「via」の検索結果

Text-to-speech via 9Router /v1/audio/speech using OpenAI / ElevenLabs / Deepgram / Edge TTS / Google TTS / Hyperbolic / Inworld voices. Use when the user wants to convert text to speech, generate audio, voiceover, narrate, or read text aloud.

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 videos via 9Router /v1/videos/generations using xAI Grok Imagine (grok-imagine-video). Async job flow - submit, poll request_id until done, download MP4. Use when the user wants to create, generate, or render a video, text-to-video (txt2vid), or image-to-video.

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。

Health smoke tests + auto-fix for gbrain installs (and OpenClaw services when present). Run after machine/container restarts or whenever something seems broken. Tests critical services, auto-fixes bounded local issues, and reports worker topology without starting daemons. Extensible via user-defined test scripts in ~/.gbrain/smoke-tests.d/*.sh.

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。

Re-run or deepen the agent's identity interview. Drives the SHARED bootstrap answer bank (state/interview.json) via `gbrain bootstrap interview --set/--show/--confirm`, then re-renders the identity files with `gbrain bootstrap render --only <FILE> --force`. One answer store, two surfaces: first-boot bootstrap and this re-run skill write the same bank, so an updated answer flows into SOUL.md, USER.md, ACCESS_POLICY.md, and HEARTBEAT.md without hand-editing rendered files. Every answer is the user's own words — never invented.

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。

Self-evolving skill optimization via SkillOpt-paper-grounded text-space optimizer.

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。

Detect which gbrain engine is in use (PGLite vs Postgres), prefer Postgres for agent-harness installs, install/provision Postgres (Supabase discovery via SUPABASE_ACCESS_TOKEN, local Postgres, opt-in Docker), and move an existing PGLite brain to Postgres with its history (plan, ask, run). Detection is one engine-free command; the install ladder is one flag.

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。

Migrate a brain from gbrain-base (or any pack) to gbrain-base-v2's 14-canonical-type taxonomy via gbrain onboard --check + the unify-types Minion handler. Collapses 94 noisy types to 15 canonical with subtypes, alias rows, and link rows. Triggers when an agent notices pack_upgrade_available, type_proliferation, or asks "what is the canonical taxonomy / how do I clean up my page types".

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 a TYPED citation/reference graph over an ingested corpus — not just embeddings. Flat similarity retrieval cannot tell you that document A *overrules* B, *distinguishes* C, or *relies_on* D. This skill extracts every inter-document reference, classifies the edge TYPE with LLM judgment, and writes first-class typed edges via `gbrain link`, so `gbrain graph-query --type` can walk the argument ("everything this brief relies on, minus anything overruled since"). Every cite-heavy corpus is the same shape: law, academic papers, patents, regulatory filings, a book's bibliography.

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 and fix citation formatting across brain pages. Ensures every fact has an inline [Source: ...] citation matching the standard format. Extended in v0.25.1: scans for broken tweet/post references that lack actual URLs and resolves them via the host's X / Twitter API integration.

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。

Compress an agent's routing file (RESOLVER.md or AGENTS.md) by converting granular skill-per-row tables into functional-area dispatchers. Each area lists sub-skills in a "(dispatcher for: ...)" clause. The LLM reads one area entry and routes to the correct sub-skill. Proven via held-out A/B eval: dispatcher pattern outperforms naive pipe-table compression.

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。

Systematic claim-by-claim verification for any content before it ships. Modeled on professional fact-checking desks (The New Yorker, ProPublica, IFCN standards): extract every verifiable claim, check each against live citable sources (never training data), assign a 6-level confidence status, apply corrections, and produce a scored pass/fail report. Includes a data-derived-claims gate for outputs produced FROM the brain or a database: PRODUCER ≠ VERIFIER (re-derive each claim via a different query path) and AFFILIATION ≠ AUTHORSHIP (person→thing claims resolve through typed edges), with delivery hard-blocked on unsupported claims.

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。

Set up and operate the Gmail/Calendar/Contacts connector and the open-loop engine: who is waiting on the user, what they promised, and the context needed to respond. Covers painless BYO OAuth setup (exactly two user interactions), the daily `gbrain waiting` digest, loop closing/muting, and troubleshooting via the typed error catalog.

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 a publication-quality PDF from any brain page via the gstack make-pdf binary. Strips YAML frontmatter, sanitizes emoji, applies running headers and page numbers. Brain page is always the source of truth; PDF is a rendering.

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。

Connect a ChatGPT or Claude account and sync its conversation history into the brain automatically. Cookie paste-in is the primary lane; sync is incremental (watermark + trailing-window gap-heal), and can run on a schedule via autopilot or host cron. Distinct from the export-file lane (that lives in conversation-archive) — this is the LIVE, account-connected, auto-scraping path.

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。

Quality gate via second model. Spawn a different AI model to review work before committing. Includes refusal routing: if one model refuses, switch silently to the next. Extended in v0.25.1 with structured review-mode gating (when to invoke vs not) and a Codex code-review handoff for the diff-review case.

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。

Save any thought or content into the brain via one CLI command. The single human-facing entrypoint that replaces "put_page vs commit-then-sync vs autopilot-wait" with one command that just works.

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。

Structured data research: search sources, extract structured data, archive raw sources, maintain canonical tracker pages, deduplicate. Parameterized via YAML recipes for investor updates, donations, company updates, or any email-to-structured-data pipeline.

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。

Import AI-assistant chat exports (ChatGPT, Claude, Perplexity) and agent session transcripts into the brain as one dated page per conversation under conversations/, validate each page against the native conversation parser, extract facts via the native conversation-facts flow, and keep the archive gap-free with a detect-and-backfill loop. Then answer archive questions: "when did I first discuss X", trace how an idea evolved across past conversations, pull a specific thread.

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。

Take any book (EPUB/PDF), produce a personalized chapter-by-chapter analysis. Each chapter is preserved in detail (The Chapter) and mirrored back to the reader's actual life (The Mirror) using brain context. The mirror observes and resonates — a friend pointing out parallels, NOT a consultant rearranging the reader's life, NOT a therapist assigning homework. The reader decides what to do about it. Layout is a top-aligned HTML table or stacked sections, never a bare markdown pipe table (pipe tables center-misalign uneven columns). Output is a single brain page at media/books/<slug>-personalized.md plus an optional PDF via brain-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。

Use context-mode tools (ctx_execute, ctx_execute_file) instead of Bash/cat when processing large outputs. Triggers: "analyze logs", "summarize output", "process data", "parse JSON", "filter results", "extract errors", "check build output", "analyze dependencies", "process API response", "large file analysis", "page snapshot", "browser snapshot", "DOM structure", "inspect page", "accessibility tree", "Playwright snapshot", "run tests", "test output", "coverage report", "git log", "recent commits", "diff between branches", "list containers", "pod status", "disk usage", "fetch docs", "API reference", "index documentation", "call API", "check response", "query results", "find TODOs", "count lines", "codebase statistics", "security audit", "outdated packages", "dependency tree", "cloud resources", "CI/CD output". Also triggers on ANY MCP tool output that may exceed 20 lines. Subagent routing is handled automatically via PreToolUse hook.

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。

Browser automation via agent-browser CLI. Use when the user needs to navigate websites, fill forms, click buttons, take screenshots, extract data, or test web 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。

Drive a native GUI app (macOS, Windows, Linux) via the cua-driver CLI (default) or MCP server; snapshot its accessibility tree, act through snapshot-bound element tokens, native menu paths, exact window geometry, or pixel coordinates, and verify from fresh state. Use when the user asks you to operate, drive, automate, or perform a GUI task in a real application on the host, or to continue, resume, or recall recent Cua 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。

Zoom Virtual Agent iOS integration via WKWebView. Use for Swift/Objective-C script injection, message handlers, support_handoff relay, and URL routing policies.

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。

Zoom Virtual Agent Android integration via WebView. Use for Java/Kotlin bridge callbacks, native URL handling, support_handoff relay, and lifecycle-safe embedding.

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。