「market」の検索結果

Complete Pump protocol fee system — tiered protocol fees based on market cap, creator fee collection across two programs, basis point arithmetic, and ceiling division for dust-safe calculations.

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Constant-product AMM bonding curve math for Pump token pricing — buy/sell quoting, fee-aware calculations, market cap computation, tiered fees, ceiling division, virtual vs real reserves, and edge-case handling.

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Generate AI-powered BPO service proposals for Japanese companies in the US market. Use when creating outsourcing proposals with AI service menus, ROI estimation, implementation roadmaps, and bilingual (JA/EN) proposal documents for 在米日系企業向けAI実装.

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Register a Snowflake table or view as a Narrative dataset from SQL, with no Configuration screen and no browser. Not for installing or configuring the native app, and not for querying a dataset that already exists. Use when: "register this table as a dataset", "register a Snowflake dataset from SQL", "onboard my_table to Narrative", "script dataset registration", "add this view to my data plane". (narrative-common)

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Create a look-alike audience from a seed audience and a candidate population dataset. Classifies Rosetta Stone attributes, generates the same materialized-view scoring pipeline Lookalike Studio emits (Naive-Bayes categorical weights + Gaussian continuous similarity), gates on approval, submits via `narrative_workflows_create`, and monitors the build to completion. Use when: "create a lookalike audience", "find more users like this segment", "expand my seed audience to 500k similar users", "score the population against my customers", "build a look-alike of dataset X". (narrative-audience)

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Write, validate, and (optionally) execute an NQL query against a Narrative dataset. Drafts the query from the user's question, runs `narrative_nql_validate` until it compiles, explains the query in plain English, and only runs it on explicit approval (or when invoked with `--run`). Use when: "write an NQL query for X", "query this dataset", "validate this NQL", "run NQL against dataset <id>", "how many rows match Y", "show me the top N records from <dataset>". (narrative-common)

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Generate, evaluate, and improve Rosetta Stone attribute mappings for a Narrative dataset. Use when: "map this dataset to Rosetta Stone", "suggest normalized attributes for dataset N", "evaluate the mappings on dataset N", "why is this mapping low confidence", "fix this expression", "improve this NQL mapping expression". (narrative-common)

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Pick one open GitHub issue off this repository's queue, implement the fix, and open a pull request for it. One issue and one PR per run. Use when the user asks to burn down the issue backlog, or when the issue-burndown workflow invokes it. It never merges, never closes an issue, never edits .github/workflows/, and never acts on an issue from outside the organisation unless a maintainer has opted it in.

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Author and submit a Narrative workflow from a natural-language intent. Picks the closest example from `assets/examples/`, adapts it to the user's case, walks the YAML against the spec, resolves the data plane, and submits via `narrative_workflows_create` only after the user has approved the rendered spec. Use when: "create a workflow that does X", "schedule a daily refresh of dataset Y", "wrap this NQL as a workflow", "build a pipeline that creates view A then refreshes view B", "submit this workflow YAML", "productionize this query as a recurring job". (narrative-common)

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Translate a fuzzy analytical question into a rigorous investigation plan. Interrogates the ask, grounds the plan in the available data dictionary, applies analytical best practices, and produces a structured brief of query specifications for a downstream query-writing skill. Plans, does not write SQL. Use when: "why did X drop", "is there a relationship between A and B", "who are our highest-value customers", "what's driving the change in Y", "investigate this trend", "design an analysis for", "scope this analytical question". (narrative-common)

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Audit a dataset or access rule before it joins an identity-graph build (access rules behave like datasets in NQL here). Enumerates failure modes (hub identifiers, high-degree nodes, suspicious values, over-connected identifiers), tests hypotheses in parallel, quantifies damage by rows / edges / entities, and proposes minimal filters ranked by severity. When issues are found, returns a validated `CREATE MATERIALIZED VIEW` NQL the caller can run to produce a graph-ready clean source; if the data passes, says so and recommends it unchanged. Plans and authors the clean-view NQL; does not execute it. Use when: "audit this dataset before the graph build", "find bad edges in <source>", "check identity data quality", "recommend filters for the graph build", "quantify damage from <identifier_type>", "pre-graph DQ". (narrative-identity)

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Weekly read-only repository audit. Grades one rotating rubric from .claude/audit-rubrics/, files between zero and five GitHub issues for what it finds, and changes no files. Use when the user asks to audit the repo, or when the audit-weekly workflow invokes it. It never edits, commits, branches, or opens pull requests.

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Produce a coverage & quality profile of a Narrative dataset (or access rule): row count, per-column null/fill rate, cardinality, ranges, top-values, inferred column shape, and quality flags. Reads bundled stats + sample first, recovers missing/stale stats by configuring and recalculating them, and escalates to a cheap `/write-nql` query only for a measure no stat can provide. Descriptive, not prescriptive. Use when: "profile dataset N", "what does dataset N look like", "coverage and quality of <dataset>", "what id types does N emit", "null rates / cardinality for <dataset>", "is this dataset's stats fresh". (narrative-common)

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Apply a set of Rosetta Stone attribute mappings to a Narrative dataset by wrapping them in a one-shot workflow that calls the `CreateRosettaStoneMappingsIfNotExist` task. Consumes the structured output of `/generate-rosetta-stone-mappings`, normalizes the generator's snake_case to the workflow task's camelCase, re-validates every expression against the dataset's current schema, gates on user approval, submits via `narrative_workflows_create`, polls the triggered run, and reports per-mapping created / conflict / failed. Use when: "apply these mappings to dataset N", "create the Rosetta Stone mappings I just generated", "push the mappings I saved earlier to <dataset>", "productionize this mapping list", "submit the suggested_mappings array". (narrative-common)

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Find the canonical Rosetta Stone attribute that best matches a fuzzy description, semantic phrase, or required schema shape. Searches the catalog with pagination, describes the shortlist in one batched call, ranks candidates by name + shape match, and returns the canonical attribute ID plus close alternatives. Use when: "find the X attribute", "what's the graph-edge attribute ID", "look up the email Rosetta Stone attribute", "search the attribute catalog for Y", "which attribute has SOURCE_ID + TARGET_ID + IS_DIRECTED". (narrative-common)

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Interactively build a Narrative identity graph workflow from one or more first-party datasets and (optionally) third-party data sources. Confirms each input dataset is mapped to the Rosetta Stone graph edge attribute (mapping it via /generate-rosetta-stone-mappings if not), then composes and submits a workflow that unions every edge source and labels connected components. Use when: "build an identity graph", "generate an identity graph", "create an identity graph", "stitch these datasets into a graph", "make a graph workflow", "label connected components on these datasets", "I want a person graph / household graph / device graph". (narrative-identity)

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Load when the user asks for CTO advice on feature strategy, architecture direction, build-vs-buy, infrastructure, market positioning, roadmap, self-hosting, cost, or organizational tradeoffs; not for reviewing an existing implementation-plan workstream.

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Create Beamer-style academic PPTX presentations using python-pptx. Produces publication-quality .pptx files with navy-blue Metropolis theme (16:9, frame title bars, progress bar) for conference talks, job market presentations, and seminar slides. Called by /present command.

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Knowledge base from EU Artificial Intelligence Act — Regulation (EU) 2024/1689. Use for AI system classification (high-risk vs prohibited vs general), conformity assessment procedures, GPAI model obligations, transparency and disclosure requirements, governance structure (AI Office / AI Board), penalty tiers, value-chain operator obligations, post-market monitoring, and Annex III use-case analysis. Does not cover EU liability rules for AI (separate proposed directive) or implementation guidance documents issued after July 2024.

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GitHub リポジトリを安全に作成、公開、リリース、監査、保守するためのプロトコル。ローカルルールとロックの確認、最初の git add 前の .gitignore 作成、プライバシーチェックの実行、README/i18n/バナー/メタデータの準備、リリースタグおよび GitHub releases の検証、組織プロファイル、llms.txt ファイル、レジストリリンクの更新を行います。

Protocolo para crear, publicar, lanzar, auditar y mantener repositorios de GitHub de forma segura: verificar reglas locales y bloqueos, crear .gitignore antes del primer git add, realizar verificaciones de privacidad, preparar README/i18n/banner/metadatos, verificar etiquetas de release y lanzamientos de GitHub, y actualizar perfiles de organización, archivos llms.txt y enlaces a registros.

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安全创建、发布、Release、审计和维护 GitHub 仓库的规范协议：检查本地规则与锁文件，在首次 git add 前创建 .gitignore，运行隐私检查，准备 README/i18n/Banner/元数据，验证 release 标签与 GitHub releases，并更新组织 Profile、llms.txt 文件及注册表链接。

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Протокол для безопасного создания, публикации, релиза, аудита и обслуживания репозиториев GitHub: проверка локальных правил и блокировок, создание .gitignore перед первым git add, проверка конфиденциальности, подготовка README/i18n/баннера/метаданных, проверка тегов релиза и релизов GitHub, а также обновление профилей организаций, файлов llms.txt и ссылок в реестрах.

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Manages your own skill landscape: surveys and compares existing skills (Audit mode), researches the web for new skills/plugins (Explore mode), and is at the same time the installer that generates lean subskills (Skill-Finder, family umbrella, maintenance skills) instead of loading a monolith. Use this skill for "compare/audit skills", "which skills are duplicated", "form skill families", "clean up/consolidate skills", "maintain the skill register", "find skills/plugins for topic X", "install new skills", "browse the skill marketplace", or for `/skill-explorer`. Delivers a sub-report per family and a globally numbered decision list; installs/uninstalls only after a security check and explicit approval.

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。