「surface」の検索結果

A luxury-automotive brand whose marketing surfaces read as cinematic editorial. The base canvas is **near-black** (`#181818`) holding pure white display type; white-canvas bands appear only inside specific editorial contexts (preowned listings, pricing tables). The single brand voltage is **Rosso Corsa** (`#da291c`) — the iconic Ferrari racing red — used scarcely on primary CTAs, the Cavallino mark, and Formula 1 race-position highlights. Type runs **FerrariSans** at modest weights (display 500, body 400) — never bombastic. Spacing follows an explicit 8px token ladder (`xxxs` 4px through `super` 128px); generous editorial pacing throughout. The brand's strongest visual signature is the **full-bleed cinematic hero photograph** that fills the viewport top with car photography, model details, or trackside livery — followed by a tighter editorial body layout below.

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。

Read incoming retalk messages from this session's user's DESIGNATED sender(s) — one-shot, or as a background --follow reader that surfaces new messages in the session as they arrive (on your next turn). agent-talk only ever receives from specific saved peers, never the whole mailbox (safety). `<user>` is this session's user directory (absolute path; from init). Always renders the conversation (sent + received) as a beautiful chat transcript so the user can track it. Use to check mail or stay reachable.

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。

Cross-agent self-inspection of your AI-agent stack. Audits skills, MCP servers, hooks, plugins, commands, credentials, and memory files across Claude Code, Codex, OpenClaw, and NanoClaw. Produces a structured inventory and narrative briefing with cross-ecosystem risk analysis. Use when the user asks to audit their own setup, check what they have installed, review their agent stack security posture, or understand cross-tool interactions. Use when a user has accumulated skills/plugins/MCP servers over time and wants visibility into their attack surface. Use after installing new skills or plugins. Do NOT use for vetting external code before install (that is repo-forensics). Do NOT use for incident response during active attacks. Do NOT use for fixing or patching vulnerabilities (forensify is read-only).

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 the changelog as a distribution surface bridging engineering releases (DEP-/FEA-) to marketing channels (GTM-) during PRD v0.8 Deployment & Ops. Triggers on requests to set up a changelog system, publish releases, or when user asks "how should we publish releases?", "changelog as marketing", "release notes", "Stripe-style changelog", "what to do with our release notes", "ship and tell". Outputs MON-CHG-* changelog entries and per-channel formats.

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。

Surface risks through guided questioning, helping users consider pivots, constraints, and prioritization during PRD v0.5 Red Team Review. Triggers on requests to identify risks, stress-test the idea, perform red team review, or when user asks "what could go wrong?", "identify risks", "red team", "risk assessment", "challenge assumptions", "stress test the idea". Consumes all prior IDs (CFD-, BR-, FEA-, PER-, UJ-, SCR-) as interview context. Outputs RISK- entries with owner decisions and mitigations. Feeds v0.5 Technical Stack 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。

Hoyeon browser automation workflow for Codex. Use when the user invokes "$hoyeon-browser-work" or asks Codex to perform a browser task through recon-first chromux automation. This adapter loads the canonical browser-work skill and follows its Codex runtime surface.

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。

Hoyeon blueprint workflow for Codex. Use when the user invokes "$hoyeon-blueprint" or wants to turn requirements.md into a validated plan.json and optional contracts.md. This adapter loads the canonical blueprint skill and follows its Codex runtime surface.

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。

Hoyeon deep research workflow for Codex. Use when the user invokes "$hoyeon-deep-research" or wants a multi-channel cited research report using web search, browser extraction, and optional Gemini. This adapter loads the canonical deep-research skill and follows its Codex runtime surface.

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。

Hoyeon Google/chromux search workflow for Codex. Use when the user invokes "$hoyeon-google-search" or needs real-browser Google search, site-specific search, time filters, or body/comment enrichment. This adapter loads the canonical google-search skill and follows its Codex runtime surface.

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。

Hoyeon implementation reference discovery workflow for Codex. Use when the user invokes "$hoyeon-reference-seek" or asks for internal patterns, open-source references, GitHub examples, official docs, or comparable implementations. This adapter loads the canonical reference-seek skill and follows its Codex runtime surface.

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。

One-shot perspective reset that surfaces blind spots mid-work. Scans what the user has been doing, generates one abstract reframing question, and runs 3 quick checks (scope drift, side effects, better approach) in under 10 lines. No dialogue, no code. Trigger phrases: "/stepback", "step back", "한발 물러서", "넓은 관점", "놓치는 거 없나", "방향 맞나", "zoom out", "큰 그림", "방향이 맞는 거야", "잠깐 멈춰", "지금 뭘 하고 있는 거야", "blind spot", "재확인"

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。

Hoyeon developer community scan workflow for Codex. Use when the user invokes "$hoyeon-dev-scan" or asks for developer reactions, community opinions, or cross-community sentiment on technical topics. This adapter loads the canonical dev-scan skill and follows its Codex runtime surface.

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。

Hoyeon execution workflow for Codex. Use when the user invokes "$hoyeon-execute" or wants to execute a Hoyeon plan.json through the Bash-first Codex adapter. This adapter loads the canonical execute skill and follows its Codex runtime surface.

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。

Hoyeon requirements workflow for Codex. Use when the user invokes "$hoyeon-specify" or wants to turn an unclear goal into structured requirements.md for Hoyeon. This adapter loads the canonical specify skill and follows its Codex runtime surface.

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。

Censys search engine for internet-connected hosts, TLS certificates, and web properties. Use when: certificate transparency monitoring, finding hosts by certificate fingerprint, alternative to Shodan for TLS/SSL analysis, discovering hosts running specific services, or tracking infrastructure changes via cert issuance.

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 the vault for structural issues: unresolved wikilinks, orphan notes, dead-end notes, and Obsidian sync conflicts. Optional --fix flags create stubs or surface fixable issues.

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。

Find keywords competitors rank for that you don't, via the DataForSEO Labs domain_intersection endpoint. Surfaces the highest-leverage content opportunities sorted by search volume and the competitor's ranking position, with cross-competitor consensus signals.

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。

Track multiple domains and keywords across runs using the DataForSEO SERP API. Saves a local watchlist of domains + their target keywords, then re-runs live rank checks on demand and surfaces any position changes (gains, losses, drops) since the last check.

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。

Technical SEO site audit via the DataForSEO On-Page API. Crawls the site (up to 100 pages by default), surfaces critical errors (broken links, redirect chains, missing meta, slow pages, schema gaps, indexability issues), and returns a Technical Score (0-100) with prioritized fixes.

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。

Generate and maintain llms.txt files for AI discoverability. Scans repos to create curated content maps that help AI answer engines surface your project accurately. Implements the llms.txt specification from Answer.AI.

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。

MEMO-ONLY prior-work overlap advisory: surfaces the two ADVISORY taxonomy signals neither a tool nor a model can decide from the paper alone — ADV-TRIVIAL-COMBINATION (standard A+B+C / 缝合 stapling) and ADV-DUPLICATE-PUBLICATION (repackaged / duplicate submission). The executor RETRIEVES candidate prior work (DBLP fuzzy-title + boolean · WebSearch · WebFetch) from the paper's own title + contribution spans in the evidence ledger; TWO fresh cross-model codex reviewers (one per axis) LAY OUT the overlap side-by-side against each anchored contribution claim. It NEVER rules 'trivial' or 'duplicate' (that is a human judgment) and absence of a match is NOT evidence of originality. Emits novelty-duplication-advisory.memo.md + an info-only findings mirror; carries NO verdict weight — tools/adjudicate_findings.py lists it in ZERO_WEIGHT_SKILLS and caps it at info. Detect-only. Adapted from ARIS novelty-check, reframed from 'is MY idea novel' to 'here is the overlap a reviewer should weigh'. Triggers: "novelty advisory", "duplication check", "prior-work overlap", "is this stapling", "缝合", "查重", "重复发表", "duplicate submission".

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 experiment integrity against the evidence ledger. At L2 (repo + result files present) a fresh cross-model reviewer reads the eval code line-by-line for fake/derived ground truth, score self-normalization, phantom results (a paper number with no backing file/key), dead/uncalled metric code, verified-scope inflation, method-described ≠ method-evaluated drift, synthesized-looking results, placeholder/fake data still wired into a released result, code-output ≠ reported-number mismatch, and missing reproducibility artifacts (an empirical/agent/LLM paper shipping neither code nor the prompts/configs its results need) — every finding span-anchored to a ledger claim_id. At L0/L1 (PDF / source only) the same patterns are surfaced as info-level 'could-not-verify' signals where the ledger gives an anchor (observability_level_required:2) — NEVER a fraud verdict from a PDF. The reviewer PROPOSES findings; tools/adjudicate_findings.py computes the verdict. Detect-only. Triggers: "experiment forensics", "audit the results", "check the eval code", "实验诚实度".

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。

Family-G proof & derivation integrity forensics: does a THIRD PARTY's written proof/derivation actually establish its theorem, or does it skip an obligation, assume its own conclusion, take an invalid step, drift a symbol's meaning, or smuggle an unstated assumption? Decides from the WRITTEN proof/derivation — verdict-bearing at L1 (the LaTeX source; PDF-extracted math is unreliable, so an L0 PDF-only run surfaces info only) — never asserts 'fabricated', only that the step shown does not hold. A fresh cross-model reviewer reads the theorem/proof + an extraction-only obligation scaffold and proposes per-obligation findings, each span-anchored to the evidence ledger (claims.json); reviewer≠adjudicator. Emits proof-derivation-forensics.findings.json; NEVER computes the verdict. dimension=proof, can be critical. Triggers: "proof forensics", "check this proof", "derivation integrity", "audit the math", "证明审计", "推导有没有漏洞".

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。

Semantic Python code queries via a stdio LSP client driving pyright-langserver. Provides binding-resolved go-to-definition, find-references, hover types, type diagnostics, file symbol outlines, and project-wide symbol search — name resolution and type inference that tree-sitter and ripgrep cannot do. Use when you need to follow an import to a definition, find all real uses of a symbol (excluding same-named-but-unrelated ones), get an inferred type, surface type errors, outline a file, or search symbols across a project. Triggers on "go to definition", "find references", "what type is", "resolve this symbol", "symbol outline", "find symbol in project", "pyright", "type-check this file".

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。