데이터·AI 스킬

Intelligent README.md generation prompt that analyzes project documentation structure and creates comprehensive repository documentation. Scans ./ directory files and creates a README.md file for the project. Only used when creating a new repository. or when the README.md file is missing.

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Busabase-backed App-in-Skill dashboard for a shared LLM gateway's cost and model governance — daily spend trend, cost breakdown by service/model, a canary-rollout status board, and a deterministic cost/error anomaly list. Use when the user invokes $kelly-llm-gateway or /kelly-llm-gateway, or wants to review LLM gateway spend, model routing, canary rollouts, rollback readiness, or cost/error anomalies for services routed through one shared gateway to multiple LLM providers/models. Human actions (promote/rollback/hold a rollout, acknowledge an anomaly) write directly onto the route's own Busabase record — this skill never changes a real routing config.

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E-commerce product-management desk (Busabase App-in-Skill) for catalog master data, SKUs, pricing, inventory, channel status, content assets, compliance notes, lifecycle state, and approval-gated product operations. Use when the user invokes $kelly-products or /kelly-products, asks for 电商商品管理, 商品库, SKU 管理, inventory/reorder, product status across Amazon/Shopify/TikTok Shop/eBay, channel publishing approvals, price-change review, quality holds, product lifecycle/archive decisions, or a Busabase-backed product management desk.

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Run a Busabase-backed sandbox desk for scoring and triaging course-instructor ("讲师") candidates found on a recruiting-style platform. Use whenever the user invokes $kelly-instructor-sourcing or /kelly-instructor-sourcing, wants a lightweight instructor candidate tracker, needs to score sourcing-platform leads against a three-axis rubric (background endorsement, expertise depth/breadth, teaching-service ability) before reaching out, or wants to log a WeChat add without building a live-connected outreach tool. Three subcommands - setup, criteria, review - plus the desk itself. v1 is sandbox-only — every dataset is deterministic mock data, it never connects to a real sourcing-platform or messaging account, and it never sends a message or automates a WeChat add - that stays entirely human-performed outside the app.

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Insurance-industry App-in-Skill for read-only data governance, backed by an operator-provisioned Busabase workspace (one Drive node for the file drive plus four Bases for QA pairs, featured information, insurer notices, and user feedback) and trusted export/restore/PDF-text-backfill scripts. Use when the user invokes $kelly-insure-data or /kelly-insure-data, wants an insurance data workspace with UI, needs to review insurance files, metadata completeness, QA pairs, featured information, insurer notices, or user feedback, wants to back up or restore a Kelly Insure Data Busabase workspace from local PDFs, or wants Busabase Drive/Base data surfaced for data quality review and ongoing data governance.

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Busabase-backed App-in-Skill control panel / kanban board for a BD or sourcing team triaging merchant and business financing leads for a lender or investment fund. Use when the user invokes $kelly-lead-funnel or /kelly-lead-funnel, wants to review the deal sourcing pipeline, funnel, lead board, or asks to move a lead's stage, reject a lead, add a note, score a lead, or see funnel conversion rates. Deterministic rule-based lead scoring only — never an LLM call — and never sends outreach, signs term sheets, or moves money.

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Busabase App-in-Skill dashboard over a fixed, deterministic mock user-behavior funnel dataset (browse → search → compare → booking attempt/abandon → complete) for a generic consumer booking product. Use when the user invokes $kelly-behavior-predict or /kelly-behavior-predict, wants to review funnel drop-off, per-segment predicted next actions, or backtest a rule-based "predicted next action" heuristic against a mock historical sample. Fully deterministic mock data and a hand-recomputable rule — never a real ML/LLM model, never a live system.

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Busabase-backed App-in-Skill low-code agent configuration and governance console for a platform team. Use when the user invokes $kelly-agent-builder or /kelly-agent-builder, wants to review or edit a catalog of mock LLM agent configs, check quota usage, find configs that need attention, move a draft to live, pause a live agent, or archive an agent. Mock config/governance console only — it never provisions or calls any real agent.

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Extract balance sheet, income statement, shares outstanding, organic growth, and GAAP reconciliation data from financial PDFs. Standardize via Tiger-Transformer and append structured output to the document markdown.

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Execute a single deterministic Python script to compute all derived financial metrics (EBITA, Invested Capital, Tax Rates, Summary Table) from extracted financial data.

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Use after changing prompts, skills, the compiler, or editorial policies — or when the user asks to measure edit quality, run regression evals, check agreement with approved edits, or says '評価して', '一致率を測って', 'eval を回して'. Compares agent output against human-approved golden projects.

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Tauri v2 (Rust + WebView のデスクトップ / モバイルアプリフレームワーク、Electron とは別) リファレンス。 tauri.conf.json, tauri CLI, command / invoke / Channel, AppHandle, Tauri capabilities / permissions (ACL), @tauri-apps/api, 公式プラグイン (fs, dialog, updater, store, sql), sidecar, 署名・配布, WebDriver。

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Kotlin (JetBrains 製プログラミング言語, kotlinlang.org) の言語仕様・標準ライブラリ・ツールチェーン。 null safety, data class, sealed class, 拡張関数, context parameters, スコープ関数 (let / run / apply / also / with), コレクション, Uuid, Duration (kotlin.time), kotlinx.coroutines (suspend, Flow), kotlinx.serialization (@Serializable, Json, ProtoBuf, CBOR), KGP, Maven, KSP, kapt, K2, KDoc。

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Expo (React Native フレームワーク, Expo SDK 58 / EAS) のリファレンス。 Expo Router (file-based routing, expo-server, data loaders, +api.ts), app.json / app.config, config plugins, Expo Modules API, expo-* SDK, Expo UI, EAS Build / Update / Submit / Workflows, eas.json, expo-updates (OTA), CNG / prebuild, Expo CLI (npx expo), development build (dev-client), expo-notifications push。

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Rules for the shared MatrxDataTable and its drill-down. Use when configuring, migrating or reviewing table columns, filters, toolbars, record links or pagination, adding group-by, 'by X' analytics or counts and sums by status, owner, model or day, or replacing hand-built totals or lists.

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Rust 製ローカルファースト vector 特化クエリ DB fandhe-db (PostgreSQL wire protocol v3 互換) の fandhe-db-engine / fandhe-db-wire-server リファレンス。 USING PLAN / USING OPERATION_ID 構文、DDL / JOIN / CTE / window 関数、 拡張クエリ・COPY・トランザクション、TLS 1.3 / SCRAM-SHA-256、 NoSQL HTTP 表層、precision・recall モード、HNSW / BM25 / RRF hybrid / rerank、 redb 永続化、tenant・RLS、SIMD / wgpu、wire_code エラー契約。

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Manual-only orchestrator for medium-to-large software development. Turns a broad goal into PR-sized and operational nodes with verification gates, then coordinates approved execution across follow-up prompts. Activate only when the user explicitly invokes my-epic; once activated, keep orchestrating that epic for the rest of the session. Do NOT use for an uninvoked planning or implementation request, a single small bug fix, one obvious PR, pure code review, ordinary SDD work that fits in one spec/PR, or implementing an epic during the same first call that created it.

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Hand off a task with only the context needed to continue in a fresh session. Use for "handoff", "引き継ぎ", "別セッションへ渡す", or "別マシンで続ける". The orca subcommand launches an independent Orca workspace or terminal tab; without it, produce a paste-ready prompt backed by a verified remote branch or PR. Do not launch sessions for capability questions or design requests.

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Detect insider threat behavioral indicators including unusual data access, off-hours activity, mass file downloads, privilege abuse, and resignation-correlated data theft.

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Implement User and Entity Behavior Analytics using Elasticsearch/OpenSearch to build behavioral baselines, calculate anomaly scores, perform peer group analysis, and detect insider threat indicators such as data exfiltration, privilege abuse, and unauthorized access patterns.

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Detect DNS-based data exfiltration by analyzing Zeek dns.log for high-entropy subdomains and anomalous query patterns

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Analyze WAF (ModSecurity/AWS WAF/Cloudflare) logs to detect SQL injection attack campaigns. Parses ModSecurity audit logs and JSON WAF event logs to identify SQLi patterns (UNION SELECT, OR 1=1, SLEEP(), BENCHMARK()), tracks attack sources, correlates multi-stage injection attempts, and generates incident reports with OWASP classification.

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Detecting data exfiltration attempts from AWS S3 buckets by analyzing CloudTrail S3 data events, VPC Flow Logs, GuardDuty findings, Amazon Macie alerts, and S3 access patterns to identify unauthorized bulk downloads and cross-account data transfers.

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Detects insider data exfiltration by analyzing DLP policy violations, file access patterns, upload volume anomalies, and off-hours activity in endpoint and cloud logs. Uses pandas for behavioral analytics and statistical baselines. Use when investigating insider threats or building user behavior analytics for data loss prevention.

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