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 6 hours ago
48,451 skills
- improve-search-visibility-entity-footprintgooseworks-ai/goose-skillsStep of the improve-search-visibility playbook: checks the profiles answer engines read, plus local listings and reviews, against the brand's facts and writes corrections. Run improve-search-visibility instead; not for direct use.WritingNo warnings★ 1.3K↓ —
- baoyu-comicJimLiu/baoyu-skillsKnowledge comic creator supporting multiple art styles and tones. Creates original educational comics with detailed panel layouts and batch-capable image generation. Use when user asks to create "知识漫画", "教育漫画", "biography comic", "tutorial comic", or "Logicomix-style comic".MediaNo warnings★ 27.3K↓ —
- colm-related-workbrycewang-stanford/Awesome-Journal-SkillsUse when positioning a COLM paper inside the fastest-moving literature in ML — triaging arXiv-heavy citations, handling concurrent work fairly, citing model and dataset artifacts correctly, distinguishing COLM's three-edition archive from adjacent venues' archives, and keeping self-citation double-blind safe.ScienceNo warnings★ 1.4K↓ —
- cell-star-methodsbrycewang-stanford/Awesome-Journal-SkillsUse to build Cell's mandatory STAR Methods — the Key Resources Table, the three Resource Availability subsections, Experimental Model and Subject Details, Method Details, and Quantification and Statistical Analysis, in the exact required order.OtherNo warnings★ 1.4K↓ —
- cikm-camera-readybrycewang-stanford/Awesome-Journal-SkillsUse when turning an accepted CIKM paper into its ACM proceedings version inside the short notification-to-camera-ready window, covering de-anonymization, the e-rights and TAPS pipeline into the ACM Digital Library, CCS concepts and metadata, GenAI-disclosure retention, artifact link publication, and Rome presentation logistics.ScienceNo warnings★ 1.4K↓ —
- asplos-workflowbrycewang-stanford/Awesome-Journal-SkillsUse when planning an ASPLOS campaign across the two-deadline cycle — sequencing evidence building, the September 9, 2026 submission gate, author-response windows, the December 21 notification, Major Revision resubmission six weeks later, artifact evaluation, and the April 2027 conference in Crete, with owners per risk.ProductivityNo warnings★ 1.4K↓ —
- atc-review-processbrycewang-stanford/Awesome-Journal-SkillsUse when reasoning about how an ATC (ACM SIGOPS Annual Technical Conference, formerly USENIX ATC) submission is evaluated — the two-round extended-abstract model, the round-one gate by two reviewers, the round-two read by 3-4 reviewers, double-blind reviewing, rebuttal, conditional acceptance with shepherding, and how ATC differs from OSDI/SOSP/NSDI.EngineeringNo warnings★ 1.4K↓ —
- ase-review-processbrycewang-stanford/Awesome-Journal-SkillsUse when reasoning about how an ASE (IEEE/ACM Automated Software Engineering) research submission is evaluated, covering double-anonymous review, the early-rejection stage before rebuttal, the Accept / Revision / Reject outcomes, the criteria-bound revision round, and how ASE's process differs from FSE's PACMSE Major Revision, ICSE's cycles, and ISSTA's rounds.EngineeringNo warnings★ 1.4K↓ —
- vis-writing-stylebrycewang-stanford/Awesome-Journal-SkillsUse when revising an IEEE VIS paper for a task-grounded visualization contribution on the first page, a design rationale that ties each visual encoding and interaction to a task, evaluation matched to the contribution type, honest limitations, and disciplined use of the VGTC/TVCG 9+2 page budget for a paper that will be read as an IEEE TVCG journal article.WritingNo warnings★ 1.4K↓ —
- vldb-experimentsbrycewang-stanford/Awesome-Journal-SkillsUse when designing or auditing the evaluation of a VLDB paper, covering workload and dataset realism at scale, competitor tuning fairness, scalability curves versus single points, tail-latency and throughput reporting, ablations that isolate the mechanism, and the loss-case disclosure PVLDB reviewers look for first.ScienceNo warnings★ 1.4K↓ —
- aaai-camera-readybrycewang-stanford/Awesome-Journal-SkillsUse when preparing an accepted AAAI paper for camera-ready source submission to AAAI Press, including proceedings page limits, two-column template compliance, copyright transfer, purchased extra technical pages, deanonymization, registration, oral or poster presentation, and final public artifact release.ScienceNo warnings★ 1.4K↓ —
- aaai-related-workbrycewang-stanford/Awesome-Journal-SkillsUse when positioning an AAAI paper's novelty against archival work, contemporaneous arXiv or workshop papers, and AAAI/IJCAI/NeurIPS/ICML/ICLR neighbors across the broad AI scope, while staying inside AAAI's dual-submission and AI-as-source policy constraints and writing a related-work section legible to non-specialist reviewers.ScienceNo warnings★ 1.4K↓ —
- wsdm-experimentsbrycewang-stanford/Awesome-Journal-SkillsUse when designing or auditing the evaluation of a WSDM paper - offline ranking and recommendation metrics with bias controls, temporal-split protocols for interaction logs, baseline selection from recent WSDM/SIGIR/KDD editions, ablations that isolate the mechanism, efficiency reporting, and online-evidence framing.ScienceNo warnings★ 1.4K↓ —
- jetson-optimize-memoryNVIDIA/skillsReclaim DRAM by disabling unused subsystems across MB1 BCT, MB2 BCT, kernel reserved-memory, and SWIOTLB. Use for headless or no-camera Jetson deployments; not for CPU/GPU frequency tuning.ProductivityNo warnings★ 3.9K↓ —
- vis-supplementarybrycewang-stanford/Awesome-Journal-SkillsUse when deciding what belongs in an IEEE VIS paper body versus its supplemental materials, covering the VGTC/TVCG 9+2 page budget, the supplemental video that VIS reviewers routinely watch, the one-week supplemental deadline extension, double-blind supplemental anonymization, and how to split a visualization paper between the reviewed pages and the archive.MediaNo warnings★ 1.4K↓ —
- socc-experimentsbrycewang-stanford/Awesome-Journal-SkillsUse when designing or auditing ACM SoCC evaluations, covering real or realistic deployments over simulation, production or representative workloads and traces, tail-latency and cost as first-class metrics, fair tuned baselines, scale and multi-tenancy behavior, reproducible measurement pipelines, and matching evidence to the shape of each cloud claim.DevOpsNo warnings★ 1.4K↓ —
- stoc-experimentsbrycewang-stanford/Awesome-Journal-SkillsUse when judging whether computation belongs in a STOC (ACM Symposium on Theory of Computing) paper at all — STOC accepts on theorems, with no empirical-evaluation expectation — and when it does, scoping it as certified proof computation, constructive search, or clearly labeled illustration rather than benchmarking.MediaNo warnings★ 1.4K↓ —
- pods-experimentsbrycewang-stanford/Awesome-Journal-SkillsUse when designing or auditing the analytical "evidence" of an ACM PODS paper — worst-case and average-case analyses, matching upper and lower bounds, dichotomy completeness, correct complexity assumptions, and the occasional empirical validation when a theory paper claims practicality — matching the rigor to the shape of each theoretical claim.ScienceNo warnings★ 1.4K↓ —
- popl-experimentsbrycewang-stanford/Awesome-Journal-SkillsUse when designing the empirical component of a POPL paper — deciding whether evidence should be a mechanization, a prototype, case studies, or benchmarks; reporting proof effort and case-study coverage honestly; and keeping performance numbers in a supporting role so the formal claim stays the paper's center of gravity.ScienceNo warnings★ 1.4K↓ —
- analyze-performance-rank-and-readgooseworks-ai/goose-skillsStep of the analyze-performance playbook: ranks creative by angle, hook and format with spend gates, writes short per-channel reads and drafts what to make next. Run analyze-performance instead; not for direct use.WritingNo warnings★ 1.3K↓ —
- rss-supplementarybrycewang-stanford/Awesome-Journal-SkillsJournal-specific Claude Code/Codex skill packs covering mainstream journals — AER, QJE, Nature, Cell, 管理世界, 经济研究 & 200+ more — your fast track to getting published. ｜ 覆盖主流期刊的 Claude Code/Codex 期刊技能包，从选题、识别策略到表格规范与审稿回复全流程，助你快速发论文。PlanningNo warnings★ 1.4K↓ —
- kdd-writing-stylebrycewang-stanford/Awesome-Journal-SkillsUse when revising a KDD paper into the venue's register, where the first page names a data regime, a mechanism, and scale evidence, efficiency adjectives trace to design decisions, and two-column sigconf pages punish sprawl. Covers Research vs ADS voice, dataset-size discipline, assertive contribution bullets, and 8-page compression.WritingNo warnings★ 1.4K↓ —
- indexingkostja94/marketing-skillsWhen the user wants to fix indexing issues from Search Console, use noindex, or implement Google Indexing API. Also use when the user mentions "fix indexing," "not indexed," "Crawled - currently not indexed," "discovered - currently not indexed," "index coverage," "noindex," "noindex tag," "pages not indexed," "why not indexed," "request indexing," or "Google Indexing API." For sitemap, use xml-sitemap.BrowserNo warnings★ 1.1K↓ —
- hf-cloud-serving-image-selectionhuggingface/skillsPick the right serving container for a SageMaker model deployment and find its current image URI. Use this skill whenever about to deploy a model to a SageMaker endpoint and an image URI needs to be chosen — including when the user says "deploy this LLM", "host this HuggingFace model", "serve this fine-tuned model", "deploy this embedding model", "host a reranker", "serve a sentence-transformers model", or when about to hardcode any container URI in deployment code. HuggingFace-curated Deep Learning Containers are ALWAYS preferred: HuggingFace vLLM (LLMs and generative rerankers), HuggingFace vLLM-Omni (multimodal), TEI (embeddings/cross-encoder rerankers), HF Inference Toolkit (other transformers). Generic images (AWS vLLM, DJL-LMI, SGLang) are used only when no HuggingFace image is compatible — never merely because they carry a newer version. Never hardcode a container URI from memory and never default to TGI. Prevents stale-image failures and wrong-region URIs.OfficialMediaNo warnings★ 11.3K↓ —
user-feedback-aggregation
rampstackco/claude-skills
PlanningUpdated Oct 27, 2026
From the author
Collecting and synthesizing user feedback across channels (support tickets, NPS, in-app feedback, sales calls, social mentions, customer councils) into a continuous signal that informs product decisions. The triage discipline that distinguishes loudest-voice (whoever complains most wins) from averaged-noise (every signal weighted equally) from triaged-synthesis (signal weighted by source quality, frequency, and decision relevance). Triggers on user feedback, customer feedback aggregation, NPS, support ticket analysis, customer councils, feedback synthesis, voice of customer, feedback triage, in-app feedback. Also triggers when feedback channels overflow with volume that does not produce decisions, when the loudest-voice problem is steering roadmap, or when continuous feedback streams need synthesis discipline.
Install
npx skills add rampstackco/claude-skills --skill user-feedback-aggregation -a claude-code
Installs to .claude/skills/user-feedback-aggregation/ · 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: MIT
Safety checks on SKILL.md
No warnings.
Why 88
Security100
Provenance76
Maintenance100
Licence100
Adoption49
Files (11)
- README.md
- SKILL.md
- references/categorization-and-tagging-at-scale.md
- references/channel-source-weighting.md
- references/channel-types-and-what-each-surfaces.md
- references/closing-the-loop-with-users.md
- references/common-feedback-aggregation-failures.md
- references/detecting-drift-in-feedback.md
- references/frequency-vs-intensity.md
- references/from-feedback-to-product-decision.md
- references/tooling-considerations.md