Codified expertise for demand forecasting, safety stock optimization, replenishment planning, and promotional lift estimation at multi-location retailers. Informed by demand planners with 15+ years experience managing hundreds of SKUs. Includes forecasting method selection, ABC/XYZ analysis, seasonal transition management, and vendor negotiation frameworks. Use when forecasting demand, setting safety stock, planning replenishment, managing promotions, or optimizing inventory levels.
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。
sayasaya8039/ZWG_Terminal☆ 22026年6月8日 更新
This skill enforces Sentry's blog writing standards across every post — whether you're helping an engineer write their first blog post or a marketer draft a product announcement.
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。
MMEHDI0606/ai-agent-foundation-template☆ 22026年5月21日 更新
Every product will be AI-powered. The question is whether you'll build it right or ship a demo that falls apart in 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。
MMEHDI0606/ai-agent-foundation-template☆ 22026年5月21日 更新
Enter explore mode - a thinking partner for exploring ideas, investigating problems, and clarifying requirements. Use when the user wants to think through something before or during a change.
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。
defin85/1c-runner-agnostic-template☆ 22026年10月11日 更新
Генерация перехватчика метода в расширении 1С (CFE). Используй когда ну…
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。
defin85/1c-runner-agnostic-template☆ 22026年10月11日 更新
Remove refusal behaviors from open-weight LLMs using OBLITERATUS — mechanistic interpretability techniques (diff-in-means, SVD, whitened SVD, LEACE, SAE decomposition, etc.) to excise guardrails while preserving reasoning. 9 CLI methods, 28 analysis modules, 116 model presets across 5 compute tiers, tournament evaluation, and telemetry-driven recommendations. Use when a user wants to uncensor, abliterate, or remove refusal from an LLM.
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。
ibragimov-oasis/vibe-coder☆ 22026年6月24日 更新
Parameter-efficient fine-tuning for LLMs using LoRA, QLoRA, and 25+ methods. Use when fine-tuning large models (7B-70B) with limited GPU memory, when you need to train <1% of parameters with minimal accuracy loss, or for multi-adapter serving. HuggingFace's official library integrated with transformers ecosystem.
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。
ibragimov-oasis/vibe-coder☆ 22026年6月24日 更新
Create a delightful, unexpected "wow" experience for the user by dynamically discovering and creatively combining other enabled skills. Triggers when the user says "surprise me" or any request expressing a desire for an unexpected creative showcase. Also triggers when the user is bored, wants inspiration, or asks for "something interesting".
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。
ibragimov-oasis/vibe-coder☆ 22026年6月24日 更新
Use this skill when the user requests to review, analyze, critique, or summarize academic papers, research articles, preprints, or scientific publications. Supports comprehensive structured reviews covering methodology assessment, contribution evaluation, literature positioning, and constructive feedback generation. Trigger on queries involving paper URLs, uploaded PDFs, arXiv links, or requests like "review this paper", "analyze this research", "summarize this study", or "write a peer 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。
ibragimov-oasis/vibe-coder☆ 22026年6月24日 更新
Use this skill instead of WebSearch for ANY question requiring web research. Trigger on queries like "what is X", "explain X", "compare X and Y", "research X", or before content generation tasks. Provides systematic multi-angle research methodology instead of single superficial searches. Use this proactively when the user's question needs online information.
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。
ibragimov-oasis/vibe-coder☆ 22026年6月24日 更新
Set up GitHub authentication for the agent using git (universally available) or the gh CLI. Covers HTTPS tokens, SSH keys, credential helpers, and gh auth — with a detection flow to pick the right method automatically.
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。
ibragimov-oasis/vibe-coder☆ 22026年6月24日 更新
Replicate and validate a GitHub issue by spinning up Archon, analyzing the issue, and systematically testing all described symptoms using browser automation. Use when: User wants to reproduce a bug, validate a GitHub issue, confirm a reported problem, or investigate whether an issue is real before working on a fix. Triggers: "replicate issue", "reproduce issue", "validate issue", "confirm bug", "test issue", "can you reproduce", "try to replicate", "verify the bug". Capability: Checks out main, pulls latest, starts Archon, reads the GitHub issue, then uses agent-browser to systematically test every symptom and produce a findings report. NOT for: Fixing issues (use /archon or /exp-piv-loop:fix-issue), general UI testing (use /validate-ui).
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。
ibragimov-oasis/vibe-coder☆ 22026年6月24日 更新
Use when the user wants to know what will break if they change something, or needs safety analysis before editing code. Examples: "Is it safe to change X?", "What depends on this?", "What will break?"
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。
ibragimov-oasis/vibe-coder☆ 22026年6月24日 更新
Query Base (Ethereum L2) blockchain data with USD pricing — wallet balances, token info, transaction details, gas analysis, contract inspection, whale detection, and live network stats. Uses Base RPC + CoinGecko. No API key required.
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。
ibragimov-oasis/vibe-coder☆ 22026年6月24日 更新
Use when the user is debugging a bug, tracing an error, or asking why something fails. Examples: "Why is X failing?", "Where does this error come from?", "Trace this bug"
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。
ibragimov-oasis/vibe-coder☆ 22026年6月24日 更新
Use this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like "the xlsx in my downloads") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.
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。
David-Li0406/meta-skill-evloving☆ 22026年7月14日 更新
Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).
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。
David-Li0406/meta-skill-evloving☆ 22026年7月14日 更新
Use when confirming whether a dispatched agent task was actually received, activated, and progressing after sessions_send or other task handoff 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。
aAAaqwq/openclaw-team☆ 22026年6月18日 更新
Architecture Decision Records (ADR) — a lightweight method for capturing important architectural decisions with context, trade-offs, and rationale. Based on Michael Nygard's ADR pattern and Google/Meta/ByteDance's design documentation culture. USE WHEN: deciding between technology choices, designing new architecture, changing existing architecture, documenting why a decision was made, onboarding new team members, or preserving design context for future maintainers. Triggered by terms like "tech choice", "architecture decision", "why did we choose", "design rationale", "ADR", or "architectural decision record".
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。
aAAaqwq/openclaw-team☆ 22026年6月18日 更新
Systematic debugging methodology covering root cause analysis, log-based debugging, production debugging, memory leak diagnosis, deadlock detection, performance anomaly investigation, and reproducible bug reproduction. Distilled from real experience at Google, DeepMind, ByteDance, and Huawei. USE WHEN: investigating production incidents, debugging hard-to-reproduce bugs, analyzing crash dumps, finding race conditions, diagnosing memory leaks, debugging performance regressions, or any situation requiring systematic root cause analysis. Triggers on "debugging", "root cause", "bug", "crash", "segfault", "deadlock", "race condition", "memory leak".
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。
aAAaqwq/openclaw-team☆ 22026年6月18日 更新
Systematic performance profiling and optimization methodology. Covers CPU profiling, memory profiling, heap analysis, goroutine/thread analysis, FlameGraph generation, latency profiling, database query profiling, network I/O profiling, and continuous performance regression detection. USE WHEN: investigating performance issues, optimizing slow code, reducing memory usage, diagnosing GC/CPU bottlenecks, finding N+1 queries, analyzing pprof/flamegraph output, or establishing a performance baseline. Triggers on "profiling", "performance analysis", "slow code", "flame graph", "pprof", "heap dump", "memory leak", "CPU spike", "bottleneck".
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。
aAAaqwq/openclaw-team☆ 22026年6月18日 更新
Track real-time cryptocurrency prices across exchanges with historical data and alerts. Provides price data infrastructure for dependent skills (portfolio, tax, DeFi, arbitrage). Use when checking crypto prices, monitoring markets, or fetching historical price data. Trigger with phrases like "check price", "BTC price", "crypto prices", "price history", "get quote for", "what's ETH trading at", "show me top coins", or "track my watchlist".
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。
aAAaqwq/openclaw-team☆ 22026年6月18日 更新
Tactical negotiation framework based on Chris Voss's "Never Split the Difference." Use when preparing for negotiations, during live negotiation scenarios, analyzing counterpart behavior, crafting responses to difficult conversations, handling objections, salary/contract negotiations, or when asked about negotiation techniques like mirroring, labeling, calibrated questions, or the Ackerman method.
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。
aAAaqwq/openclaw-team☆ 22026年6月18日 更新
Research markets with sizing, segmentation, competitor mapping, pricing checks, and demand validation that turn fuzzy ideas into decision-ready evidence. Use when (1) you need TAM, SAM, SOM, whitespace, or category sizing; (2) you must compare competitors, pricing, positioning, or customer segments before acting; (3) the user asks whether a niche, launch, expansion, or go-to-market bet is actually worth pursuing.
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。
aAAaqwq/openclaw-team☆ 22026年6月18日 更新