「hf」の検索結果

Use when adding character/enemy/game-flow states or NPC behaviour - explicit transition tables instead of flag soup, hierarchical and pushdown machines, behaviour trees per engine, navmesh pathfinding, AI tick budgets, and table-driven tests.

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TileMap BFS寻路实现、六边形网格寻路、路径追溯与流式寻路功能。用于需要网格寻路、动态障碍物避让、游戏AI导航等场景。

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。

Generates 9 consistent multi-angle fashion-editorial portraits from a single reference image, with locked identity (face/skin/eyes), preserved hairstyle structure, faithful outfit/accessories, and a unified photography style across all frames. Use when the user says "multi-angle", "multi-angle shots", "九连拍", "多角度九连拍", "9-angle portraits", "fashion lookbook", "model consistency series", "consistent portraits from one photo", "generate 9 angles of this model", or "e-commerce model multi-angle pack". Part of the product-shots ecosystem for cross-border e-commerce apparel and accessory listings.

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。

Generating pixel-accurate HTML/CSS code from image mockups (PNG/JPG/screenshots) and performing visual verification for faithful reproduction. Use when mockup-to-code generation is needed.

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。

Audit an implementation against its plan with a hostile peer agent, debate findings to consensus, and drive fix waves until the auditor runs dry. Use when a done-claim needs verification the implementer cannot game ("hostile review", "adversarial audit", "is this PR faithful", "find the escape hatches") — the loop that beats the laziness bias with its own game theory.

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。

Show the truthful state of workflow contracts from git, gh, worktrees, and transient launch claims; offer archive, resume, reconciliation, or wf-run 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。

Flagship orchestrator that launches 5 parallel agents (cashflow, debt, investments, retirement, protection) to produce a comprehensive financial health analysis with a composite Financial Health Score (0-100), letter grade (A+ through F), and a prioritized 90-day action plan. Use when the user says "/finance analyze", "full financial analysis", "financial health check", "analyze my finances", or any request for a complete financial picture.

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。

Kundenforschung datensparsam und einwilligungsorientiert planen, durchführen und auswerten. Nutze diesen Skill für Interviewleitfäden, Umfragen, Usability-Tests, die Auswertung vorhandener Gespräche, Support-Tickets oder Bewertungen, und für Personas, Segmente und Jobs-to-be-Done. Klärt vorab Zweck, Rechtsgrundlage, Einwilligung, Aufzeichnung, Pseudonymisierung und Aufbewahrung, wertet nur innerhalb des Stichprobenrahmens aus und trennt Befund von Entscheidung. Beachtet DSGVO, BDSG und Beschäftigtendaten; Österreich und Schweiz werden getrennt behandelt.

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Ein evidenzbasiertes, tragfähiges Content-Portfolio für Zielgruppen, Geschäftsziele, Distribution und die real vorhandene Produktionskapazität planen. Nutze diesen Skill für Content-Strategien, Themenfelder, Redaktionspläne und Backlogs, für die Diagnose eines bestehenden Contentbestands, für die Abbildung von Kauf- und Nutzungswegen und für Entscheidungen zu CMS und Governance. Priorisiert offen nach Nachfrage, Aufwand und Evidenz, schützt vor dünnen Massenseiten und benennt Belegklassen. Einzelne Texte schreibt copywriting, die technische Prüfung macht seo-audit.

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。

B2B-Outreach-E-Mails im deutschen und DACH-Kontext konservativ entwerfen und prüfen. Nutze diesen Skill für Kaltakquise-Mails, Nachfassnachrichten, Betreffzeilen und Outreach-Sequenzen an Geschäftskontakte, und für die vorgelagerte Frage, ob eine geplante Ansprache überhaupt zulässig ist. Prüft § 7 UWG, die kumulativen Voraussetzungen der Bestandskundenausnahme nach § 7 Abs. 3 UWG, Herkunft der Kontaktdaten und Sperrlisten; Österreich und Schweiz werden getrennt behandelt. Liefert ausschließlich Entwürfe mit sichtbarem Versandblocker: kein Versand, keine Terminierung, kein Kontaktimport. Keine Rechtsberatung.

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 the primary outcome is authoring, rewriting, summarizing, or validating user-facing documentation without implementation comparison or required source attribution, including READMEs, tutorials, API docs, GitLab Markdown, source-faithful summaries, and audience-specific prose.

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。

This skill should be used when the user asks to "lint bash script", "run shellcheck", "format shell script", "use shfmt", "fix shellcheck errors", or mentions shell script linting, formatting, code quality, or pre-commit hooks for bash.

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 verifying information (fact, number, quote, event, statement) against authoritative primary sources, or cross-checking a number via one-level metric decomposition (Z = P × Q). Triggers: "verify X", "is this true", "find the original source", "where is this number from", "two sources disagree", "is it true X never did Y". Covers five scenarios: (1) basic truthfulness check, (2) completeness / out-of-context quoting, (3) one-level reasoning verification, (4) negative-statement handling, (5) multi-source conflict side-by-side output. Dig into whitelisted primary sources only (user-supplied files, official websites & databases, authoritative industry sources); cited reports / charts / datasets must be downloaded and read locally to count as verified — if download is blocked, hand the link to the user. If nothing can be found, plainly state "cannot verify" rather than guessing, patching, or citing secondary paraphrases. Always reply in the user's question language.

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。

Review user-facing docs, CLI reference text, examples, and scaffolds for truthfulness, prose quality, and RFC leakage. Use when a broad review needs a dedicated docs/claims pass or when the user asks for docs-truth review specifically.

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 the user wants to run a diffusers pipeline from a terminal (one-off generation, batch jobs, smoke-testing a new model), run on HF Sandbox hardware via `--remote`, introspect a pipeline's input schema before calling it, or attach a LoRA at inference time. Prefer this over writing ad-hoc Python scripts for generation tasks.

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。

Blender MCP expert for scene inspection, Python scripting, GLTF export, and material/animation extraction. Activate when: (1) using Blender MCP tools (get_scene_info, execute_python, screenshot, etc.), (2) writing Blender Python scripts for extraction or manipulation, (3) exporting scenes to GLTF/GLB for web (Three.js, R3F), (4) debugging material or texture export losses, (5) optimizing GLB files with gltf-transform, (6) using asset integrations (PolyHaven, Sketchfab, Hyper3D Rodin, Hunyuan3D). Covers critical export gotchas, material mapping survival, texture optimization pipeline, headless CLI patterns, and known 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。

Audit whether the theories/methodologies a project claims to implement are faithfully operationalized — or name-dropped, partially built, distorted, or over-claimed. Source-grounds the load-bearing theories; tags the rest provisional. Run periodically alongside /framework-health.

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 imp outputs differ between two paths that should agree - chunk sizes, prefill vs decode, prefix-cache resend, two engines (imp vs llama.cpp vs HF), two builds or kernels - "output changes with chunk size", "first token differs", "logprobs moved", "which layer diverges", "who is right, imp or llama.cpp", "near-tie". Do NOT use for repetition loops or garbage output (check-degeneration), a new arch that loads wrong (add-model-arch), or perf (benchmark-cuda).

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。

Verify that implementation code faithfully implements its specification — checks function contracts, API contracts, and protocol compliance against source specifications

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。

Produce a truthful Japanese App Store metadata pack and app-name shortlist in twelve minutes without a multi-agent research round. Use for a live launch sprint, fallback ASO copy, app naming under a hard deadline, or when local product evidence is available but market research is intentionally deferred.

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。

Epistemic transparency — acknowledging uncertainty, flagging limitations, avoiding overconfidence, and communicating what is known, unknown, and uncertain with proportional confidence. Maps the HEXACO personality dimension to AI reasoning: truthful calibration of confidence, proactive disclosure of gaps, and resistance to the temptation to appear more certain than warranted. Use before presenting a conclusion, when answering questions where knowledge is partial or inferred, after noticing a temptation to state uncertain information as certain, or when a user is making decisions based on provided information.

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Queries the NCATS Translator ARAX production API for bounded, typed, provenance-rich one-hop and endpoint-pinned two-hop biomedical knowledge-graph relationships. Use for Biolink-constrained RTX-KG2 lookup, explicit selected-provider ARAX federation, separate entity normalization, qualifier-aware graph traversal, and inspection of TRAPI edge bindings, publications, and knowledge-source provenance. Do not use for inference, ranking, open-ended pathfinding, clinical guidance, or sensitive queries.

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Molecular featurization hub with one consistent interface over 100+ featurizers. Fingerprints (ECFP/Morgan, MACCS, atom pair, topological torsion, Avalon, RDKit, ERG), RDKit and Mordred descriptor sets, pharmacophore and 3D shape descriptors, scaffold keys, and pretrained embeddings (ChemBERTa, ChemGPT, MolT5, GIN, Graphormer) through a common transformer API with caching and parallelism. Use this skill to convert SMILES into model-ready feature matrices for QSAR, virtual screening, and molecular ML, and to choose between featurizer families. Also trigger on molfeat, MoleculeTransformer, FPVecTransformer, PretrainedHFTransformer, molfeat model store, or featurizer selection.

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。

Create interview preparation from a job analysis, tailored resume, and master resume. Use when the user wants likely questions, truthful STAR answers, and ways to discuss gaps.

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。