「graphs」の検索結果

Builds citation networks from Semantic Scholar API and CrossRef DOI metadata. Visualizes paper influence graphs using NetworkX, identifies seminal works, and tracks research lineage across fields.

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。

Plan and implement native RayTK (t3kt/raytk) raymarching/SDF toolkit integration into tdmcp — beyond the existing package-manager staging, into AI-driven RayTK operator (ROP) node graphs. Use whenever the user wants to build, plan, design, implement, continue, fix, QA, or ship RayTK support: create/wire RayTK ROPs, a RayTK operator knowledge catalog, a RayTK-native raymarch/SDF scene tool, RayTK recipes, or the RayTK setup/doctor flow — including follow-ups (re-run, continue, refresh after a new RayTK release, re-QA, docs). Also triggers on 'raytk', 'raymarching toolkit', 'SDF toolkit', 't3kt', 'montar um build para o raytk', 'integrar o raytk'. This owns RayTK integration specifically; use it before the generic tdmcp-pipeline for this feature. Simple questions about RayTK can be answered directly.

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Reference patterns for graph algorithm problems including BFS, DFS, Dijkstra, topological sort, union-find, MST, Bellman-Ford, and bipartite checking. Provides recognition signals, Python templates, edge cases, and common mistakes for each technique. Use when solving problems involving graphs, trees, shortest paths, connectivity, or dependency ordering.

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Use when verifying that a model in the imp inference engine produces coherent output without repetition loops, token-stuck states, or state corruption across turns or streams. Triggers on "degenerates", "check degeneration", "repetition loop", "own own own", "stuck token", "empty content", "multi-turn regression", "does it still work", "NIAH", and after enabling CUDA graphs / changing forward pass / MoE routing / KV cache or KV dtype / GDN state or scan / sparse attention / PDL / speculation (MTP, n-gram) / ragged prefill / batched-decode kernels (smallm, producer quantize) / FA2 softmax.

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。

Transform Markdown or text specifications into human-readable HTML documents with summaries, reordered structure, visual explanations, diagrams, charts, and traceability to the source. Use when asked to convert a spec, requirements document, API spec, product spec, design doc, or technical document into readable HTML, especially when the user wants summarization, restructuring, diagrams, graphs, images, or visual aids rather than a literal Markdown-to-HTML conversion.

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。

Design intentional internal link graphs for SEO, UX, and LLM-readable sites. Use this skill when the user asks for links between short briefs, longform articles, topics, projects, author pages, breadcrumbs, source trails, related stories, orphan-page checks, overlink checks, anchor intent, or validation that a link graph reinforces the canonical content model without creating duplicate story bodies or hidden links.

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 turning perf, DTrace, pprof, or async-profiler stacks into an SVG flamegraph with the FlameGraph scripts, reading one, or diffing two profiles. Not for collecting data: use linux-perf.

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Use when collecting sampling profiles with perf record, reading perf report or perf annotate, measuring PMU counters with perf stat -e, or using PAPI instrumentation to interpret IPC, miss rates, MPKI, or memory bandwidth on Linux. Not for SVG rendering: use flamegraphs.

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 writing Bazel BUILD files with cc_library or cc_binary rules, Bzlmod dependencies, toolchain registration, remote execution, sandbox debugging, or bazel query and cquery graphs.

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Use these skills when you need to explore the database structure, discover schema objects like tables and graphs, and execute custom SQL queries to interact with your data.

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。

OpenTSC v2.1: Soul/Shell architecture for interpersonal intelligence. Local, offline-first, with event graphs, judgment engine (K7), self-creation (K8), three-layer attributes, calibrated predictions, 11 VSM professions, and a pluggable zvec memory index (semantic search, identity resolution, jieba segmentation, emotion scoring incl. batched+cached LLM) that runs with zero extra deps. Transforms implicit social judgment into explicit, queryable, auditable, evolving intelligence.

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 writing prose from a structured outline, building draft content section by section, or constructing clear sentences and paragraphs from an approved blueprint.

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 `graphify-dotnet` to generate codebase knowledge graphs, architecture snapshots, and exportable repository maps from .NET or polyglot source trees, with optional AI-enriched semantic relationships.

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。

Write engaging PR titles and descriptions for any forge (GitHub today; Bitbucket planned). Use when creating or updating PRs. Leads with narrative paragraphs and reaches for lists, tables, and diagrams when content is genuinely structured.

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。

Process multimedia files with FFmpeg (video/audio encoding, conversion, streaming, filtering, hardware acceleration) and ImageMagick (image manipulation, format conversion, batch processing, effects, composition). Use when converting media formats, encoding videos with specific codecs (H.264, H.265, VP9), resizing/cropping images, extracting audio from video, applying filters and effects, optimizing file sizes, creating streaming manifests (HLS/DASH), generating thumbnails, batch processing images, creating composite images, or implementing media processing pipelines. Supports 100+ formats, hardware acceleration (NVENC, QSV), and complex filtergraphs.

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Use when the user wants a complete SPSS or SPSS-MCP empirical research workflow: organize source data, prepare datasets, design variables and models, run SPSS analyses, export Chinese result paragraphs and tables, write a Chinese LaTeX paper, and compile the final PDF.

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。

"Calculate network centrality metrics to identify important nodes in graphs. Use this skill when the user needs to find key influencers, critical infrastructure nodes, or central actors in a network — even if they say 'who is most important in this network', 'key nodes', or 'network influence measurement'.".

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 story dependencies, detect issues, and generate visual dependency graphs

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Replace sentence-by-sentence revised or paraphrased text back into an original paper or document while preserving the original formatting, typography, paragraph settings, citation layout, and document conventions as the source of truth. Use when the user provides revised text that maps back to original sentences, paragraphs, sections, or the whole document, but may also contain clear local sentence merges or splits, and asks to replace it back into the source, keep the original formatting, do sentence-by-sentence replacement, preserve original formatting, or only swap wording without rewriting the document. Only allow minimal edits to the rewritten text when fixing citation formatting, restoring protected original content, restoring the original person reference, or minimally splitting/merging text back into original sentence slots; otherwise do not improve grammar, style, logic, or wording.

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。

Write engaging PR titles and descriptions for any forge (GitHub today; Bitbucket planned). Use when creating or updating PRs. Leads with narrative paragraphs and reaches for lists, tables, and diagrams when content is genuinely structured.

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。

Stress-tests a fuzzy or vague feature idea before any PRD, spec, or ticket is written. TRIGGER when: the user has a fuzzy feature idea — one or two paragraphs, vague on users / scope / success — and wants to harden it, or says "grill / stress-test / pressure-test this idea." DO NOT TRIGGER when: the idea is already turned into a spec or PRD; bug fixes or refactors; the idea is hardened and you are ready to write the PRD → creating-prd.

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Performs static analysis of binary code using radare2 and Ghidra — function enumeration, cross-reference tracing, decompilation, and control flow graphs without executing the binary. Use when disassembling or decompiling code, mapping functions, tracing data flow, or building hypotheses before dynamic verification.

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Generate Mermaid architecture diagrams from your codebase. Produces C4 context and container diagrams, sequence diagrams for key flows, ER diagrams from database schemas, and module dependency graphs. Use when you need architecture diagrams, system diagrams, database ERD, sequence diagrams, dependency visualization, or C4 model diagrams.

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Transform reference photographs and visually dense images into sparse, recognizable, high-saturation flat-vector-style illustrations through semantic compression rather than literal tracing. Use when the user asks for photo-to-flat illustration, photo-to-vector-style redraw, 照片转扁平插画, 无渐变高饱和插画, iconification, visual simplification, composition-preserving abstraction, a consistent minimal illustration set, or revisions that remove gradients, glow, texture, blur, clutter, text, logos, numbers, or UI. Default to human-perceived semantic complexity 6 out of 10 or lower, fixed-HSB solid fills, crisp boundaries, and no gradients or light-halo effects.

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。