「one」の検索結果

Use this skill when implementing a Figma design as code (design → code) — the read-FROM-Figma direction. Triggers: 'implement this Figma design', 'build this screen from Figma', 'turn this Figma into code', 'code this up from Figma', 'design to code', or a figma.com design URL provided alongside a codebase. Encodes the workflow for reading a node out of Figma with get_design_context and adapting its reference output to the target project — reusing existing components, tokens, and conventions, and honoring Code Connect mappings, component docs, design annotations, and design tokens by priority. Complements figma-code-connect (component mapping) and figma-generate-design / figma-use (the reverse, code → design direction).

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。

Draws original hand-authored SVG spot illustrations in an outlined cartoon style for design and coding subjects. Identifying traits: a warm near-black ink outline on every flat fill in three weights (2.5 / 2 / 1.5 px), tapered ink slivers for folds, brows and finger splits, big-headed characters with huge white eyes, thick brows, open smiles and pink blush, blob hair with white highlight dashes, and a small decor set of "+" sparkle crosses, outline rings, squares and coloured dots on a transparent card. Covers one character with one hero prop, gripping and holding hands, sneakers, clothing folds and UI props. Use when someone asks for an outlined, inked, comic, sticker or doodle-style cartoon, a friendly character or mascot, or a spot illustration for an empty state, onboarding, 404 or error page, success state, blog header, feature spot or marketing card.

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。

Draws original design and coding scenes as hand-authored SVG cards (480x360) in a two-colour brush-marker style. Identifying traits: thick wobbly black marker lines with blobby ends, built as filled ribbons; one second ink, mint green, in flat blocks that slip 2-10 px off the line like a misregistered print; solid black hair, trousers and shoes with mint highlight dashes; and a crinkly squiggle camo on cream areas, all on warm cream paper. Characters are chunky, with profile heads, big noses, dot eyes, big hands and feet. It covers people coding, thinking, carrying and resting with laptops, mugs, chairs and rubber ducks. Use when someone asks for brush, marker, felt-tip, two-colour, duotone, riso-print, zine, mint-and-black or doodle illustrations, or for empty states, onboarding, 404 pages, blog headers, feature spots or marketing cards with a bold hand-made look.

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。

Draws true-isometric diorama illustrations of developer and design workspaces in one green ramp, generated as SVG with a bundled scene kit. Traits: one rounded off-white slab with a faceted bevelled edge; every face filled by the way it faces (top lightest, right darkest) from five greens; 0.9 deep-green outlines on every edge; dense fine repeated detail (rack units, ribs, vents, keys, code lines); soft green shadows and glows on the slab. Covers projection, shading, line weights, object vocabulary, layout and the iso.mjs kit (racks, databases, laptops, containers, cards, badges, books, plants, cables, status tower). Use when someone asks for isometric illustration, an iso scene, 2.5D tech art, monochrome or green isometric art, a server room, dev stack or cloud picture, or art for empty states, onboarding, 404 pages, blog headers, feature spots or marketing cards in this style.

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。

Build the effects of a water and crystal skill as one visual language in Three.js. Water that reads as clear water, never a teal fill or a textured ball. A ray-marched water orb whose surface swells, rings and deforms in place: it refracts the frame behind it with an inverted horizon, has a silver rim and sharp highlights, and shows a faint caustic web on its far wall. Streams wound up from tide pools as uneven beaded ropes with silver edges, drips and shed drops. A lash that unspools from the orb, whips and pours into a pool under a torn, lopsided splash crown of aerated white water with spikes breaking into drops. Analytic ripple rings and rain on the pools. Crystals traced against their own half-space planes (Snell, Fresnel, total internal reflection, exit dispersion, Beer–Lambert, fracture discs, a white-hot root glow and a resonance band that climbs the stone), growing out of the water in a draining veil, then shattering into Voronoi shards that fall, tumble and rest with real contact on a heightfield. Caustics on the pool floor, and a moonlit tidal rock flat: rock banks traced from target images onto a world grid, flat mirror pools, a moon-glitter column of micro-facet glints, and a moon disc with maria. Use for water or ice-crystal skills, water orbs or bubbles, water whips, jets or tentacles, splash impacts, tide pools, rain, crystal growth or eruption, crystal resonance, shattering glass or quartz, caustics, wet-stone night scenes, moon reflections on water, "water and crystal", "hydro", "clear water VFX", "crystal shatter" or "skill effects only". Includes a reusable module and a five-beat demo on a scanned CC0 low-tide floor with failure-mode switches.

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。

Upload one Kleros-ecosystem file per paid request to IPFS through the Kleros x402 gateway for $0.01 USDC on Base mainnet. Use for dispute evidence, MetaEvidence JSON, court/dispute/arbitrator policies, Curate item metadata, juror justifications, or any artifact a Kleros contract or subgraph will reference by CID. Trigger when the request mentions Kleros, court, arbitrator, dispute, juror, Curate, Proof of Humanity, evidence, meta-evidence, justification, explicitly names this gateway or skill, or asks to test or validate it. Do NOT use for generic IPFS/CID requests without Kleros context; recommend a general-purpose pinning service. Each request accepts exactly one file: different bytes require separate paid uploads, while identical bytes should reuse one CID.

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。

Smart money tracking — netflow, trades, holdings, perp trades. Use when finding what smart money wallets are buying/selling or tracking whale activity.

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。

The default layout for every OpenBot configuration screen — PageShell and its prose/wide widths, PageSection and PageRows, Item row composition, the settings-row pattern where a summary and a chevron open a dialog, and the size and variant vocabulary. This is what a new screen looks like unless an instruction says otherwise. Use when adding or changing a screen under app/src/routes, adding a row to an admin or settings page, choosing a Button or Item size, picking between a bordered and a filled row, laying out a dialog, or reviewing a diff that adds max-w-*, a hand-drawn card, or a new spacing scale under app/src. Don't use for where the data comes from (that is openbot-data-access), for the gallery components under components/gallery that a Bot draws, for the chat and channel surfaces, or for editing the primitives under components/ui themselves.

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。

Use when building, editing, validating, testing, or debugging an n8n workflow through the n8n-mcp MCP server — designing a flow, configuring a node, writing an expression or Code node, wiring credentials, or fixing one that misbehaves. The entry-point skill for the n8n-mcp-skills pack: it routes you to the right specialist skill, gives working knowledge of every n8n-mcp tool from turn one, and states the rules that keep workflows from breaking in production. Always consult it first on any n8n, workflow, node, or automation task — even a quick one-off, and even when the user names no skill — because n8n's surface drifts between versions and the specialist skills prevent silent failures.

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。

Core component library and design system patterns. Use when building UI, using design tokens, or working with the component library.

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。

Extract and consolidate reusable components, design tokens, and patterns into your design system. Identifies opportunities for systematic reuse and enriches your component library. Use when the user asks to create components, refactor repeated UI patterns, build a design system, or extract tokens.

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。

Add, review, or fix JavaScript interop in Blazor components. USE FOR: calling JavaScript from Blazor, calling .NET from JavaScript, collocated .razor.js modules, IJSRuntime, IJSObjectReference lifecycle, DotNetObjectReference, ElementReference, timing rules for when JS is available, IAsyncDisposable disposal of JS references, server-side JS interop safety. DO NOT USE FOR: general Blazor component authoring without JS interop needs (use author-component), forms (use collect-user-input).

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。

Symbolicate .NET runtime frames in Apple platform .ips crash logs (iOS, tvOS, Mac Catalyst, macOS). Extracts UUIDs and addresses from the native backtrace, locates dSYM debug symbols, and runs atos to produce function names with source file and line numbers. Automatically downloads .dwarf symbols from the Microsoft symbol server using Mach-O UUIDs. USE FOR triaging a .NET MAUI or Mono app crash from an .ips file on any Apple platform, resolving native backtrace frames in libcoreclr or libmonosgen-2.0 to .NET runtime source code, retrieving .ips crash logs from a connected iOS device or iPhone, or investigating EXC_CRASH, EXC_BAD_ACCESS, SIGABRT, or SIGSEGV originating from the .NET runtime. DO NOT USE FOR pure Swift/Objective-C crashes with no .NET components, or Android tombstone files. INVOKES Symbolicate-Crash.ps1 script, atos, dwarfdump, idevicecrashreport.

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。

Complete bug bounty workflow — recon (subdomain enumeration, asset discovery, fingerprinting, HackerOne scope, source code audit), pre-hunt learning (disclosed reports, tech stack research, mind maps, threat modeling), vulnerability hunting (IDOR, SSRF, XSS, auth bypass, CSRF, race conditions, SQLi, XXE, file upload, business logic, GraphQL, HTTP smuggling, cache poisoning, OAuth, timing side-channels, OIDC, SSTI, subdomain takeover, cloud misconfig, ATO chains, agentic AI), LLM/AI security testing (chatbot IDOR, prompt injection, indirect injection, ASCII smuggling, exfil channels, RCE via code tools, system prompt extraction, ASI01-ASI10), A-to-B bug chaining (IDOR→auth bypass, SSRF→cloud metadata, XSS→ATO, open redirect→OAuth theft, S3→bundle→secret→OAuth), bypass tables (SSRF IP bypass, open redirect bypass, file upload bypass), language-specific grep (JS prototype pollution, Python pickle, PHP type juggling, Go template.HTML, Ruby YAML.load, Rust unwrap), and reporting (7-Question Gate, 4 validation gates, human-tone writing, templates by vuln class, CVSS 3.1, PoC generation, always-rejected list, conditional chain table, submission checklist). Use for ANY bug bounty task — starting a new target, doing recon, hunting specific vulns, auditing source code, testing AI features, validating findings, or writing reports. 中文触发词：漏洞赏金、安全测试、渗透测试、漏洞挖掘、信息收集、子域名枚举、XSS测试、SQL注入、SSRF、安全审计、漏洞报告

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。

Hunt JWT cryptographic failures — alg:none signature-stripping and RS256→HS256 key-confusion that let an attacker forge a token for any identity (e.g. an admin) without knowing a secret. Use when the app authenticates with a JSON Web Token (an `eyJ...` Bearer token in the Authorization header, a cookie, or a login response). This skill OWNS JWT signature/crypto forgery (alg:none, key confusion, kid/jku header injection); hunt-ato covers JWT as one ATO path, hunt-auth-bypass covers SSO/SAML token trust, hunt-api-misconfig covers non-crypto JWT handling. Critical when a forged token grants access to another user's data or an admin-only endpoint.

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。

Hunt vector-store / embedding-layer weaknesses in RAG pipelines (OWASP LLM08 Vector and Embedding Weaknesses) — persistent corpus poisoning that survives across sessions and users (distinct from one-shot indirect prompt injection, which is owned by hunt-llm-ai), cross-tenant vector-database IDOR (unauthenticated or unscoped queries against Pinecone/Weaviate/Chroma/Milvus/Qdrant/pgvector), source-text/metadata leakage in similarity-search results, and retrieval-hijack via adversarial embedding proximity ('SEO poisoning' for RAG). Targets: any app with a shared knowledge base, document upload feeding a chatbot, or a directly reachable vector-DB port. Validate: a second, clean session/account must inherit a poisoned result, or a cross-tenant artifact must be independently verifiable — confabulation is not a finding, same bar as hunt-llm-ai. Use when target is RAG-backed, exposes a vector-DB port, or lets users upload documents that other users' queries later retrieve.

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。

Hunting skill for auth bypass vulnerabilities. Built from 12 public bug bounty reports across SAML XSW / parser-differential (GitHub Enterprise CVE-2025-25291/25292), SAML signature stripping (Uber, Rocket.Chat, samlify CVE-2025-47949), SAML domain enforcement bypass via control characters (HackerOne 2024), partner-portal cross-IdP assertion reuse (Slack), WordPress XMLRPC bypassing SSO (Uber), JWT alg-confusion HS256/RS256 (Jitsi), JWT signature-validation skip (Linktree, Newspack), and token-audience confusion (Argo CD CVE-2023-22482). For standalone JWT signature/crypto forging (alg:none, key confusion, kid/jku) see hunt-jwt-crypto; this skill covers JWT only inside SSO/SAML/token-trust bypass chains. SAML assertion-layer attacks (XSW, comment injection, signature stripping, XXE-in-assertion) are owned by hunt-saml; this skill owns the broader cross-protocol auth-bypass taxonomy. Use when hunting auth bypass — see the Legacy-Protocol Matrix for branded-UI vs legacy-endpoint 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。

Inverse-fold a protein backbone (PDB structure) into amino-acid sequence with ProteinMPNN (Dauparas et al. 2022, github.com/dauparas/ProteinMPNN). Reach for this skill to run sequence design on RFdiffusion backbones, to redesign one chain of a PDB while holding interface residues fixed, or to generate a temperature-swept set of sequences for downstream folding.

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。

Classical end-to-end empirical analysis workflow in the modern tidyverse + econometrics R ecosystem — dplyr + tidyr + haven + fixest + sandwich + lmtest + clubSandwich + AER + ivreg + did + bacondecomp + HonestDiD + eventstudyr + rdrobust + rddensity + Synth + gsynth + synthdid + MatchIt + WeightIt + cobalt + ebal + grf + DoubleML + mediation + marginaleffects + modelsummary + kableExtra + gt + ggplot2 + ggpubr + cowplot + binsreg. **Defaults to economics empirical-paper style** (AER / QJE / AEJ) — every run produces a publication-ready output set with a multi-column regression table (M1→M6 progressive controls/FE) as the centerpiece, plus Table 1 (descriptives), mechanism / heterogeneity / robustness tables, and event-study + coefficient + trend figures. Covers the full 8-step R pipeline an applied economist runs on every paper — (1) data import & cleaning (read_dta/read_csv, naniar, janitor, validate-merges), (2) variable construction (mutate/across/winsorize/group_by + lag/lead with dplyr), (3) descriptive statistics & Table 1 (gtsummary, modelsummary::datasummary, tableone), (4) classical diagnostic tests (shapiro/jarque.bera.test/bptest/dwtest/bgtest/vif/adf.test/kpss.test/Hausman), (5) baseline modeling (fixest::feols, ivreg, did::att_gt, eventstudyr, sun_ab, did_imputation, synthdid, rdrobust, MatchIt, WeightIt, grf::causal_forest, DoubleML, mediation), (6) robustness battery (modelsummary stack, clubSandwich CRSE, fwildclusterboot, ri2, robomit Oster, bacondecomp, HonestDiD), (7) further analysis (interactions + marginaleffects, mediation::mediate, gsem via lavaan, dose-response splines, grf CATE), (8) publication-ready tables & figures (modelsummary, kableExtra, gt, stargazer, texreg, flextable to LaTeX/Word/HTML; ggplot2 + ggpubr + cowplot + binsreg + iplot for figures). **Also covers two parallel domain modes that share the same 8-step scaffolding** — **Mode A — Epidemiology / public health** (target-trial emulation, IPTW + g-formula + TMLE doubly-robust triplet via `WeightIt` / `gfoRmula` / `tmle` / `ltmle`, Mendelian randomization via `MendelianRandomization` / `TwoSampleMR` / `MRPRESSO`, KM / Cox / AFT / RMST survival via `survival` / `survminer` / `flexsurv`, E-value sensitivity via `EValue`, principal stratification — STROBE / TRIPOD reporting), and **Mode B — ML causal inference** (DML via `DoubleML`, S/T/X/R/DR meta-learners via `causalweight` / `grf`, causal forest via `grf::causal_forest`, BART/BCF via `bartCause` / `bcf`, matrix completion via `MCPanel`, CATE distribution + policy tree via `policytree`, off-policy evaluation, conformal causal via `conformalInference` / `cfcausal`, fairness audit via `fairmodels`, DAG learning via `pcalg` / `bnlearn` / LLM-assisted). Use when the user asks for a complete R empirical analysis, wants a tidyverse-style reproducible R script / Quarto workflow, prefers fixest over reghdfe, needs the R counterpart to StatsPAI / 00.1 / 00.2, or names a specific R step in isolation ("feols with cluster", "MatchIt nearest neighbor", "bacondecomp in R", "gtsummary table 1", "modelsummary to Word"). Mode A triggers on "target trial emulation R", "tmle ltmle", "MendelianRandomization", "TwoSampleMR", "MRPRESSO", "survival cox AFT", "STROBE R", "EValue R", "公共健康 R", "流行病学 R". Mode B triggers on "DoubleML R", "grf causal forest", "policytree", "bartCause bcf", "conformal causal R", "fairmodels", "pcalg NOTEARS", "因果机器学习 R".

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。

Records an architecture decision as a Nygard/MADR-shaped ADR under decisions/ — capturing the context that forced the choice, the options weighed, the decision itself, and its consequences in both directions, plus the supersedes chain that keeps a decision log honest. Read when asked to record an architecture decision, write an ADR, log the decision we made, document why we chose X over Y, capture this decision for the record, or supersede an old decision with a new one. Do NOT read to frame a proposal or explore an idea not yet decided (frame-a-proposal), to write a spec or implementation plan (write-a-spec), to write an incident postmortem (write-a-postmortem), or to judge whether a design is sound (review-a-design). This skill records a decision already made; it does not make one.

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。

Get detailed information about a specific Component on a GameObject — type, enabled state, and (optionally) serialized fields and properties. Supports token-saving path-scoped reads via `paths` or `viewQuery`. Use 'gameobject-find' to list components first.

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。

Turn one approved master still into a character's whole animated sprite set - one image-to-video clip per action (idle, walk, run, attack, jump, hurt, cast and so on), all from that still, generated through generate2dmedia route_media.py (the xAI API when a key is configured, else the local Grok CLI) - then gate each take, key it with a soft matte, register it to the master, pick loops or retime actions, finish (HD by default, pixel on request) and package fixed-canvas frames, alpha WebM, packed MP4 for iPhone and a PNG fallback with engine metadata and a JS runtime. Also processes a supplied clip. Use for character and creature animation, fluid motion and any video-to-sprite work. Not for FX, icon or prop sheets (generate2dsprite), maps or plate loops (generate2dmap), code-drawn animation (codeart2d, only on request) or calling a video API by itself (generate2dmedia).

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。

Inspect and profile React Native component trees from agent-device. Use for React Native performance, profiling, props, state, hooks, render causes, slow components, excessive rerenders, or questions like why a component rerendered.

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。

Analyze and optimize React component performance issues (slow renders, re-render thrash, laggy lists, expensive computations). Use when asked to profile or improve a React component, reduce re-renders, or speed up UI updates in React apps.

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。