Apply when designing types, reviewing a function signature, or writing code in any statically-typed language. Make illegal states unrepresentable, brand semantic primitives, parse external data at boundaries, refuse to lie to the compiler, exhaust variants, derive from authoritative schemas.
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。
michael-denyer/pstack-claude☆ 2,0222026年10月14日 更新
Update the status of a plan step. MUST call after completing each step to track progress. Status values - pending, in_progress, completed, failed, skipped.
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。
openakita/openakita☆ 1,9982026年9月24日 更新
Get memory system statistics including total count and breakdown by type. When you need to check memory usage or understand memory distribution.
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。
openakita/openakita☆ 1,9982026年9月24日 更新
Generates a Meta-analysis results section description for funnel plots, including statistical tables (Egger's, Begg's, Trim & Fill) and figure legends. Supports English and Chinese outputs. Use when user provides a funnel plot image and statistics and wants a formatted report.
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。
aipoch/medical-research-skills☆ 1,9552026年9月17日 更新
Draft Diversity, Equity, and Inclusion statements for academic applications.
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。
aipoch/medical-research-skills☆ 1,9552026年9月17日 更新
Verifies whether a scientific or biomedical claim is actually supported by the cited original papers rather than by citation drift, overstatement, selective citation, or correlation-to-causation inflation. Use this skill whenever a user wants to check whether a repeated statement, slide claim, manuscript sentence, review assertion, or “people often say” scientific conclusion is truly supported by the underlying primary literature. Always separate the claim itself, the cited paper(s), what the paper actually showed, what it did not show, and whether later retellings drifted beyond the original evidence. Never fabricate references, findings, study features, or citation chains.
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。
aipoch/medical-research-skills☆ 1,9552026年9月17日 更新
Organizes the evidence and competitive landscape around a drug, target, or pathway by separating disease relevance, tractability, preclinical evidence, clinical evidence, modality fit, and crowding. Always map what is biologically supported, what is druggable, what has actually advanced, and what remains strategically open. Never confuse target relevance with druggability, preclinical activity with clinical promise, or narrative excitement with validated development maturity. Never fabricate references, trial status, approval status, company activity, or asset metadata.
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。
aipoch/medical-research-skills☆ 1,9552026年9月17日 更新
Reconstruct your recent working context from your own chat history, live state, and the shared record (user reports, prior fixes, incidents), then hand back a tight current-state brief. Use for 'recall my work on X', 'catch me up', 'what have I been working on', 'where did I leave off', before starting or resuming work.
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。
backnotprop/pstack☆ 1,8652026年10月14日 更新
Apply when concurrent actors might write to the same file, branch, key, or state object. Eliminate the sharing first; serialize structurally only when one shared writer is a real invariant.
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。
backnotprop/pstack☆ 1,8652026年10月14日 更新
Apply when designing types, reviewing a function signature, or writing code in any statically-typed language. Make illegal states unrepresentable, brand semantic primitives, parse external data at boundaries, refuse to lie to the compiler, exhaust variants, derive from authoritative schemas.
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。
backnotprop/pstack☆ 1,8652026年10月14日 更新
Use this skill when the user wants to build a financial model — 3-statement model, DCF valuation, LBO, SaaS unit economics, sensitivity / scenario analysis, debt schedule, or fundraising projections — in Excel. Trigger on: 'financial model', '3-statement model', 'P&L + BS + CF', 'DCF', 'WACC', 'NPV', 'terminal value', 'LBO', 'debt schedule', 'cash sweep', 'MOIC', 'IRR / XIRR', 'sensitivity table', 'scenario analysis', 'ARR model', 'unit economics', 'CAC / LTV', 'cap table forecast'. Output is a single formula-driven .xlsx. This skill is a scene layer on top of officecli-xlsx — it inherits every xlsx v2 rule (4-color code, visual floor, number formats, cache-drift, Known Issues, Delivery Gate minimum cycle). DO NOT invoke for a simple budget tracker, CSV dump, or operational KPI sheet — route those to officecli-xlsx base.
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。
zhu1090093659/deepseek-pp☆ 1,8642026年8月14日 更新
List cities for a given state using the bundled background data. Use this skill to validate state inputs or expand destination choices before flight/restaurant/attraction/driving/accommodation lookups.
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。
benchflow-ai/skillsbench☆ 1,8492026年10月13日 更新
Quantum mechanics simulations and analysis using QuTiP (Quantum Toolbox in Python). Use when working with quantum systems including: (1) quantum states (kets, bras, density matrices), (2) quantum operators and gates, (3) time evolution and dynamics (Schrödinger, master equations, Monte Carlo), (4) open quantum systems with dissipation, (5) quantum measurements and entanglement, (6) visualization (Bloch sphere, Wigner functions), (7) steady states and correlation functions, or (8) advanced methods (Floquet theory, HEOM, stochastic solvers). Handles both closed and open quantum systems across various domains including quantum optics, quantum computing, and condensed matter physics.
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。
benchflow-ai/skillsbench☆ 1,8492026年10月13日 更新
World-class data science skill for statistical modeling, experimentation, causal inference, and advanced analytics. Expertise in Python (NumPy, Pandas, Scikit-learn), R, SQL, statistical methods, A/B testing, time series, and business intelligence. Includes experiment design, feature engineering, model evaluation, and stakeholder communication. Use when designing experiments, building predictive models, performing causal analysis, or driving data-driven decisions.
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。
benchflow-ai/skillsbench☆ 1,8492026年10月13日 更新
Download flood stage thresholds from NWS (National Weather Service). Use when determining flood levels for USGS stations, accessing action/minor/moderate/major flood stages, or matching stations to their flood thresholds.
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。
benchflow-ai/skillsbench☆ 1,8492026年10月13日 更新
Geospatial routing data handling for depot and station coordinates, route node IDs, internal index mappings, great-circle distance matrices, and route-distance reconstruction. Use when optimization or reporting tasks involve latitude/longitude, station IDs, depots, distance metrics, vehicle routes, or validating travel distance from reported paths.
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。
benchflow-ai/skillsbench☆ 1,8492026年10月13日 更新
Assist in designing optimal DP states and transitions
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。
a5c-ai/babysitter☆ 1,8462026年9月17日 更新
State capture and restore across context window compactions. Monitors usage thresholds and serializes quality, task, and spec state for seamless continuation.
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。
a5c-ai/babysitter☆ 1,8462026年9月17日 更新
Write or review UI copy — buttons, errors, empty states, microcopy, notifications, labels — using the voice & tone system (clear, concise, useful, human, honest) with the what→why→how error formula and inclusive-language rules. Use when the user needs interface copy, error messages, empty-state text, or a copy review.
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。
plugin87/ux-ui-agent-skills☆ 1,7702026年10月11日 更新
Display the current state of the FPF knowledge base
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。
NeoLabHQ/context-engineering-kit☆ 1,7672026年8月27日 更新
Expert Terraform/OpenTofu specialist mastering advanced IaC automation, state management, and enterprise infrastructure patterns. Handles complex module design, multi-cloud deployments, GitOps workflows, policy as code, and CI/CD integration. Covers migration strategies, security best practices, and modern IaC ecosystems. Use PROACTIVELY for advanced IaC, state management, or infrastructure automation.
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。
rmyndharis/antigravity-skills☆ 1,7642026年10月1日 更新
Expert data scientist for advanced analytics, machine learning, and statistical modeling. Handles complex data analysis, predictive modeling, and business intelligence. Use PROACTIVELY for data analysis tasks, ML modeling, statistical analysis, and data-driven insights.
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。
rmyndharis/antigravity-skills☆ 1,7642026年10月1日 更新
Display project status, active tracks, and next actions
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。
rmyndharis/antigravity-skills☆ 1,7642026年10月1日 更新
Worked qupai-system (曲牌体) opera scripts, act by act. Guan Hanqing's three surviving zaju (窦娥冤/救风尘/金线池 — twelve acts, 126 tunes, one mode and one rhyme per act, sung/spoken ratios); Tang Xianzu's 牡丹亭 (55 scenes, ~420 tunes, 集唐 closing quatrains, the 圆驾 south-north composite suite); Kong Shangren's 桃花扇 (44 scenes plus four unnumbered, the 副末 frame line, upper/lower halves, a fan's seven transformations, his sixteen 凡例); 长生殿 (Wang Jilie's fifty-scene staging table); Wei Minglun's 巴山秀才 (1983 Sichuan 高腔, 43 tunes, nine 帮腔 passages). Every case states its edition and collation status, then structure, tunes, stage conventions and transferable devices. Method is in sw-chinese-opera-qupai; this skill holds the worked scripts. Use when you want a real example of how a 传奇 or 杂剧 is assembled, need actual scene counts and tune counts to imitate, are judging where a famous script breaks prosody, or need models for 副末开场, 集唐下场诗, 南北合套 or 帮腔 (曲牌体全本/牡丹亭/桃花扇/窦娥冤/救风尘/长生殿/巴山秀才/唱段表/结构表).
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。
jtydhr88/screenwriting-skills☆ 1,6692026年10月3日 更新