「intervention」の検索結果

This skill should be used when the user asks to "これらのIssueを全部実装して", "implement these issues autonomously", "Issue一覧を自動で片付けて", "これらのタスクを順番に実装してPRにして", or provides a list of GitHub Issue numbers/URLs for autonomous batch implementation. Fetches all specified Issues, resolves dependency order, then iterates through each — creating a worktree, implementing, and opening a PR — without user intervention.

Formats and structurally validates local treatment-plan documentation after clinical decisions have already been supplied and verified by authorized licensed professionals. Use for source traceability, clinician-authored intervention records, goals and checkpoints, shared-decision records, reconciliation handoffs, and release gates—not for clinical decision-making.

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Use when a coding task should be driven end-to-end from issue intake through implementation, review, deployment, and acceptance verification with minimal human re-intervention.

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Multi-agent autonomous startup system for Claude Code. Triggers on "Loki Mode". Orchestrates 100+ specialized agents across engineering, QA, DevOps, security, data/ML, business operations, marketing, HR, and customer success. Takes PRD to fully deployed, revenue-generating product with zero human intervention. Features Task tool for subagent dispatch, parallel code review with 3 specialized reviewers, severity-based issue triage, distributed task queue with dead letter handling, automatic deployment to cloud providers, A/B testing, customer feedback loops, incident response, circuit breakers, and self-healing. Handles rate limits via distributed state checkpoints and auto-resume with exponential backoff. Requires --dangerously-skip-permissions flag.

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Leverages BigQuery's built-in machine learning and GenAI capabilities for advanced data analytics. Use when you need to write SQL queries that perform time-series forecasting, predict values, detect outliers or anomalies, find key drivers, perform semantic search or vector search, classify text, calculate similarity, summarize content, translate language, evaluate models, filter by semantic conditions, measure the causal effect of an intervention, compute correlations between columns, detect change points or structural breaks, extract trend or seasonality components, or leverage generative AI capabilities in BigQuery. Do not use for general BigQuery dataset, table, or job management requests.

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Use when configuring `rig watchdog` policies, authoring wake/refocus/alignment-checkpoint messages, or choosing the right intervention level for a stale-owner situation. The 3-level continuity-check stack (wake / refocus / alignment-checkpoint), the discipline that prevents cadence pollution and bureaucracy theater, and the artifact-pool loop-edge pattern for evidence-aware waking.

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Production-grade Bayesian causal inference with PyMC, CausalPy, and DoWhy. Enforces DAG-first thinking, mandatory user checkpoints for assumptions, design-specific refutation, and defensible reporting with causal language guardrails. Trigger on: causal inference, causal effect estimation, treatment effects, counterfactuals, difference-in-differences (DiD), synthetic control, regression discontinuity (RDD), interrupted time series (ITS), instrumental variables (IV), propensity scores, DAGs, causal graphs, confounders, backdoor criterion, do-calculus, interventional distributions, pm.do(), pm.observe(), CausalPy, DoWhy, mediation analysis, refutation, sensitivity analysis, parallel trends, placebo tests, or any question of the form "does X cause Y" or "what is the effect of X on Y."

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。

Iterative diagnose-and-fix workflow that repeatedly uses the diagnose skill to find a proven problem, plan the smallest credible improvement, implement it, re-diagnose, and continue until no useful fix remains or user intervention is required. Requires the diagnose skill; selected installs must also install diagnose. Use when asked to keep debugging, fix and verify, iterate until satisfied, improve after diagnosis, or run a diagnosis/fix/verification loop.

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。

Generate concise (3-4 page), focused medical treatment plans in LaTeX/PDF format for all clinical specialties. Supports general medical treatment, rehabilitation therapy, mental health care, chronic disease management, perioperative care, and pain management. Includes SMART goal frameworks, evidence-based interventions with minimal text citations, regulatory compliance (HIPAA), and professional formatting. Prioritizes brevity and clinical actionability.

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。

Format and structurally validate local treatment-plan documentation after clinical decisions have already been supplied and verified by authorized licensed professionals. Use for source traceability, clinician-authored intervention records, goals and checkpoints, shared-decision records, reconciliation handoffs, and release gates—not for clinical decision-making.

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。

Generates PI(E)COS structure (Population, Intervention, Comparator, Outcomes, Study Design) from Meta-analysis or study titles. Use when the user wants to extract these elements from a title.

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Evidence-based medical knowledge and research mentor grounded in the Bian Que tradition. Covers clinical reasoning, diagnostic thinking (望闻问切), pharmacology, pathology, differential diagnosis, medical literature appraisal, and the philosophy of early intervention. Trigger whenever users ask about medicine, clinical science, drugs, disease mechanisms, diagnosis, lab interpretation, treatment comparison, or health sciences. Even without explicit research framing, trigger on any topic touching disease, therapeutics, or clinical decision-making. Part of the AIPOCH Medical Research Skill Hub.

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。

Human-in-the-loop integration for LangGraph workflows with approval and intervention points

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。

Autonomously engage with complex coding tasks — multi-step planning, tool use, self-correction, and solution verification without human intervention.

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。

Structure a cohort analysis for retention, LTV, or behavioural patterns. Use when asked to run a cohort analysis, analyse retention by cohort, segment users by behaviour over time, or calculate lifetime value by acquisition period. Produces a complete cohort analysis framework with methodology, cohort definitions, retention curves, and prioritised interventions.

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 provides 1-50 concrete bad-case search queries for one Viking Search app and wants local deterministic fixes. This skill only verifies request-level fine-operation interventions against a read-only baseline scene and delivers a console-ready configuration sheet, validated payloads, and a replay script. It must not mutate scenes, apps, dictionaries, datasets, recall core parameters, or online defaults.

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。

Customer leadership advisor on CX strategy, retention and expansion, and voice-of-customer programs. Use when defining a CX strategy, scoring CX maturity, planning churn interventions, or designing a VoC program.

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。

The canonical way to run agent-relay - self-bootstrap the local broker and autonomously spawn, monitor, and coordinate a team of worker agents without human intervention. Covers infrastructure startup, agent spawning, lifecycle monitoring, message-based reading via the relay MCP, and team coordination.

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 targeted interventions that strengthen belonging and inclusion for specific classroom dynamics. Use when students feel isolated, cliques form, or new students need integrating.

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。

Generate clinical trial protocols for medical devices or drugs. This skill should be used when users say "Create a clinical trial protocol", "Generate protocol for [device/drug]", "Help me design a clinical study", "Research similar trials for [intervention]", or when developing FDA submission documentation for investigational products.

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。

Self-healing codebase system. Monitors runtime errors, test failures, and build breaks. Automatically diagnoses root cause, generates fix, validates fix, and applies if safe. Zero human intervention for routine failures.

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。

Diagnoses why your product team ships slowly and produces a velocity plan with a baseline, a bottleneck map, the top 3 interventions and a stop-doing list. Use it when you say 'we're too slow', 'how do we ship faster', 'projects keep slipping', 'too many meetings', 'approvals take forever', 'our releases are scary', 'velocity', 'cycle time', 'we hired more people and nothing got faster' or 'AI made coding fast but we still ship slowly'. Draws on 226 Lenny's Podcast guests including Farhan Thawar, Nicole Forsgren and Melissa Perri.

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。

Generate clinical trial protocols for medical devices or drugs. This skill should be used when users say "Create a clinical trial protocol", "Generate protocol for [device/drug]", "Help me design a clinical study", "Research similar trials for [intervention]", or when developing FDA submission documentation for investigational products.

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。

Query ClinicalTrials.gov API v2 for trial data. Search by condition, drug/intervention, location, sponsor, or phase; fetch details by NCT ID; filter by status; paginate; export CSV. For clinical research, patient matching, and trial portfolio 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。