'conform' 검색 결과

Codified expertise for quality control, non-conformance investigation, root cause analysis, corrective action, and supplier quality management in regulated manufacturing.

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実装差分が plan / todo / test-cases アーティファクトの方針と一致しているかを検査し、逸脱・漏れ・意図外変更を検知する

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新実装が、同種の先行実装（同レイヤ・同責務・同概念）に在る防御 / バリデーション / エラー処理 / 規約 / 共通化ロジック / 概念定義を、欠落・重複・食い違いなく継承しているかを grep で先行特定して確認する

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リポジトリに DESIGN.md やデザイントークン定義が存在する場合に、新規 UI 実装の色・余白・フォントサイズ・角丸・シャドウがその定義済みスケールに準拠しているかを照合する。定義が無ければ実行しない

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Use when working with Aws Config Deep — aWS Config deep rule and compliance management. Covers Config rules, conformance packs, aggregators, advanced queries, remediation actions, configuration history, and multi-account compliance dashboards. Use when auditing AWS resource compliance, managing Config rules, reviewing conformance pack results, or querying resource configurations across accounts.

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Implement comprehensive API security testing using the 42Crunch platform to perform static audit and dynamic conformance scanning of OpenAPI specifications.

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(core) route header and body-height conformance via PageHeader. Use when fixing a (core) page's header, title bar, breadcrumbs, sub-route nav, body height (h-page, calc(100dvh…)), mobile header, or buttons hidden behind the avatar, or on a header-conformance assignment. NOT for /administration/* routes.

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Create an AI agent company or team package from an idea or an existing repository. Define roles, reporting lines, skills, and work handoffs, then generate structured files that follow the Agent Companies specification.

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• Build an agent company from an idea
• Turn a repository into an agent team package
• Define agent roles and work handoffs

Study Model Context Protocol (MCP), which connects AI applications with external tools, through guided lessons and exercises. Inspect requests and responses, practice implementation and security checks, and record progress alongside observed results.

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• Learn MCP fundamentals through requests and responses
• Practice building MCP clients and servers
• Study authorization, security, and operational failures

Run molecular property predictions, explore molecular shapes, and estimate how compounds bind to proteins through a cloud Python API. Process compound batches and connect modeling steps without maintaining local high-performance computing infrastructure.

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• Predicting properties across a compound library
• Exploring molecular shapes and tautomeric forms
• Estimating how compounds bind to proteins

Generate candidate positions and orientations for small molecules binding to proteins with DiffDock and DiffDock-L. Prepare single or batch docking runs, organize predicted poses by model confidence, and review validation needs without treating confidence as binding affinity.

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• Predicting binding poses for a protein and small molecule
• Preparing and validating batch docking inputs
• Comparing poses across receptor conformations

Turn work history, education, and skills into a complete resume for Reactive Resume. Gather missing facts through conversation and produce JSON, a structured data format, with the chosen template and layout.

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• Create a new resume through conversation
• Convert work history into Reactive Resume JSON
• Choose a resume template and section order

Validates skill files and registration consistency, then checks project tests and system health. Compares failures with recent changes to distinguish regressions, outdated tests, intermittent failures, and infrastructure issues, producing a report that tracks results over time.

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• Validating definitions and registration for new skills
• Running daily tests and system health checks
• Classifying failures after code changes

Plan and conduct internal audits for medical device quality management systems under ISO 13485. Organize evidence, classify findings, verify corrective and preventive actions, and prepare for external audits with structured checklists and risk-based scheduling.

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• Plan an annual internal audit schedule
• Document audit evidence and classify findings
• Verify the effectiveness of corrective actions

Review an AI system's EU AI Act readiness through six structured questions. Produce an article-cited assessment covering risk classification, conformity checks, organizational obligations, transparency, and general-purpose models, with gaps and three prioritized actions.

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• Reviewing a new or materially changed AI system
• Checking readiness before an EU launch
• Refreshing an annual compliance assessment

Turn a founder’s business vision into linked Markdown files through a phased interview covering strategy, finance, sales, operations, and governance. Build a company knowledge bundle that people and AI agents can read, with progress tracking and decision history.

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• Structure a new business through phased interviews
• Build a company knowledge base for AI agents
• Document decisions and track planning progress

Import a custom pedestrian into CARLA on UE 5.8, connect it to the shared animation skeleton, and verify that it moves. Export an existing walker as an FBX starting point or investigate a walker that stands still.

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• Add a custom pedestrian to CARLA
• Export an existing walker as an FBX for editing
• Investigate a walker that spawns but does not move

Create a new skill for a Hive agent, with a clear SKILL.md, supporting files, and an appropriate storage scope. Guides validation and discovery checks so the agent can find and use the skill.

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• Create a skill for project-specific procedures
• Package a reusable skill for multiple projects
• Keep a skill limited to one colony

Review smart contract security through five steps covering Slither scans, special features, diagrams, documented safety conditions, and manual checks. Get project-specific findings, testing setup guidance, and a prioritized action plan for development or deployment reviews.

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• Review smart contract security before deployment
• Triage Slither findings and false positives
• Visualize inheritance and access permissions

Coordinate CAD and source assets through conversion to USD, a 3D scene format, material and physics assignment, and SimReady checks. Produce stage reports, with preview rendering and validated delivery packages when requested.

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• Prepare CAD assets for simulation
• Convert source assets to USD and check basic validity
• Assign materials and physics properties

Helps users generate, edit, and validate mirrord.json configuration files for mirrord (MetalBear). Use when the user wants to connect their local process to a Kubernetes environment, configure features (env/fs/network), or needs feedback on an existing mirrord.json. Always ensures output JSON is valid and schema-conformant.

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Classical end-to-end empirical analysis workflow in the traditional Stata ecosystem — native Stata + reghdfe + ivreg2 + csdid + did_imputation + eventstudyinteract + sdid + rdrobust + rddensity + synth + synth_runner + psmatch2 + teffects + ebalance + coefplot + esttab + asdoc + binscatter. **Defaults to economics empirical-paper style** (AER / QJE / AEJ) — every run produces a publication-ready output set with a multi-column regression table (M1→M6 progressive controls/FE) as the centerpiece, plus Table 1 (descriptives), mechanism / heterogeneity / robustness tables, and event-study + coefficient + trend figures. Covers the full 8-step Stata pipeline an applied economist runs on every paper — (1) data import & cleaning (use/import, destring, misstable, duplicates, merge assert), (2) variable construction (gen/egen/winsor2/xtile/xtset with L./F./D.), (3) descriptive statistics & Table 1 (tabstat/balancetable/asdoc), (4) classical diagnostic tests (sktest/swilk/hettest/imtest/xtserial/xttest3/vif/dfuller/kpss/hausman/estat overid), (5) baseline modeling (reg/xtreg/reghdfe/ivreg2/ivregress/csdid/did_imputation/eventstudyinteract/sdid/rdrobust/synth/psmatch2/teffects/heckman/qreg/ppmlhdfe), (6) robustness battery (bacondecomp/honestdid/rwolf/ritest/wildbootstrap/oster), (7) further analysis (subgroup/triple-diff/interactions/medsem/marginsplot/binscatter by group), (8) publication-ready tables & figures (esttab/outreg2/estout/coefplot/marginsplot/rdplot/twoway combined). **Also covers two parallel domain modes that share the same 8-step scaffolding** — **Mode A — Epidemiology / public health** (target-trial emulation, IPTW + g-formula + TMLE doubly-robust triplet via `teffects ipw` / `teffects ipwra` / `teffects aipw` / `eltmle`, Mendelian randomization via `mrrobust` (IVW / Egger / weighted median) and `mregger` / `mrpresso`, KM / Cox / AFT / RMST survival via `sts` / `stcox` / `streg` / `strmst2`, E-value sensitivity via `evalue` (Linden-Mathur), principal stratification — STROBE / TRIPOD reporting), and **Mode B — ML causal inference** (DML via `ddml` / `pdslasso`, S/T/X/R/DR meta-learners via `crforest` and `ddml interactive`, causal forest via `crforest` / `cforest`, BART/BCF via `bart` / `bartCause`-style externals, CATE distribution + policy tree via `crforest`, off-policy evaluation, conformal causal externals, fairness audit, DAG learning via `pcalg` / external Python callouts). Use when the user asks for a complete Stata empirical analysis, wants a reproducible .do-file pipeline, needs a Stata counterpart to the Python StatsPAI / Full-empirical-analysis-skill, or names a specific Stata step in isolation ("run reghdfe with two-way clustering", "csdid event study", "winsor2 at 1%", "esttab to LaTeX", "coefplot with CI", "ivreg2 weak-IV test", "synth_runner placebos", "teffects psmatch balance check"). Mode A triggers on "target trial emulation Stata", "teffects ipw aipw", "eltmle", "mrrobust", "mregger weighted median", "stcox AFT survival", "strmst2", "evalue Stata", "STROBE Stata", "公共健康 Stata", "流行病学 Stata". Mode B triggers on "ddml Stata", "pdslasso", "crforest causal forest Stata", "policy tree Stata", "因果机器学习 Stata".

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Swift Concurrency review and remediation for Swift 6.2+. Use when asked to review Swift Concurrency usage, improve concurrency compliance, or fix Swift concurrency compiler errors in a feature or file. Concrete actions include adding Sendable conformance, applying @MainActor annotations, resolving actor isolation warnings, fixing data race diagnostics, and migrating completion handlers to async/await.

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Audit, design, write, materially revise, or remove Tutti tests. Use whenever Codex changes or reviews unit, component, conformance, integration, regression, platform, or repository tests; enforce a protected product contract, credible failure, correct owning boundary, negative-control evidence, overlap review, deterministic setup, and an executing CI lane.

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