459 件 ・ 関連度順
概要と使いどころ
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.
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.
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。
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.
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 / 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。
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。
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。
Give insights into the source code.
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。
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。
Analyzes AWS CloudWatch Logs using the CloudWatch Logs API and Logs Insights query syntax. Identifies error patterns, calculates error rates, and generates metric filters from log 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。
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。
Investigates anomalous patterns in AWS CloudWatch Logs using the CloudWatch Logs Insights API and CloudWatch Anomaly Detection. Correlates log spikes with deployment events via AWS CodeDeploy API.
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.
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。
Uses AWS SDK CloudWatchClient GetMetricData and CloudWatch Logs Insights StartQueryExecution to automate incident triage. Correlates alarms via DescribeAlarms with X-Ray trace segments for root cause analysis.
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。
Expert product discovery guidance for user research and problem validation. Use when conducting user interviews, validating problems, applying jobs-to-be-done framework, sizing opportunities, customer segmentation, competitive analysis, prototype testing, usability testing, designing surveys, or synthesizing research insights. Covers discovery sprints, continuous discovery, and research 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。
Extracts reusable patterns from sessions. Use at session end to capture debugging insights and project-specific knowledge.
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。
WHOOP CLI with health insights, trends analysis, and data fetching (sleep, recovery, HRV, strain).
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 Threads (Meta) audience and niche from real data. Scan a niche query or hashtag for the top posts right now with likes, replies, reposts, and quotes, and pull a profile (yours or a competitor) for follower count, bio, verified status, and recent posts with engagement. Powered by Apify, no login. Triggers on "analyze Threads", "what is working on Threads", "top posts for", "competitor Threads profile", "niche scan", "follower count". Not for writing posts (use threads-post-writer).
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。
Extract actionable insights from completed coding sessions. Use when a session completes to capture learnings for future sessions.
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。
Pre-pipeline aggregator that scans AI agent cache directories (.claude, .cursor, .antigravity, .openclaw) or any user-specified directory for experimentation logs, extracts insights and numeric results, and formats them as PaperOrchestra-ready inputs (idea.md + experimental_log.md). TRIGGER when the user says "aggregate my agent logs for paper writing", "extract experiments from my coding agent history", "prepare PaperOrchestra inputs from my cache", "turn my agent logs into a paper", mentions a folder or directory they want to use as the basis for a paper, or wants to run PaperOrchestra but only has scattered agent experiment histories rather than structured inputs. Run this BEFORE paper-orchestra. Also called automatically by paper-orchestra when workspace/inputs/idea.md or workspace/inputs/experimental_log.md are missing.
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。
Reflective Memory. Use when working with memory, reflection, context management, or intent tracking. Provides the reflective memory practice — how and when to use keep tools for capturing insights, tracking commitments, and maintaining awareness.
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。