「token」の検索結果

Use when querying or calling pay-per-token foundation models, or when creating, updating, listing, or querying Unity Gateway (also called Unity AI Gateway) services, including model provider services, MCP services, system.ai services, and three-part Unity Catalog service names (catalog.schema.service). Also use for managing spend budgets and usage alerts on Unity Gateway, and for migrating from legacy AI Gateway. Also use when a workload with Model Serving endpoint permissions (for example CAN_QUERY) gets PERMISSION_DENIED querying a three-part model name, since serving-endpoint access does not grant access to a Unity Gateway service. Not for configuring legacy AI Gateway on Model Serving endpoints; use databricks-model-serving for that.

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Build a complete typographic system -- modular or fluid type scale, line-height and tracking per size, weight roles, and vertical rhythm -- exported as tokens, Tailwind config, and CSS. Pairs with font-pairing-suggester (which picks the fonts; this builds the system they live in).

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Define a product's motion design language -- duration and easing scales, choreography rules, and signature moves -- and export it as tokens plus ready-to-use Framer Motion variants and CSS. The difference between animations and a motion system: everything moves like it belongs to the same product.

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Convert a light-only site or app to a real dark mode -- semantic token mapping, elevation-based surfaces, recalibrated shadows and imagery -- not a naive inversion. Handles Tailwind, CSS variables, and styled-components, plus the toggle, persistence, and no-flash boot.

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Use when a coding session is burning context fast, output is drowning in noise, the model keeps forgetting earlier decisions, or the user asks to reduce tokens, stop the verbose narration, work in a huge repo, or make a long session survive. Covers what to read, what to summarize, what to write to disk, and when to start fresh.

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Shapes turns action-first: action leads, steps numbered, state restated. Use for ADHD-friendly output. Do not use to trim tokens; use response-compression.

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Delegates tasks to Z.ai GLM-5.x via the stock claude binary and an endpoint swap. Use when delegation-core selects GLM or a 1M-token context is needed.

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Delegates tasks to Gemini CLI implementing delegation-core for Google's models. Use when delegation-core selects Gemini or 1M+ token context is needed.

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Delegates tasks to Meta's Muse Code CLI (muse) via delegation-core. Use when delegation-core selects Muse or repository-scale work needs a 1M-token context.

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Run and interpret repo diagnostic scripts (ratchets, validators, token stats). Use when measuring health. Do not use to run tests; use night-market-operations.

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Use deepjazz for legacy MIDI jazz generation with music21 preprocessing, abstract grammar tokens, Keras/Theano LSTM training, safe diagnostics, and modernization guidance.

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Use bm25s for sparse lexical retrieval: tokenize text, build and query BM25 indexes, persist or memory-map them, run the high-level or bm25 CLI workflows, and diagnose optional acceleration and integrations.

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CI/CD pipeline attack hunting (GitHub Actions focus) - pwn requests (pull_request_target), script injection, self-hosted runner takeover, cache poisoning, OIDC-to-cloud token theft, poisoned pipeline execution. Wiki-first, FIND schema output.

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Local Windows privilege escalation on a STANDALONE / workgroup host, or a local shell on a domain member - foothold to SYSTEM. Token privileges (SeImpersonate/Potato), service misconfig (weak perms / unquoted path / writable binary), registry autologon creds, scheduled-task + writable-script abuse, DLL hijack, AlwaysInstallElevated, UAC bypass, credential loot. For DOMAIN escalation (kerberoast/DCSync/ADCS/BloodHound) use hunt-ad instead. Wiki-first, FIND schema output.

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Report session token and CO2 savings vs no-Honey baseline.

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Show Honey's benchmark scoreboard: committed quality and token results per task tier vs Caveman, Ponytail, and no-skill. One-shot.

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Rewrite a memory or context file (CLAUDE.md, AGENTS.md, a todo or notes file) into Honey-terse form so it costs fewer input tokens every session, without losing meaning. Backs up the original first. Use when asked to shrink or compress context files, trim CLAUDE.md, or cut per-session input cost. Prose only — never code, config, or data.

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Rewrite context files terse to cut per-session tokens.

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Decide when to delegate to Honey's read-only subagents (hive-scout, hive-reviewer) instead of working inline, so the expensive tokens — large file reads and review passes injected back into the orchestrator's context — come back compressed (Honey Lever 3). Use when a task is search-heavy or review-heavy, spans many files, or you want to keep the orchestrator's context small. Not for trivial one-file work.

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Same pixels, fewer tokens. For user-facing markup: keeps the full design, expresses it densely with CSS vars, shorthand, fluid units.

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Write less code and say less about it. Cuts token cost.

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 Honey's benchmark scoreboard — the committed quality and token results per task tier (code, user-facing, agent-to-agent) from bench/. Reports only the reproducible committed figures, never invents per-repo numbers. Use when asked how much Honey saves, how it compares to Caveman / Ponytail / no-skill baseline, or for the headline numbers.

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Read huge dense read-only text as PNG pages via pxpipe export: ~60-75% fewer input tokens. Lossy on exact strings; never for files you will edit.

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。

Same pixels, fewer tokens: dense CSS for user-facing UI.

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。