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概要と使いどころ
Automate Attio CRM operations -- search records, query contacts and companies with advanced filters, manage notes, list attributes, and navigate your relationship data -- using natural language through the Composio MCP 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。
Automate Airtable tasks via Rube MCP (Composio): records, bases, tables, fields, views. Always search tools first for current schemas.
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。
Local-only regression / benchmark skill for ui-clone-skills maintainers. Drives the standard ui-reverse-engineering pipeline against the canonical reference site (https://realfood.gov) and records AE/SSIM, iteration count, gate fail counts, and outcome to benchmark/history.csv so prompt / sub-doc / model-version drift surfaces as a trend. Trigger phrases: "run benchmark" / "regression benchmark" / "benchmark clone". The Makefile no longer has a `benchmark` target — setup is inline bash in this skill (Step 1 below). Internal: NOT registered in `.claude-plugin/plugin.json` `skills`. Not part of the public 3-skill marketplace surface. Maintainer tooling 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。
Airtable REST API via curl. Records CRUD, filters, upserts.
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。
Passive domain reconnaissance using Python stdlib. Use this skill for subdomain discovery, SSL certificate inspection, WHOIS lookups, DNS records, domain availability checks, and bulk multi-domain analysis. No API keys required. Triggers on requests like "find subdomains", "check ssl cert", "whois lookup", "is this domain available", "bulk check these domains".
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。
Records decisions and documentation. Use when making architectural decisions, changing public APIs, shipping features, or when you need to record context that future engineers and agents will need to understand the codebase.
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。
Guides AI agents through Senzing entity resolution workflows using the Senzing MCP server. Covers data mapping to Senzing format, SDK code generation (Python, Java, C#, Rust, TypeScript/Node.js), documentation search, error troubleshooting, sample data access, reporting and visualization, SDK setup guides, and V3-to-V4 migration. Use when working with entity resolution, record matching, record linkage, deduplication, Senzing SDK integration, data mapping for Senzing ingestion, or troubleshooting Senzing error codes. Also use when the user mentions matching records across data sources, finding duplicate entities, identity resolution, master data management, or needs to resolve who is who across datasets.
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。
Test, fold or refute hypotheses beside the knowledge record, and reconcile records across branches. Use when open or check say hypotheses wait or an id is CONTESTED, when a write was refused into a hypothesis, before merging a branch whose record changed, and when a pull request's dry run or remeasure is red. Covers consolidate --dry-run, the fold, --refute, --from, same and distinct at the fold, and remeasure.
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。
Map existing materials into a first knowledge record, or investigate a subject's history in depth. Use whenever the user asks to map, survey, go through, reconstruct or investigate what already exists - folders of documents, decision records, meeting notes, emails, exports, code - or wants a starting record built from existing materials, even when the word 'record' is never said: consult this before listing or reading the files yourself, because a map has an agreed scope, a protocol and a report. Also, once, at a suitable moment, to offer the starting choices where a workspace has no record and the work is clearly ongoing - never on a greeting. Learning while working needs no map. Requests come in any 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。
Use when writing or refactoring modern C# with maintainability and performance standards. Keywords: coding standards, C# best practices, refactor C#, records, pattern matching, async best practices.
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。
Escreva código C# moderno com recursos avançados como records, pattern matching e async/await. Otimiza aplicações .NET, implementa padrões empresariais e garante testes abrangentes.
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。
Padrões abrangentes para criar, manter e gerenciar Registros de Decisões de Arquitetura (ADRs) que capturam o contexto e a lógica por trás de decisões técnicas significativas.
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 this Canvas-owned clean-room skill to guide collaborative writing for documentation, proposals, technical specs, decision records, PRDs, RFCs, operating guides, and structured memos. Trigger when the user wants help drafting, organizing, refining, reviewing, or testing a document before sharing it.
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。
Assesses potential suppliers against defined commercial, operational, and compliance requirements. Use when the user provides supplier records plus sourcing requirements and wants a consistent shortlist with strengths and risks.
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。
Qualifies and scores B2B prospects against a supplied ideal customer profile. Use when the user provides a lead list, company records, prospect data, or qualification criteria and wants prioritization, fit analysis, or disqualification reasons.
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。
Identifies likely duplicate profiles or companies using available record evidence. Use when the user provides a list and wants duplicate groups, match confidence, and merge recommendations. Describe records as likely duplicates unless there is an exact deterministic match.
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。
Compares a group of companies by products, positioning, market focus, and differentiators. Use when the user provides several company records and wants a structured competitive comparison.
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。
Normalizes company names, industries, domains, locations, and descriptions. Use when the user provides company records and wants consistent canonical fields with confidence and unresolved items flagged.
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。
Normalizes and improves the consistency of prospect-list data. Use when the user provides a messy prospect or lead export and wants standardized records, flagged issues, and a cleanup summary.
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。
Groups companies by industry, business model, size, maturity, market, or strategic fit. Use when the user provides company records and wants consistent segment assignments with definitions and confidence.
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。
Identifies missing information required to complete qualification, segmentation, or outreach preparation. Use when the user provides records and a goal and wants prioritized research gaps with sources.
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。
Converts inconsistent job titles into standardized role, department, and seniority labels. Use when the user provides records with free-text titles and wants consistent role family, department, and seniority fields.
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 adding, auditing, pruning, archiving, restoring, or reviewing Agent Notes in deepseek-harness; checks every new note for superseded active records, classifies implemented notes by future decision value, deletes rejected notes that no longer prevent a tempting fallacy, and applies the frozen archived/{kind} triplet and manifest rules.
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。
使用 AACT (Aggregate Analysis of ClinicalTrials.gov) PostgreSQL 数据仓库进行批量、历史、聚合性临床试验数据挖掘。Use this skill when the user requests bulk SQL analysis over the full clinical trials data warehouse — historical trial trends, disease landscapes, similar-design matching, or multi-year aggregations across hundreds of thousands of NCT records. 触发场景包括：AACT 查询、临床试验批量分析、PostgreSQL 试验数据、全量 NCT 检索、试验数据挖掘、历史试验分析、clinical trials data warehouse、SQL trials、bulk trial analysis、disease landscape、试验设计相似性匹配、跨年度聚合、sponsor/phase/country 多维统计。**与 clinical-trials-v2 差异**：本 skill 走批量 SQL · 离线大数据（PostgreSQL）；v2 走实时 API · 单查询。两者互补：单条 NCT 实时状态用 v2，百万级历史挖掘用本 skill。支持云端公共 PostgreSQL（aact-db.ctti-clinicaltrials.org · 零部署）和每日 dump 本地还原（高性能 · 离线）两种连接方式，自动检测优先用本地。跨平台（macOS/Linux/Windows）参数化 SQL 防注入，read-only 强制保护。
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。