Determine whether an app is ready to submit, then drive the current App Store release flow with asc, including validation, staging, review submission, first-time availability, subscriptions, IAP, Game Center, and App Privacy checks.
日本語の概要は準備中です。原文の説明を表示しています。
CamilleScholtz/swmpc☆ 2392026年10月6日 更新
Use when the user has a vague research direction and wants to explore feasible specific topics. Outputs a structured analysis with candidate topics, innovation/feasibility scoring, and a pre-survey of 20–30 representative works. Single-stage, no Python runtime.
日本語の概要は準備中です。原文の説明を表示しています。
ai4s-research/ai4s-skills☆ 2372026年7月28日 更新
Use when capturing ONE new idea the user voices and wants recorded — "save this idea", "I have an idea", "log this idea", "/idea", "idea: ...". Creates a provenance-tracked folder (one folder per idea) in your ideas repo, dedups against an index, optionally mirrors to a GitHub Project view filtered by label:idea. NOT for mining ideas out of transcripts/meetings (use a dedicated extraction skill) and NOT for dated work tasks (use an issue tracker / the manager skill). Triggers also in Russian: «запиши идею», «у меня идея», «зафиксируй идею».
日本語の概要は準備中です。原文の説明を表示しています。
serejaris/personal-corp-os☆ 2302026年10月8日 更新
Use when choosing between engines or models for an agent pipeline and the answer must come from measurement on your own data, not from marketing pages: speech recognition for meeting recordings, the model that turns a transcript into notes, the critic model that checks it. Runs every variant in an isolated container, measures time, cost per hour of input (USD and RUB, with price source and date) and quality (WER/CER and course terms for ASR; code checks plus a judge of a different model for LLM steps), writes results.json/csv and one static analytics page. Triggers on "бенчмарк", "сравни движки", "сколько стоит час записи", "какую модель взять для пайплайна", "benchmark the pipeline", "compare ASR engines".
日本語の概要は準備中です。原文の説明を表示しています。
serejaris/personal-corp-os☆ 2302026年10月8日 更新
| Nuxt 4 data management: composables, data fetching with useFetch/useAsyncData, and state management with useState and Pinia. Use when: creating custom composables, fetching data with useFetch or useAsyncData, managing global state with useState, integrating Pinia, debugging reactive data issues, or implementing SSR-safe state patterns.
日本語の概要は準備中です。原文の説明を表示しています。
secondsky/claude-skills☆ 2292026年9月28日 更新
Run a cumulative daily or retrospective sweep of research papers on a chosen topic, audit their claims, methods, integrity signals, and independent support, then identify overlooked but feasible project or business opportunities in a detailed source-grounded report. Use when asked to monitor papers every day, mine buried research, evaluate whether a paper is credible or reproducible, find unimplemented research ideas, or separate promising work from hype, weak evidence, and retracted or contradicted results.
日本語の概要は準備中です。原文の説明を表示しています。
tamdogood/builder-essential-skills☆ 2232026年8月17日 更新
Direct, build and render premium cinematic motion design from code — launch films, product and feature videos, looping feature animations for landing pages and social, logo stings and reveals, UI walkthroughs, app demos, kinetic type, teasers, trailers, intros and promo clips. Use this whenever the user wants any video, animation, motion graphic or animated demo made with Remotion, HyperFrames, HTML/GSAP or React, or asks to storyboard, time, score, render, polish or critique one — even if they only say "make a video about X", "animate this feature" or "we need a launch clip". It supplies concept and copy discipline, type and colour taste that defers to a supplied brand, a beat-locked timeline that drives both picture and a synthesized soundtrack, choreography and transition craft, real product UI in motion, film-grade finishing (true motion blur, no banding, BT.709, sync-checked mux) and a measured self-critique loop on contact sheets and renders.
日本語の概要は準備中です。原文の説明を表示しています。
Leonxlnx/cinetic☆ 2202026年10月14日 更新
Design reproducible evaluations for AI agents with representative task sets, explicit rubrics, appropriate graders, baselines, regression gates, and failure analysis. Use when defining agent quality, comparing prompts or models, validating a release, measuring tool-use reliability, investigating regressions, or deciding whether an agent is ready for production.
日本語の概要は準備中です。原文の説明を表示しています。
seb1n/awesome-ai-agent-skills☆ 2142026年8月10日 更新
The order of operations for an Active Directory penetration test: setup, collection, exploitation, post-processing. Use this whenever you are planning or driving an AD assessment and need to know what to run before what and why (map before you exploit; harvest easy credentials before spraying to avoid lockouts; collect the graph before you reason about paths). Covers phase sequencing with standard tooling: netexec/nxc, impacket, certipy, bloodyAD, kerbrute, BloodHound CE. Invoke it at the start of an engagement, when deciding the next phase, or when a step feels out of order.
日本語の概要は準備中です。原文の説明を表示しています。
ADScanPro/Claude-AD☆ 2112026年8月25日 更新
Think and work like an expert Mechanical Design Engineer. Use when a task calls for Mechanical Design Engineer judgment. Reasons from function, datum reference frames, and tolerance budgets; releases inspectable drawings and MBD through ASME Y14.5 GD&T, WC/RSS/Monte Carlo stack-ups, SAE J1739 DFMEA (Action Priority), and Boothroyd–Dewhurst DFM/DFA—not stress plots alone.
日本語の概要は準備中です。原文の説明を表示しています。
K-Dense-AI/scientific-agents☆ 2102026年10月3日 更新
Think and work like an expert Low-Temperature Physicist. Use when a task calls for Low-Temperature Physicist judgment. Reasons from kT budgets, He-3/He-4 dilution refrigeration, and BCS/GL superconductivity; measures Tc, QHE, and Landauer conductance with lock-in/SQUID workflows while treating wiring heat loads, Kapitza resistance, flux trapping, TLS dielectric loss, and sample-vs-MXC thermometer mismatch as first-class failure modes.
日本語の概要は準備中です。原文の説明を表示しています。
K-Dense-AI/scientific-agents☆ 2102026年10月3日 更新
Think and work like an expert MEMS Engineer. Use when a task calls for MEMS Engineer judgment. Reasons from scale-dependent mechanics, squeeze-film damping, and electrostatic pull-in through DRIE Bosch/surface micromachining, CoventorMP/COMSOL, foundry PDKs, LDV/WLI metrology, and AEC-Q103 qual while treating release stiction, DRIE scallop bias, package-stress offset drift, and functional-WLT-vs-reliability gaps as first-class failure modes.
日本語の概要は準備中です。原文の説明を表示しています。
K-Dense-AI/scientific-agents☆ 2102026年10月3日 更新
Think and work like an expert Microbial Ecologist. Use when a task calls for Microbial Ecologist judgment. Reasons from Vellend assembly (selection, dispersal, drift, diversification), compositional stats (ANCOM-BC2, MaAsLin2, Aitchison), and SILVA/GTDB/EMP workflows; treats kitome contamination, GCN bias, pseudoreplication, SparCC-as-interaction, and PICRUSt2-as-measured-function as first-class failure modes.
日本語の概要は準備中です。原文の説明を表示しています。
K-Dense-AI/scientific-agents☆ 2102026年10月3日 更新
Think and work like an expert Mathematical Physicist. Use when a task calls for Mathematical Physicist judgment. Reasons from Hilbert-space domains, Wightman/OS and Haag–Kastler axioms, constructive QFT, Gibbs measures, and spectral/scattering theory; uses Reed–Simon, Glimm–Jaffe, MathSciNet/math-ph, while treating wrong self-adjoint extensions, invalid Wick rotation, limit-order swaps, and lattice-as-continuum claims as first-class failure modes.
日本語の概要は準備中です。原文の説明を表示しています。
K-Dense-AI/scientific-agents☆ 2102026年10月3日 更新
Think and work like an expert Microbial Physiologist. Use when a task calls for Microbial Physiologist judgment. Reasons from Monod/chemostat (μ = D), YX/S and Pirt maintenance, Crabtree/overflow, 13C-MFA and FBA, and BMSAB taxonomy; treats OD-as-biomass yield error, FBA-as-measured-flux, washout misread, and portable ms across media as first-class failure modes.
日本語の概要は準備中です。原文の説明を表示しています。
K-Dense-AI/scientific-agents☆ 2102026年10月3日 更新
Think and work like an expert Knowledge Representation Researcher. Use when a task calls for Knowledge Representation Researcher judgment. Reasons from model-theoretic semantics, the expressivity-vs-decidability-vs-scalability tradeoff, and competency questions through OWL 2 profiles, reasoners (HermiT, Pellet, ELK), ROBOT/Protégé pipelines, and SHACL validation while treating unsatisfiable classes, silent OWA-vs-CWA semantic mixing, hallucinated LLM-suggested axioms, and IRI-reuse on bad merges as first-class failure modes.
日本語の概要は準備中です。原文の説明を表示しています。
K-Dense-AI/scientific-agents☆ 2102026年10月3日 更新
Think and work like an expert MLOps Engineer. Use when a task calls for MLOps Engineer judgment. Reasons from data contracts, feature parity, evaluation gates, and rollback-readiness through MLflow/W&B registries, Feast feature stores, KServe/Triton serving, Great Expectations/TFDV validation, and Evidently PSI/KS drift monitors while treating train-serve skew, data leakage, silent degradation, and schema/concept drift as first-class failure modes.
日本語の概要は準備中です。原文の説明を表示しています。
K-Dense-AI/scientific-agents☆ 2102026年10月3日 更新
Think and work like an expert Immunotherapy Scientist. Use when a task calls for Immunotherapy Scientist judgment. Reasons from antigen recognition and checkpoint circuits through CAR-T/bispecific design, ASTCT CRS/ICANS grading, flow cytometry release CQAs, iRECIST response assessment, COMPASS/TIDE biomarker modeling, and JACIE/FACT IEC accreditation while treating antigen escape, pseudoprogression, tonic signaling, and step-up CRS as first-class failure modes.
日本語の概要は準備中です。原文の説明を表示しています。
K-Dense-AI/scientific-agents☆ 2102026年10月3日 更新
Think and work like an expert Information Retrieval Scientist. Use when a task calls for Information Retrieval Scientist judgment. Reasons from the Probability Ranking Principle, Saracevic's relevance layers, and Cranfield pooling through tuned BM25, SPLADE/ColBERT/dense and LLM reranking, trec_eval/ir_measures with paired topic-level tests, interleaving, and nugget-based RAG evaluation while treating unjudged-as-nonrelevant pools, position-biased clicks, LLM-judge circularity, single-vector embedding limits, and benchmark contamination as first-class failure modes.
日本語の概要は準備中です。原文の説明を表示しています。
K-Dense-AI/scientific-agents☆ 2102026年10月3日 更新
Think and work like an expert Geodesist. Use when a task calls for Geodesist judgment. Reasons from coordinates as four-dimensional objects with epoch, velocity, and frame realization (ITRS vs. ITRF2020, WGS84) through GAMIT/GLOBK and Bernese PPP-AR, SBAS/PS-InSAR with GACOS atmospheric correction, IERS Conventions, and 14-parameter Helmert transforms while treating ITRF-realization switches, undocumented APC/ATX mismatches, monument motion, and unscreened seasonal loading as first-class failure modes.
日本語の概要は準備中です。原文の説明を表示しています。
K-Dense-AI/scientific-agents☆ 2102026年10月3日 更新
Think and work like an expert Geologist. Use when a task calls for Geologist judgment. Reasons from Steno's principles and Walther's Law through Brunton strike/dip, measured sections, hand-lens rock ID (QAPF/Folk/Dunham), thin-section petrography (PPL/XPL, Michel-Lévy, point counting), FGDC/GeMS geologic maps, NGMDB/Geolex/Macrostrat, and stereonet structural analysis while treating weathering, float, and map-as-hypothesis as first-class failure modes.
日本語の概要は準備中です。原文の説明を表示しています。
K-Dense-AI/scientific-agents☆ 2102026年10月3日 更新
Think and work like an expert Genomicist. Use when a task calls for Genomicist judgment. Reasons from reference-relative coordinates, haplotypes, variant classes, and sequencing-as-measurement through GATK/DeepVariant, VEP/ClinVar/gnomAD, GIAB/hap.py benchmarking, and ACMG/AMP-ClinGen frameworks while treating build mismatches, paralog/pseudogene and GC dropout artifacts, contamination and index hopping, batch effects, and annotation drift as first-class failure modes.
日本語の概要は準備中です。原文の説明を表示しています。
K-Dense-AI/scientific-agents☆ 2102026年10月3日 更新
Think and work like an expert Herpetologist. Use when a task calls for Herpetologist judgment. Reasons from detectability-limited sampling and ectotherm phenology through VES, pitfall/drift-fence and cover-board arrays, NAAMP call surveys, Program MARK CJS, unmarked/occuTTD occupancy, and Bd/Bsal MW113 qPCR biosecurity; uses AmphibiaWeb, Reptile Database, SSAR 9th ed./CNAH names, Amphibian Disease Portal, GBIF/CoordinateCleaner, and ARRIVE 2.0 while treating weather bias, pitfall selectivity, and pathogen cross-contamination as first-class failure modes.
日本語の概要は準備中です。原文の説明を表示しています。
K-Dense-AI/scientific-agents☆ 2102026年10月3日 更新
Think and work like an expert High-Energy Astrophysicist. Use when a task calls for High-Energy Astrophysicist judgment. Reasons from Compton/synchrotron radiative processes and compact-object energetics through HEASARC/Fermi/Swift/XMM/Chandra/NuSTAR/XRISM pipelines, XSPEC/Sherpa spectral fitting, pile-up and background systematics, blazar/GRB/TDE campaigns, and GCN multi-messenger coordination while treating RMF versioning, soft-proton flares, and look-elsewhere significance as first-class failure modes.
日本語の概要は準備中です。原文の説明を表示しています。
K-Dense-AI/scientific-agents☆ 2102026年10月3日 更新