「tracking」の検索結果

Implement ReasoningBank adaptive learning with AgentDB's 150x faster vector database. Includes trajectory tracking, verdict judgment, memory distillation, and pattern recognition. Use when building self-learning agents, optimizing decision-making, or implementing experience replay systems.

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。

Live SEO data via DataForSEO MCP server. SERP analysis (Google, Bing, Yahoo, YouTube, Google Images), keyword research (volume, difficulty, intent, trends), backlink profiles, on-page analysis (Lighthouse, content parsing), competitor analysis, content analysis, business listings, AI visibility (ChatGPT scraper, LLM mention tracking), and domain analytics. Requires DataForSEO extension installed. Use when user says "dataforseo", "live SERP", "keyword volume", "backlink data", "competitor data", "AI visibility check", "LLM mentions", "image SERP", "google images", "image rankings", or "real search 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。

Optimize resumes for Applicant Tracking Systems, check ATS compatibility, and analyze keyword 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。

Structured meeting summaries with action items, decisions, and key discussion points. Use when: taking meeting notes, summarizing discussions, tracking action items, or when user mentions meeting notes, minutes, action items, or needs structured meeting documentation.

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。

Agile product ownership for backlog management and sprint execution. Covers user story writing, acceptance criteria, sprint planning, and velocity tracking. Use when writing user stories, creating acceptance criteria, planning sprints, estimating story points, breaking down epics, or prioritizing the backlog.

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 plan, design, or implement an A/B test or experiment, or build a growth experimentation program. Also use when the user mentions "A/B test," "split test," "experiment," "test this change," "variant copy," "multivariate test," "hypothesis," "should I test this," "which version is better," "test two versions," "statistical significance," "how long should I run this test," "growth experiments," "experiment velocity," "experiment backlog," "ICE score," "experimentation program," or "experiment playbook." Use this whenever someone is comparing two approaches and wants to measure which performs better, or when they want to build a systematic experimentation practice. For tracking implementation, see analytics. For page-level conversion optimization, see cro.

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。

Managing Linear issues, projects, and teams. Use when working with Linear tasks, creating issues, updating status, querying projects, or managing team workflows.

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 王建硕 wants to systematically grow his X (Twitter) followers by running numbered, A/B-testable growth experiments and tracking which ones actually work. Every action gets a number, a hypothesis, a target metric, a before-state (for rollback), and a verdict. The North-Star metric is the new-follower ÷ profile-visit ratio (conversion) — every profile change is judged against it. Daily it ingests the X Analytics CSV export, scores each running experiment, and recommends keep / rollback. Triggers — "涨粉", "增加 X 粉丝", "X 涨粉实验", "follower growth", "A/B test my profile", "今天的涨粉检查", "/wjs-x-increasing-follower".

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。

Implement ReasoningBank adaptive learning with AgentDB's 150x faster vector database. Includes trajectory tracking, verdict judgment, memory distillation, and pattern recognition. Use when building self-learning agents, optimizing decision-making, or implementing experience replay systems.

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。

Suede-owned measurement discipline for tracking plans, event and conversion instrumentation, UTM and campaign-parameter hygiene, and verification of what actually fires. Use when setting up, auditing, or repairing analytics across web, product, paid, and lifecycle surfaces. NOT FOR: experiment design or significance decisions (use suede-ab-testing), campaign optimization (use suede-ads), attribution models, model comparison, or cross-tool reconciliation (use suede-attribution), or revenue-process architecture (use suede-revops).

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。

Monitor competitor pricing, features, changelogs, dashboards, and product changes with Firecrawl. Use for recurring competitive intelligence, pricing tier extraction, feature change tracking, or structured competitor alerts.

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。

Manage tasks and issues with the td CLI. Use for creating, tracking, querying, and handing off work items. Covers task lifecycle, session management, structured handoffs, epics, dependencies, and boards.

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。

Show whether each skill is earning its context-window cost — combined tokens-used view sorted by waste. Use when the user asks 'are my skills worth it', 'what's my context budget', 'which skills are dead weight', or wants to audit skill value, token cost, or usage. Trigger with '/janitor-value'.

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。

Adding integral action to MPC for offset-free tension tracking.

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。

CRE Asset Management analysis suite — 9 specialist skills for post-acquisition multifamily operations including annual budgeting, monthly variance analysis, rent collection, renewal decisions, lease-up tracking, capex execution, NOI improvement, hold/sell/refi scenario analysis, and quarterly asset review memos.

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。

CRE Legal review suite — 6 specialist skills for PSA review, title & survey analysis, estoppel tracking, loan document review, insurance coordination, and transfer document preparation for multifamily acquisitions.

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。

Build GraphRAG retrieval pipelines on Neo4j using the neo4j-graphrag Python package (v1.22.0+). Covers retriever selection (VectorRetriever, HybridRetriever, VectorCypherRetriever, HybridCypherRetriever, Text2CypherRetriever, ToolsRetriever), external vector DB retrievers (Weaviate, Pinecone, Qdrant), retrieval_query Cypher fragments, query_params, filters, GraphRAG pipeline wiring (GraphRAG + LLM + prompt), all LLM providers (OpenAI, Anthropic, Gemini/VertexAI, Bedrock, Cohere, Mistral, Ollama), embedder setup, index creation, token usage tracking, Cypher 25 SEARCH clause, and LangChain/LlamaIndex integration. Does NOT handle KG construction — use neo4j-document-import-skill. Does NOT handle plain vector search — use neo4j-vector-index-skill. Does NOT handle GDS analytics — use neo4j-gds-skill. Does NOT handle agent memory — use neo4j-agent-memory-skill.

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。

Design error handling, structured logging, and observability with OpenTelemetry (traces, metrics, logs), error classification, recovery patterns (retry with jitter, circuit breaker, bulkhead, timeout), error budgets/SLOs with burn rate alerts, and production incident triage. Use when user asks to implement error handling, logging, monitoring, observability, OpenTelemetry, error boundaries, circuit breakers, retry logic, or SLO tracking. Do NOT use for incident runbooks (use runbook-gen), vendor-specific APM setup (Datadog, Sentry agent config), or K8s debugging.

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。

Runs security audits on codebases — full scans, diff reviews, threat models, vulnerability triage, remediation guidance, and finding tracking. Activate when the user says "security scan", "audit this repo", "review this PR for security", "threat model", "triage vulnerabilities", "fix this vuln", or "track findings".

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。

Automated iterative agent runner for spec-based development in Kiro. Wraps kiro-cli in a self-correcting bash loop that picks up tasks from a Kiro spec, implements them one at a time, verifies against exit criteria, and accumulates corrections and codebase patterns across iterations. Use this skill when the user mentions "ralph loop", "ralph", "spec loop", "iterative spec runner", "run my spec tasks automatically", "kiro spec automation", "self-correcting agent loop", "implement spec tasks in a loop", "run kiro-cli in a loop", "automated task implementation", or wants to drive a Kiro spec to completion through repeated agent iterations. Also use when the user wants to set up, configure, troubleshoot, or understand the Ralph Loop workflow — including progress tracking, corrections, codebase patterns, timing logs, and the summary dashboard.

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 when optimizing app store listings, researching keywords, or tracking mobile app performance on Apple App Store and Google Play Store.

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 plan, design, or implement an A/B test or experiment. Also use when the user mentions "A/B test," "split test," "experiment," "test this change," "variant copy," "multivariate test," or "hypothesis." For tracking implementation, see data-and-funnel-analytics.

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。

Designs and runs reproducible evaluations for AI agents, prompts, tools, skills, and model-backed workflows using realistic datasets, isolated baselines, objective assertions, rubric grading, trajectory analysis, cost/latency tracking, and regression comparison. Use when measuring agent quality, optimizing skill triggering, comparing prompts or models, or gating an AI feature release. Not for ordinary deterministic unit tests.

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。

Defines CC-* code rules and waivers. Use when mapping lint findings to rule IDs or tracking complexity mass, erosion, and verbosity across changes.

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。