「intervention」の検索結果

Critically supervise another Codex or agent session without taking it over, or audit a long, compacted or delegated session for commitments it forgot, narrowed or left unverified. Maintain executable closure, material-claim, and intervention ledgers; challenge unsupported claims before decision boundaries; correct proved errors; and stop only after accepted work and owned processes close. Use for long-running tasks, campaign watches, delegated reviews, and likely-mistake checks.

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Observe and assess a garden using a structured sensory protocol adapted from Coordinate Remote Viewing. Covers pre-entry clearing (meditate checkpoint), Stage I gestalt impression, Stage II sensory layer (leaf, stem, root, soil), Stage III pattern recognition with AOL management, and garden health triage matrix (heal checkpoint). Use before any intervention, when plants show stress symptoms, at seasonal transitions, when evaluating a new garden site, during regular health monitoring, or after extreme weather events such as frost or heat waves.

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Sustained neutral pattern recognition across systems without urgency or intervention. Maps naturalist field study methodology to AI reasoning: framing the observation target, witnessing with sustained attention, recording patterns, categorizing findings, generating hypotheses, and archiving a pattern library for future reference. Use when a system's behavior is unclear and action would be premature, when debugging an unknown root cause, when a codebase change needs its effects witnessed before further changes, or when auditing own reasoning patterns for biases or recurring errors.

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Search the ClinicalTrials.gov registry through its version 2 REST API for interventional and observational studies, their phases, enrolment, endpoints, sponsors, and posted results. Use this skill to survey who is developing what against an indication, date a competitor's programme, read primary and secondary outcome measures, find eligibility criteria, and distinguish a study that completed from one that was terminated or withdrawn. Also trigger on ClinicalTrials.gov, NCT number, trial registry, study phase, enrolment, primary outcome measure, recruiting status, trial sponsor, or competitive landscape.

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"Apply the Technology Acceptance Model (Davis, 1989) and Unified Theory of Acceptance and Use of Technology (Venkatesh et al., 2003) to predict technology adoption. Use this skill when the user needs to evaluate user acceptance of a new system, diagnose adoption barriers, design interventions to improve technology uptake, or when they ask 'why aren't users adopting this', 'what drives technology acceptance', or 'how do we increase adoption rates'.".

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"Apply the Theory of Planned Behavior to predict behavioral intentions from attitudes, subjective norms, and perceived behavioral control, and identify intervention leverage points. Use this skill when the user needs to predict adoption of a new behavior, diagnose why an intended behavior does not occur, design behavior change campaigns, or when they ask 'why do people not follow through', 'what predicts behavior change', or 'how to increase adoption rates'.".

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"Apply Self-Determination Theory to analyze motivation quality along the autonomy continuum and design interventions that satisfy basic psychological needs. Use this skill when the user needs to diagnose why intrinsic motivation is declining, evaluate incentive structures for motivational crowding, design need-supportive environments, or when they ask 'why did rewards backfire', 'how to foster intrinsic motivation', or 'what needs drive engagement'.".

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"Apply Difference-in-Differences (DID) to estimate causal treatment effects by comparing changes in outcomes between treatment and control groups. Use this skill when the user evaluates policy interventions, natural experiments, or regulatory changes, needs to test parallel trends, or when they ask 'did this policy work', 'how do I identify causal effects without randomization', or 'what is the treatment effect'.".

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"Apply supply and demand analysis to explain price determination, market equilibrium, and the effects of policy interventions. Use this skill when the user needs to analyze how prices are set in a market, predict the effect of taxes/subsidies/price controls, or understand shifts in supply or demand curves — even if they say 'why did the price go up', 'what happens if the government sets a price cap', or 'how does a tariff affect the market'.".

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"Apply action research through Plan-Act-Observe-Reflect cycles and Participatory Action Research (PAR) to generate knowledge while improving practice. Use this skill when the user needs to design practitioner research that integrates inquiry with intervention, facilitate participatory research with stakeholders, structure iterative improvement cycles, or when they ask 'how do I research my own practice', 'how do I involve participants as co-researchers', or 'how do I combine research with practical change'.".

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"Build employee turnover prediction models to identify flight risk and retention drivers. Use this skill when the user needs to predict which employees are likely to leave, identify retention risk factors, or prioritize HR interventions — even if they say 'attrition prediction', 'who is going to quit', or 'employee retention model'.".

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Build customer health scores and churn risk models using leading indicators. Define health dimensions (Usage, Engagement, Support, Sentiment, Contract), weight and score customers, segment into Healthy / At-Risk / Red, and generate intervention playbooks. Use during QBR prep, customer success reviews, or when churn spikes.

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Use when a task needs the judgment of an Elementary School Teacher — interpreting a running record or fluency check against grade-level benchmarks, deciding whether a struggling reader needs a Tier 2 intervention, running a self-contained classroom's daily schedule across multiple subjects, implementing (not writing) an IEP/504 accommodation with fidelity, or writing a parent-conference summary grounded in assessment data.

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Infectious disease modeling with SIR/SEIR models — R0, fitting, forecasting, and intervention scenarios.

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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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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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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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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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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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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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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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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.

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。