654 件 ・ 関連度順
概要と使いどころ
A step-by-step playbook for early-stage startups going from pre-seed to their first 1000 users. Covers MVP validation, initial traction channels, and founder-led growth tactics. By @WeiYipei — follow on X for more startup growth insights.
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The revenue-focused SaaS playbook for going from $0 to $50K MRR. Covers pricing experiments, churn reduction, activation optimization, and expansion revenue tactics. Follow @WeiYipei on X for SaaS growth insights.
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Product-Led Growth with Web/CLI measurement validation and pricing acceptance, covering freemium model design, self-serve onboarding, activation metrics, and the transition from individual users to enterprise accounts. Follow @WeiYipei on X for PLG insights.
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Feedback synthesis — cluster support tickets, NPS verbatims, app store reviews, and churn surveys by theme, separate signal from noise, and produce an actionable insight report. Use when asked to "synthesize this feedback", "analyze support tickets", "what are users complaining about", "NPS analysis", "churn feedback synthesis", or "what's the feedback telling us".
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。
Run a user interview — produce an interview guide and synthesize the output into an actionable insight report. Use when asked to "run a user interview", "synthesize these interview notes", "what do users actually want", "build a persona from this feedback", "find the JTBD in these transcripts", or "analyze this interview data".
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。
Read your TikTok niche and audience from real data. Scan a hashtag for top videos with plays, likes, and comments, see which hooks and sounds are working, pull a profile's videos (yours or a competitor's), and read the commenters on a video (TikTok hides likers, so commenters are the signal). Powered by Apify, no login. Triggers on "what is trending on TikTok", "analyze this hashtag", "competitor videos", "read my comments", "which sounds are working". Not for writing a script (use tt-hook-scripter).
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Set up Hierarchical Agent Memory (HAM) — scoped CLAUDE.md files per directory that reduce token spend. Trigger on "go ham", "set up HAM", "ham commands", "ham help", "ham route", "ham remove", "ham update", "ham status", "ham benchmark", "ham baseline start", "ham baseline stop", "ham metrics clear", "HAM savings", "HAM stats", "HAM dashboard", "HAM sandwich", "HAM insights", "HAM carbon", or "ham sync".
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。
Set up and maintain Hierarchical Agent Memory (HAM) for Codex using scoped local memory files. Trigger on "go ham", "set up HAM", "ham commands", "ham help", "ham route", "ham remove", "ham update", "ham status", "ham benchmark", "ham baseline start", "ham baseline stop", "ham metrics clear", "HAM savings", "HAM stats", "HAM dashboard", "HAM sandwich", "HAM insights", "HAM carbon", or "HAM audit".
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。
GTM prospecting, enrichment, outreach, and Deepline Play work/audits. Providers: adyntel,affinity,ai_ark,akta,allegrow,amplemarket,apify,attention,attio,aviato,bettercontact,bigquery,bluesky,bounceban,brave,browserbase,builtwith,cbinsights,clay,clickhouse,cloudflare,contactout,contextdev,crunchbase,crustdata-v3,customer_db,databricks,dataforseo,datagma,dealroom,deepline_ip_to_company,deepline_native,deeplineagent,discolike,dropleads,edges,emailbison,emailguard,enformion,enigma,exa,findymail,firecrawl,fireflies,firmable,forager,fullenrich,fundable,generic_http,getleads,gong,google_ads_audiences,google_workspace,govfiles,grain,hackernews,harvestapi,heyreach,hubspot,hunter,icypeas,instantly,intercom,ipqs,kaspr,kernel,leadmagic,lemlist,limadata,linkedin_ads_audiences,luma,lusha,meta_audiences,millionverifier,nooks,notion,openmart,opensosdata,openwebninja,outreach,parallel,peopledatalabs,pitchbook,podscan,polymarket,postgres,predictleads,prospeo,quickenrich,rapidapi,redshift,rocketreach,salesforce,salesforge,….
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。
Crawl GitHub trending repositories, analyze with LLM for Chinese insights, categorize by themes, compute diffs against history, and generate Markdown reports. Default brief mode stops at trend analysis; optional detailed mode appends per-project analysis. Supports incremental gap-filling and selective re-analysis with caching.
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。
AI-powered issue operations via gh-models. TRIGGERS - issue summarization, auto-labeling, issue insights.
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Tools are how AI agents interact with the world. A well-designed tool is the difference between an agent that works and one that hallucinates, fails silently, or costs 10x more tokens than necessary. This skill covers tool design from schema to error handling. JSON Schema best practices, description writing that actually helps the LLM, validation, and the emerging MCP standard that's becoming the lingua franca for AI tools. Key insight: Tool descriptions are more important than tool implementa
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。
Intelligent financial management skill for Claude Code that provides comprehensive PocketSmith API integration with AI-powered analysis, transaction categorization, rule management, tax intelligence, and scenario planning. Use when working with PocketSmith data for (1) Transaction categorization and rule management, (2) Financial analysis and reporting, (3) Australian tax compliance (ATO) and deduction tracking, (4) Scenario analysis and forecasting, (5) PocketSmith setup health checks, (6) Budget optimization and spending insights.
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Expert research analyst specializing in comprehensive information gathering, synthesis, and insight generation. Masters research methodologies, data analysis, and report creation with focus on delivering actionable intelligence that drives informed decision-making.
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Memory is the cornerstone of intelligent agents. Without it, every interaction starts from zero. This skill covers the architecture of agent memory: short-term (context window), long-term (vector stores), and the cognitive architectures that organize them. Key insight: Memory isn't just storage - it's retrieval. A million stored facts mean nothing if you can't find the right one. Chunking, embedding, and retrieval strategies determine whether your agent remembers or forgets. The field is fragm
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Expert knowledge synthesizer specializing in extracting insights from multi-agent interactions, identifying patterns, and building collective intelligence. Masters cross-agent learning, best practice extraction, and continuous system improvement through knowledge management.
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VNX-specific domain expertise for the intelligence system, central state databases, and dispatch lifecycle. Use when working on quality_intelligence.db / runtime_coordination.db / dispatch_tracker.db, code_snippets FTS5, snippet_metadata linkage, success_patterns / antipatterns, T0 state projection (t0_state.json), intelligence_injections lifecycle, dispatch → receipt → events → archive flow, central state architecture, project_id propagation across VNX, or any code that reads/writes VNX governance state. Triggers include: editing intelligence injection code, querying central DB for cross-project insights, debugging "intelligence not appearing in T0 state", modifying schema in `schemas/quality_intelligence.sql` or `schemas/runtime_coordination_v*.sql`, working with `scripts/quality_db_init.py` or `scripts/lib/coordination_db.py`.
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。
VNX-specific domain expertise for the intelligence system, central state databases, and dispatch lifecycle. Use when working on quality_intelligence.db / runtime_coordination.db, code_snippets FTS5, snippet_metadata linkage, success_patterns / antipatterns, T0 state projection (t0_state.json), intelligence_injections lifecycle, dispatch → receipt → events → archive flow, central state architecture, project_id propagation across VNX, or any code that reads/writes VNX governance state. Triggers include: editing intelligence injection code, querying central DB for cross-project insights, debugging "intelligence not appearing in T0 state", modifying schema in `schemas/quality_intelligence.sql` or `schemas/runtime_coordination_v*.sql`, working with `scripts/quality_db_init.py` or `scripts/lib/coordination_db.py`.
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。
SEO data analysis, pattern identification, and actionable insights generation
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。
Add a new learning to the agent learnings file. Use when the user says /add-learning, asks to record a lesson, or when an implementation produced a reusable insight worth preserving for future agents.
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Use this skill for ANY financial market, investment, or macro-economic analysis. ALWAYS trigger this skill — do not skip it — when the user asks about: BTC, bitcoin, crypto, gold, silver, oil, stocks, forex, bonds, interest rates, Fed policy, inflation, deflation, stagflation, market structure, trading setups, portfolio allocation, asset allocation, investment strategy, price analysis, market outlook, geopolitics affecting markets, or any question involving "should I buy/sell/hold [any asset]", "what will [asset] do", or "analyze [market/asset]". Apply Genesis Protocol (First Principle + System Thinking) combined with SMC Layer analysis to produce structured insights with explicit scenarios, entry/exit zones, and uncertainty. For non-market strategic thinking, use genesis-mind-strategic-intelligence instead.
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 skill for ANY financial market, investment, or macro-economic analysis. ALWAYS trigger this skill — do not skip it — when the user asks about: BTC, bitcoin, crypto, gold, silver, oil, stocks, forex, bonds, interest rates, Fed policy, inflation, deflation, stagflation, market structure, trading setups, portfolio allocation, asset allocation, investment strategy, price analysis, market outlook, geopolitics affecting markets, or any question involving "should I buy/sell/hold [any asset]", "what will [asset] do", or "analyze [market/asset]". Apply Genesis Protocol (First Principle + System Thinking) combined with SMC Layer analysis to produce structured insights with explicit scenarios, entry/exit zones, and uncertainty. For non-market strategic thinking, use genesis-mind-strategic-intelligence instead.
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。
Scans AWS CloudWatch Logs using the CloudWatch Logs Insights API and CloudWatch Anomaly Detection API. Identifies unusual error patterns, latency spikes, and log volume anomalies across log groups.
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Triages AWS CloudWatch alarms using boto3 CloudWatch.describe_alarms, CloudWatch Logs Insights queries, and AWS X-Ray trace analysis via the xray-sdk. Correlates alarm triggers with deployment events.
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。