporting
Report sports analysis and modeling results with the question, data, methods, validation, baselines, metrics, interpretation, limits, figures, and reproduction pointers. Use for research notes, reports, and final answers.
- installations
- 1
- GitHub Stars
- 49
- Mis à jour
- 9 sept.
walrusquantsimulation-sports
Simulate game and season outcomes from user-supplied probabilities or ratings, summarize uncertainty, and test sensitivity to assumptions. Use for standings, win totals, matchup distributions, and scenario analysis.
- installations
- 1
- GitHub Stars
- 49
- Mis à jour
- 9 sept.
walrusquantsports-modeling-doctrine
Define a sports analysis or prediction question, grain, decision time, baselines, primary metrics, validation, and acceptance criteria before choosing algorithms. Use at the start of any sports modeling project.
- installations
- 1
- GitHub Stars
- 49
- Mis à jour
- 9 sept.
walrusquantsports-visualization
Create honest sports-analysis figures from user-owned data, including distributions, rates, rating trajectories, calibration plots, and walk-forward metric comparisons. Use for exploration and communication.
- installations
- 1
- GitHub Stars
- 49
- Mis à jour
- 9 sept.