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Found 8 Skills
Constitution / Operating Model / Practice / Skillがorganizational outcomeとinvariantを維持するかを、formal model・deterministic checks・cold eval・grader controls・context budgetで検証する時に使用する。
Verified corrections for IAM behaviors that AI agents frequently get wrong — policy evaluation edge cases, trust policy gotchas, STS session limits, Organizations quirks, and SAML/MFA specifics. Use alongside documentation when working with IAM roles, policies, STS, or Organizations. Do NOT use for non-IAM authorization like Cognito user-pool policies or app-level RBAC.
Analyzes events through economic lens using supply/demand, incentive structures, market dynamics, and multiple schools of economic thought (Classical, Keynesian, Austrian, Behavioral). Provides insights on market impacts, resource allocation, policy implications, and distributional effects. Use when: Economic events, policy changes, market shifts, financial crises, regulatory decisions. Evaluates: Incentives, efficiency, opportunity costs, market failures, systemic risks.
Reasoning skill for prior authorization clinical policy evaluation. Use when the user asks about payer clinical criteria, step therapy requirements, medical necessity definitions, CMS LCD/NCD coverage rules, appeals documentation strategy, formulary tier implications, or FHIR Da Vinci PAS implementation guidance. Triggers include "prior auth policy", "step therapy", "medical necessity", "coverage determination", "LCD", "NCD", "formulary tier", "PA appeal", "peer-to-peer review", "Da Vinci PAS", "clinical criteria", "PA denial", "drug authorization", "utilization management".
Conduct structured policy analysis including problem definition, alternative evaluation, and evidence-based recommendation. Use this skill when the user needs to evaluate policy options, compare interventions, assess regulatory impact, or make public sector recommendations — even if they say 'which policy should we adopt', 'what's the best approach to this public problem', or 'evaluate these policy alternatives'.
Benchmarks a trained Physical AI Studio policy in a simulation gym and reports success metrics. Use when running physicalai benchmark, editing configs under library/configs/benchmark, adding or changing a Benchmark class in physicalai.benchmark, tuning rollout/episode/env settings, recording rollout videos, or interpreting results.json / results.csv.
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'.
Run the root-level workflow runtime policy or rule-based rollouts and verify simulator success. Use for evaluation, checkpoints, or local controllers; do not use for replay or dataset annotation.