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.
49,409 skills from 2,375 repositories · updated 3 hours ago
48,362 skills
- icse-experimentsbrycewang-stanford/Awesome-Journal-SkillsUse when designing or auditing the evaluation of an ICSE research-track paper, covering subject and benchmark selection, baseline fairness, statistical tests and effect sizes as SE reviewers expect them, qualitative-methods rigor, ablations for AI-based techniques, and threats-driven study design.TestingNo warnings★ 1.4K↓ —
- imc-writing-stylebrycewang-stanford/Awesome-Journal-SkillsUse when drafting or revising the prose of an ACM IMC measurement paper, covering the first-page measurement finding, making vantage points and methodology auditable, longitudinal framing for a moving Internet, arguing limitations rather than reciting them, writing the Ethics section early, and acmart page-budget discipline.WritingNo warnings★ 1.4K↓ —
- searchtaishi-i/awesome-japanese-nlp-resourcesSearch all Japanese NLP resources (libraries, models, datasets, tutorials, dictionaries, Hugging Face). Accepts keywords or natural language questions in any language. Use whenever the user asks which Japanese NLP resource to use, or wants to find one: tokenizers / morphological analyzers, BERT or LLM models, embeddings, NER, text classification, datasets / corpora, dictionaries, tutorials, or Hugging Face models. Trigger phrases include '日本語の形態素解析ライブラリ', 'おすすめの日本語tokenizer', '日本語BERTモデル', '日本語の感情分析データセット', '日本語LLM 一覧', 'which Japanese embedding model', 'Japanese NER library'.OtherNo warnings★ 1K↓ —
- crabboxUpdatedopenclaw/crabboxDetect and use Crabbox for repository tests and validation on remote runners. Use when crabbox.yaml or .crabbox.yaml exists, the crabbox CLI is available, or work needs remote compute, a clean or reusable environment, target-platform coverage, or auditable execution evidence.TestingNo warnings★ 1.5K↓ —Also in 1 other repo
- lancet-writingbrycewang-stanford/Awesome-Journal-SkillsUse to structure and hold the length of a Lancet Article main text — Introduction, Methods, Results, Discussion (~3000–3500 words), the Research in context panel, a limited reference list (~30), and a cautious, globally minded Discussion that does not overstate.WritingNo warnings★ 1.4K↓ —
- ipsn-experimentsbrycewang-stanford/Awesome-Journal-SkillsUse when designing or auditing IPSN-lineage evaluations, covering real testbeds and deployments, ground-truth instrumentation, energy/latency/footprint measurement on real hardware, on-device/TinyML profiling, estimation-theoretic baselines and bounds, and matching evidence to the shape of each sensing claim across the IP and SPOTS tracks.DevOpsNo 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↓ —
- 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↓ —
- pldi-experimentsbrycewang-stanford/Awesome-Journal-SkillsUse when designing or auditing a PLDI evaluation — choosing defensible benchmark suites and baseline compiler configurations, measuring runtime, compile time, and memory with warmup and variance discipline, running ablations that isolate the claimed mechanism, and scoping claims to the platforms measured.ProductivityNo 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↓ —
- 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↓ —
- 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↓ —
- 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↓ —
- 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↓ —
- sosp-experimentsbrycewang-stanford/Awesome-Journal-SkillsUse when designing or auditing the evaluation of a SOSP paper — mapping every claim to an experiment, choosing baselines a systems PC will accept as fair, mixing microbenchmarks with end-to-end and failure runs, reporting tails and overheads honestly, and isolating the mechanism the design credits.DesignNo 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↓ —
- 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↓ —
- 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↓ —
- wacv-experimentsbrycewang-stanford/Awesome-Journal-SkillsUse when designing or auditing WACV experiments, covering Applications-track systems evidence (latency, power, robustness under real constraints) versus Algorithms-track matched-baseline novelty, comparative assessment under the deployed condition, uncertainty over seeds and sessions, ablations, and evidence that survives the two-round review.DevOpsNo 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↓ —
- 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↓ —
- krexahashgraph-online/awesome-codex-pluginsUse this skill when the user wants to research a trading idea, or turn an idea, post, reel or video into a tested strategy and deploy it as a live trading agent on Krexa. Covers Solana spot and tokenized stocks (xStocks), perps (Hyperliquid, Jupiter Perps, Polymarket Perps), Polymarket prediction markets, and perps in the user's own Binance, Bybit, OKX, Kraken or Coinbase account. Krexa runs the research, backtests and agents on its own servers and enforces trading limits in code. The key can ask for a deploy but cannot move money; the user approves every live deploy in the Krexa app.DevOpsNo warnings★ 1.5K↓ —
- 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↓ —
- cav-review-processbrycewang-stanford/Awesome-Journal-SkillsUse when reasoning about how a CAV (Computer Aided Verification) submission is evaluated, covering the two-stage reviewing process (two reviews then an early-reject filter, then two more reviews with a rebuttal), the partial double-anonymity by category, the accept/reject outcome, the optional non-conditional artifact evaluation, and how CAV differs from TACAS and FMCAD.EngineeringNo warnings★ 1.4K↓ —
os-say-simple
kharmanskyi/open-steps
WritingUpdated Oct 5, 2026
From the author
ALWAYS invoke this skill when the user asks for simpler or shorter about something said or written - "say it simply", "what does this mean", "I don't understand your answer", "too long", "wait, what?", "bro" - in any language, about any text: your own answer, a report, a review comment, an error. "I don't understand what to DO" is os-step-by-step; this skill restates text. It restates for a reader who does not read code: leads with the point, keeps every number, warning and caveat - no facts added, no bad news dropped. A number returns exactly that many points, most important first.
Install
npx skills add kharmanskyi/open-steps --skill os-say-simple -a claude-code
Installs to .claude/skills/os-say-simple/ · 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
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Why 88
Security100
Provenance76
Maintenance100
Licence100
Adoption49
Files (2)
- SKILL.md
- references/01-wall-of-text.md