'intervention' 검색 결과

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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Analyze customer health signals, identify churn risk flags, and generate an intervention playbook. Use when user says "churn risk", "customer at risk", "red account", "at-risk customer", "health score dropped", "renewal at risk", "customer going dark", "cancel risk", or "what's wrong with this account" - even if they don't say "churn" explicitly. Applies to CSMs triaging accounts or building proactive retention workflows.

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Audit rehabilitation setback prediction systems for clinical risk modeling and early intervention. Triggers: you need to evaluate risk factor models (Charlson, Elixhauser), early warning indicators for functional decline.

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Audit a student information system for dropout risk prediction. Analyzes attendance patterns, grade trajectories, behavioral indicators, early warning model accuracy, intervention tracking, equity in risk scoring, and socioeconomic factor integration..

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Analyze mental health care planning systems for treatment goal alignment, intervention scheduling, progress milestone tracking, multi-provider coordination, step-down/step-up criteria, and individualized care pathway optimization..

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Aggregate friction log files across a time period to identify recurring patterns, classify each cluster by intervention type, produce a prioritized action plan with success criteria, and calculate convergence metrics. Complements `skmtc-retro` (which captures per-session signal) by acting as the system's actuator: turning accumulated observations into decisions. The primary output is a review document that makes the "is it getting better?" question answerable. Use this skill when the user asks to "review retros", "review friction", "run a retro review", "what's the pattern across sessions", "what should we fix next", or on a periodic cadence (monthly, pre-release). Also run after a cluster of sessions on the same feature area to surface systemic issues before they compound. Distinct from `skmtc-retro` — that skill captures per-session observations; this skill synthesizes them into decisions. Do not run this skill as a substitute for a per-session retro.

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Handle mental health crisis situations in AI coaching safely. Use when implementing crisis detection, safety protocols, emergency escalation, or suicide prevention features. Activates for crisis keywords, safety planning, hotline integration, and risk assessment.

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Expert in cluttering -- the lesser-known fluency disorder characterized by rapid/irregular speech rate, excessive disfluencies, and frequent ADHD comorbidity. Covers differential diagnosis from stuttering, assessment protocols, therapy approaches (rate control, self-monitoring, pragmatic awareness), technology-assisted intervention, and AI tools for people who clutter. Activate on 'cluttering', 'rapid speech disorder', 'speech rate too fast', 'speech disfluency not stuttering', 'disorganized speech', 'ADHD speech problems', 'cluttering vs stuttering', 'speech intelligibility', 'mazed speech', 'telescoping words'. NOT for stuttering-only treatment (overlap is common but they are distinct), hearing disorders, or voice disorders (pitch, resonance).

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Design randomized controlled trials for causal inference. Use when user mentions: randomized evaluation, RCT, field experiment, randomized experiment, treatment assignment, causal impact, experimental design, control group, intervention evaluation.

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Call this skill when faced with "wicked problems", complex challenges, or when a user asks for strategic innovation, root-cause analysis, or long-term problem solving. Do not use for simple, linear tasks. This skill forces the agent to bypass superficial symptoms, map the entire ecosystem, and anticipate second-order consequences before proposing an intervention.

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This skill recommends against initiating puberty blocking or gender-affirming hormone treatment in prepubertal children diagnosed with gender dysphoria or gender incongruence. Clinical triggers include a young child presenting with GD/gender incongruence and caregivers or clinicians considering hormone intervention.

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Recommends additional treatments for patients with persistent overt hypercortisolism after initial surgical intervention for Cushing's syndrome. Trigger phrases include "post-op CS patient shows ongoing hypercortisolism," "persistent overt hypercortisolism after TSS," and "elevated UFC or midnight salivary cortisol following surgery."

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This skill recommends re-evaluating the need for treatment of other pituitary hormone deficiencies in the postoperative period following surgical intervention for Cushing's syndrome. It is triggered when assessing a post-op CS patient for ongoing pituitary function, evaluating for hypopituitarism, or checking HPA axis recovery with morning cortisol/ACTH stimulation tests and pituitary MRI within 1–3 months.

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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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Before improving anything, show the spread of what can concretely be done on this specific input — a short menu of 3-5 interventions from lightest to heaviest touch, then wait for the user to pick. Domain-agnostic; works on any material the user hands over. Trigger when the user says "spread", "show me my options", "what can you do with this", or asks to improve or fix something without specifying how. Do NOT trigger when the user has already named a specific fix — then just do the fix.

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当用户询问 ClinicalTrials.gov / 临床试验 / NCT / 试验注册 / 招募状态 / phase 1/2/3 / RCT lookup / sponsor 检索 / interventional trial / observational study / 主次要终点 / trial outcomes / 试验招募中 / recruiting status 时,通过 ClinicalTrials.gov API v2 实时检索官方注册数据。专注实时单次查询(≤1000 条/请求),典型场景: "某 NCT 试验最新状态"、"BTK 抑制剂 Phase 3 在招募试验"、"某试验已发布的主次要终点结果"。明确边界: 批量 >10k 试验 / 全库 SQL 历史分析 → aact-bulk-trials; 关联 PubMed 文献 → pubmed-eutils; RCT 自动 GRADE 评级 → medical-evidence-grading; 引用入报告附录 → evidence-appendix-sync。跨平台无锁(Claude Code / Codex / Cursor 通用)。

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Use when turning expert materials, execution traces, repository evidence, or long-form documents into an inspectable, correctable, and empirically verified agent skill. Covers source governance, capability-vs-behavior separation, contrastive induction from successes and failures, intervention testing, versioning, rollback, and task-conditioned vs. reusable-library evaluation.

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First-principles strategy — strip away conventional assumptions and find the theoretical maximum for your business. Three modes. Diagnostic mode runs a full session (45+ min) to define your theoretical maximum, map the gap honestly, classify constraints, invert for failure modes, set strategic intent, and work backwards to your first intervention. Review mode (15-20 min) checks whether your theoretical maximum has shifted, whether the gap has changed, and whether you're still climbing the right peak. Alert mode defines constraint-shift, peak-shift, and local-peak triggers that tell you when to reassess. Use when someone is stuck optimising incrementally, when conventional wisdom is driving strategy, when a new technology or regulation changes what's physically possible, or when you suspect the business is climbing a local peak. Integrates with company-context.md. Based on practice by Eterdis (eterdis.com).

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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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Structured 6-step procedure for improving, renovating, or rebuilding existing pipelines, individual project folders, documentation structures, or software stacks. Addressable as "pipeline optimizer" (for whole topic pipelines, e.g. a software, research, or game-dev pipeline) or "project-folder optimizer" (for individual project folders within a pipeline, e.g. a single software tool or paper project). Triggers on tasks like "improve pipeline X", "optimize the stack", "rebuild Y", "renovation", "pipeline refactoring", "clean up project folder", "improve folder structure", "unify conventions", "documentation consolidation", "integrate into existing system", or any substantial intervention in established structures. Delivers building-stock analysis, purpose clarification, ideal sketch, gap plan, empirical pain-point identification, and retests with fresh subagents. Prevents parallel standards, duplication, and pipeline breaks.

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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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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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Explains ML predictions on omics data with SHAP, LIME, and permutation importance, handling the correlated-feature trap, the conditional-vs-interventional Shapley choice, and the attribution-is-not-causation boundary. Use when interpreting an omics classifier, debugging shortcut/batch learning, or deciding whether an attribution ranking can be trusted as biology. For validated feature selection see machine-learning/biomarker-discovery; explanations are not a selection method.

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