650 件 ・ 関連度順
概要と使いどころ
Build one release phase against the specs — plan it on an interactive board (endpoints, components, resources, with what already exists read from the codebase), let the user adjust, then build node by node while the board shows live progress. Use when the user wants to build a phase, implement the phase-1 slice, execute the plan from a story map or spec, or watch a build happen visually.
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。
Appwrite Swift SDK skill. Use when building native iOS, macOS, watchOS, or tvOS apps, or server-side Swift applications with Appwrite. Covers client-side auth (email, OAuth), database queries, file uploads, real-time subscriptions with async/await, and server-side admin via API keys for user management, database administration, storage, and functions.
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。
Continuous environmental scanning across six PESTEL categories — Political, Economic, Social, Technological, Environmental, Legal. Not a one-time analysis but an ongoing radar. Three modes. Diagnostic mode runs a full scan and prioritises forces by impact, speed, and reversibility. Review mode checks the watch list monthly for movement and new signals. Alert mode defines concrete monitoring triggers for each top force. Use whenever someone wants to understand external forces acting on their business, assess macro risk, prepare for planning, or check whether the world has moved since last time they looked. Connects to the strategy map (forces act on the chain) and Wardley Map (forces drive component evolution). Based on practice by Eterdis (eterdis.com).
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。
Watch for the next dev/prod error or request in a Convex app and react to 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。
Watch for the next dev/prod error or request in a Convex app and react to 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。
Detects and prevents code injection attacks targeting serverless functions (AWS Lambda, Azure Functions, Google Cloud Functions) through event source poisoning, malicious layer injection, runtime command execution, and IAM privilege escalation via function modification. The analyst combines static analysis of function code, CloudTrail event correlation, runtime behavior monitoring, and IAM policy auditing to identify injection vectors across the expanded serverless attack surface including API Gateway, S3, SQS, DynamoDB Streams, and CloudWatch event triggers. Activates for requests involving Lambda security assessment, serverless injection detection, function event poisoning analysis, or serverless privilege escalation investigation.
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。
Deploys canary files (honeytokens) across file systems to detect ransomware encryption activity in real time. Uses strategically placed decoy documents monitored via file integrity monitoring or OS-level watchdogs to trigger alerts when ransomware modifies or encrypts them. Activates for requests involving ransomware canary deployment, honeyfile setup, deception-based ransomware detection, or file integrity monitoring for encryption.
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。
Deploy cloud-native deception across AWS, Azure, and GCP using decoy (honey) resources whose only purpose is to generate a high-fidelity alert the instant an attacker touches them: canary IAM access keys, permission-less decoy users/roles/service principals, honey object-storage buckets, and decoy secrets in Secrets Manager / Key Vault / Secret Manager. Wires detection through CloudTrail + EventBridge, Azure Sentinel honeytoken watchlists + Defender, and GCP Cloud Audit Logs, so any use of a decoy is routed to the SOC with near-zero false positives. Use when protecting cloud accounts and data stores, when an org has only on-prem honeypots and needs cloud coverage, when seeding fake AWS keys to catch credential theft and code-leak exposure, or when detecting cloud reconnaissance and lateral movement. Keywords: cloud deception, canary token AWS, honey S3 bucket, decoy IAM credentials, CloudTrail alert, GuardDuty, Sentinel honeytoken, decoy secret, honey service account, cloud honeypot, breach detection.
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。
Scrapes pump.fun/board using Chrome browser automation to extract the top 100 trending Solana tokens and writes structured markdown for a trading agent to consume. Use this skill any time you need to: scan pump.fun for new tokens, refresh the pump.md token list, run the scheduled board scrape, collect Solana meme token data, or build/update a trading watchlist from pump.fun. Even if the user says something casual like "check pump" or "update the token list" or "what's trending on pump", use this skill. The output file path and format are configurable but default to /Users/8bit/solanaos/pump.md.
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。
Fetch GitHub issues, spawn sub-agents to implement fixes and open PRs, then monitor and address PR review comments. Usage: /gh-issues [owner/repo] [--label bug] [--limit 5] [--milestone v1.0] [--assignee @me] [--fork user/repo] [--watch] [--interval 5] [--reviews-only] [--cron] [--dry-run] [--model glm-5] [--notify-channel -1002381931352]
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 the user wants to send the same prompt to several LLMs and compare the answers - sequentially (stopwatch, clean per-lane timing) or in parallel (true race), with optional repetitions per model, judged by the starting model across quality, correctness, completeness, instruction fidelity and latency (time is only one dimension). Triggers on /compare-race, 'model race', 'vergleiche modelle', 'gleicher prompt an mehrere modelle'.
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。
Report what is genuinely new in the last 30 days on a topic, by slicing locally collected nightly channel databases with no live crawling. Run it before a strategic decision, as the fresh-signal feed for the gap phase. Triggers: "/last30days <topic>", "trendwatch <topic>", "what changed last 30 days on X".
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。
Send and read messages between machines in a fleet over a shared Telegram group, so cross-machine coordination survives a file-sync outage and humans can watch the machines talk. A synced folder stays as the fallback for oversized payloads. Triggers: "/bus", "post to the bus", "check the bus", "tell the other machines".
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 the user asks to "set up my founder social-selling routine", "build a daily engagement block for target accounts", or "turn funding / hiring signals into selling plays"; produces the founder/seller daily operating block — a time-boxed engagement-block spec (substantive value-add comments on target-account posts, never a pitch), warm-touch-before-ask cadence rules, trigger-response plays consuming the social-pulse-monitor B2B trigger watchlist (funding / hiring / launch signals), and a quarterly diagnostic that reads LinkedIn SSI as an input diagnostic only (Estimated, vendor-defined — never a target metric). All volume norms are Estimated/community-derived; all execution is human — zero mass-DM, zero connection-request automation, zero engagement automation (LinkedIn UA §8.2 red line); 1:1 pitch and DM mechanics route to outreach-manager. Not for cold email sequences — use cold-outbound-sequencer. 社交销售/创始人日常互动块/热身触达节奏/触发信号剧本
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 the user asks to "check if our surfaces have drifted from the canon", "watch for competitor repositioning", or "define when we should reposition"; produces a drift report — self-drift per flagship surface vs the narrative-registry canon over time (via wayback.py, change history Measured with as-of dates), competitor-repositioning alerts, an explicit repositioning-trigger condition set, and a D1/W1/M1 message-shift retro (intended vs actual pull-through, evidence-labeled) — feeding the TALE L drift-audit sub-items and the narrative-whiplash guardrail fact base. Not for the first-time consistency check before a surface ships — use narrative-cascade-planner; not for computing the TALE profile result or running the vetoes — use narrative-quality-auditor; not for echo-rate / AI-answer resonance measurement — use narrative-resonance-monitor. 自漂移监测/竞品重定位告警/重定位触发/叙事漂移复盘
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。
When the user wants to set up a recurring, self-running marketing workflow — a repeatable loop an AI agent runs on a cadence (weekly, daily, on a trigger) rather than a one-off task. Also use when the user mentions 'marketing loop,' 'recurring marketing workflow,' 'automate my marketing,' 'marketing on autopilot,' 'weekly marketing review,' 'ad fatigue check,' 'content refresh loop,' 'churn watch,' 'ranking drop alert,' 'always-on marketing,' 'marketing automation workflow,' or 'run this every week.' Use this to pick, adapt, and schedule an ongoing marketing loop that orchestrates the other marketing skills. For one-off marketing ideas, see marketing-ideas. For the experimentation loop specifically, see ab-testing.
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。
Implements type-safe reactive SQL persistence in Flutter using Drift v2.32 (formerly Moor) built on SQLite with automatic code generation. Activates when defining table schemas with Drift DSL, writing type-safe join or subquery operations, handling schema migrations with MigrationStrategy, using reactive watch() streams for live UI updates, implementing ACID transactions, defining foreign key constraints or cascade deletes, enabling WAL mode for write performance, troubleshooting generated .g.dart type mismatches, migrating from sqflite or sqlite3 v2 to v3, using DAOs for query separation, or building offline-first apps requiring strong relational data integrity and compile-time SQL verification.
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。
Provider (v6.x) state management for Flutter using ChangeNotifier and InheritedWidget with MVVM architecture pattern. Use this skill when implementing Provider-based state management, using Consumer/Consumer2 widgets for targeted rebuilds, accessing state with context.read/context.watch/context.select, setting up ChangeNotifierProvider or StateNotifierProvider, configuring MultiProvider for multiple providers, implementing ProxyProvider for dependent state, using Selector for optimized rebuilds, migrating from StatefulWidget to reactive state, maintaining legacy Provider codebases, debugging provider not found errors, preventing memory leaks with proper ChangeNotifier disposal, fixing context access issues in initState, or resolving .value constructor anti-patterns. Essential for apps with shared state across widgets (shopping carts, authentication, global settings, form state, theme management), when teaching state management fundamentals, or when debugging memory leaks and resource cleanup in Provider-based applications.
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。
Experimental workflow for monitoring explicitly authorized delivery tasks and reporting stalled work. Report-only: findings are produced in the session or as a draft; no scheduled action is taken.
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 building this Hydrolix Grafana datasource plugin — frontend webpack bundle (`dist/module.js*`) or Go backend binary (`dist/gpx_plugin_*`). Covers the split between host-built frontend and container-built Go, when each needs an explicit rebuild, watch vs one-shot, and the verification chain (typecheck / lint / unit tests). Apply for any task that edits `src/**`, `pkg/**`, `Magefile.go`, `webpack.config.ts`, or that needs to confirm `dist/` matches the working tree before running e2e or the dev stack.
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 running the dev Grafana stack and/or the Playwright e2e suite against a *packaged* plugin zip (a release/RC artifact like `hydrolix-hydrolix-datasource-<ver>.zip`) rather than the working-tree `dist/`. Unpacks the package into a timestamped `dist_<timestamp>/dist` sandbox alongside copies of the compose artifacts, so the run is reproducible and the folder stays available to experiment with afterwards. Delegates stack bring-up to `stack-run` (frozen mode: read-only mount + `DEV=false` so the mage-watcher can't rebuild the packaged Go binary), verifies signature/version, runs the suite, and treats cleanup as optional — the sandbox is deleted only with the user's approval. Apply whenever the task is "test this plugin package / zip" or "run e2e against the built artifact".
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。
Fetch and read transcripts from YouTube videos. Use when you need to summarize a video, answer questions about its content, or extract information from 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。
Use when working with Consul Kv — consul KV store management, key namespace analysis, session/lock inspection, and watch configuration. Covers KV tree structure, key count by prefix, session health, prepared queries, and transaction support diagnostics. Read this skill before any Consul KV operations.
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 working with Aws — mANDATORY parallel execution patterns (30x speedup), CloudWatch statistics syntax, Cost Explorer aggregation, output token limits, and common pitfalls.
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。