「rules」の検索結果

Formatting, structure, and execution rules for Quarto markdown documents. Trigger when: - Writing, editing, or rendering Quarto documents, reports, presentations, or websites. - Files matching the pattern **/*.qmd are in the workspace or referenced. - Tasks involve: quarto render, quarto preview, knitr, jupyter. - Prompt contains keywords: quarto, qmd, yaml header, code chunk, callout, cross-reference.

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 router for how a change legally happens in this repo — which sign-off gate fires for which kind of change, what counts as approval, how the packages-edit guard works, and how work is allowed to reach main. Use when starting ANY change and unsure which rules apply, when an Edit to packages/* gets BLOCKED, when a pipeline issue sits on status:blocked and you need to know what unblocks it, or when asked "do I need sign-off for this", "how do I get this merged", "can I deploy this", "who approves X", "why won't my approval label/reaction do anything".

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。

Create or change immutable business values with meaningful invariants, equality, or behavior, such as order numbers and money. Use when primitive values obscure domain rules; avoid wrapping every scalar.

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。

Create, extend, or fix Eloquent business models and their state transitions. Use when moving business rules out of controllers or Actions, or explicitly migrating a model into a DDD capability.

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。

Create or refactor business calculations and rules spanning multiple domain objects when no single entity or value object naturally owns the behavior. Use when assessing a growing service class.

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。

Test or fix domain rules, state transitions, value objects, and domain services directly. Use for business-rule regression tests, rejected transitions, and model behavior without HTTP.

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。

Resolve, validate, and audit manuscript integrity and applicable requirements for academic manuscripts and paper artifacts. Use when Codex needs to determine active paper rules from manuscript state, paper type, venue, submission stage, or workflow mode; lint a LaTeX paper project; assess submission readiness; create or update a paper policy profile; explain why a rule is active; or validate the paper-policy registries. Do not use as the primary skill for prose drafting, peer review, rebuttal writing, citation discovery, or figure/table creation.

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。

Distributed machine learning, data mining, and iterative HPC with Exasol. Covers end-to-end ML pipelines (DISTRIBUTE BY + SET scripts + BucketFS), per-entity federated training with partial_fit and ctx.reset(), batch inference, map-reduce ensemble training, distributed ensemble and SON algorithm for frequent itemset mining (Apriori, FP-Growth, association rules, market-basket analysis), Lua execute script orchestration for iterative algorithms (k-means, SGD, gradient descent), scikit-learn model training, parallel hyperparameter search, per-entity forecasting, anomaly detection, model lifecycle in BucketFS (pickle/joblib/ONNX versioning), GPU acceleration via CUDA SLCs (PyTorch/TensorFlow/RAPIDS), and ML-specific performance tuning (skew, OOM, multi-pass chunking).

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。

Audit a composer output against four hard checks before delivery — cite integrity, voice rules, word count, schema validity. Always invoked after the polisher (English) or humanizer-* (Arabic / Hebrew). Emits a VerificationReport Pydantic object; orchestrator branches on `overall_passed`. Target ≥0.9 score per run. Triggers automatically as the final composer sub-agent; not user-facing.

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。

Static analysis checklist for Convex backends covering 72 rules across security, performance, correctness, schema, architecture, configuration, and client-side patterns. Use when writing, reviewing, or auditing Convex code. Trigger on mentions of "convex-doctor", "health score", "static analysis", "anti-patterns", "audit convex", or before shipping backend changes.

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。

Static analysis checklist for Convex backends covering 72 rules across security, performance, correctness, schema, architecture, configuration, and client-side patterns. Use when writing, reviewing, or auditing Convex code. Trigger on mentions of "convex-doctor", "health score", "static analysis", "anti-patterns", "audit convex", or before shipping backend changes.

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。

Writing anything the fleet produces for a person to read: a README, a changelog, a commit message, a review note, a task file, a docblock, a user-facing string. Carries the WordPress Documentation Style Guide rules in `references/`. Use before drafting or rewriting prose, then run the pass that takes the machine out: `humanizer` for English, `humaniseur-fr` for French.

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。

Audit websites for SEO, performance, security, technical, content, and 15 other issue cateories with 230+ rules using the squirrelscan CLI. Returns LLM-optimized reports with health scores, broken links, meta tag analysis, and actionable recommendations. Use to discover and asses website o...

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。

Mandatory skills, conventions, and behavioral rules for Avalonia UI development using the Zafiro toolkit.

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。

Convert human-written CLAUDE.md into AI-native structured-label format. Battle-tested across 4 models. Same rules, fewer tokens, higher compliance.

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 spec with 98 rules for building CLI tools that AI agents can safely use. Covers structured JSON output, error handling, input contracts, safety guardrails, exit codes, and agent self-description.

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 project-specific AGENTS.md and companion rules by analyzing a codebase. Supports full, minimal, update, and dry-run modes with package-manager detection, monorepos, backups, managed blocks, confidence scoring, and command validation.

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。

Inter-agent communication protocol for C-suite agent teams. Defines invocation syntax, loop prevention, isolation rules, and response formats. Use when C-suite agents need to query each other, coordinate cross-functional analysis, or run board meetings with multiple agent roles.

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。

Build an evidence-backed ECC install plan for a specific repo by sorting skills, commands, rules, hooks, and extras into DAILY vs LIBRARY buckets using parallel repo-aware review passes. Use when ECC should be trimmed to what a project actually needs instead of loading the full bundle.

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。

Reference for Stripe test clock constraints and pitfalls. Use when planning or debugging test-clock workflows, when an advance or customer-attach step fails, or to check rules before building a time-simulation scenario with create_stripe_test_clock, advance_stripe_test_clock, and create_stripe_test_customers.

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。

Detects defense evasion techniques used by adversaries in endpoint logs including log tampering, timestomping, process injection, and security tool disabling. Use when investigating suspicious endpoint behavior, building detection rules for evasion tactics, or conducting threat hunting for stealthy adversary activity. Activates for requests involving evasion detection, defense evasion analysis, log tampering detection, or MITRE ATT&CK TA0005.

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。

This skill covers detecting anomalies in Modbus/TCP and Modbus RTU communications in industrial control systems. It addresses function code monitoring, register range validation, timing analysis, unauthorized client detection, and deep packet inspection for malformed Modbus frames. The skill leverages Zeek with Modbus protocol analyzers, Suricata IDS with OT rules, and custom Python-based detection using Markov chain models for normal Modbus transaction sequences.

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。

Detects early-stage ransomware indicators in network traffic before encryption begins, including initial access broker activity, command-and-control beaconing, credential harvesting, reconnaissance scanning, and staging behavior. Uses network detection tools (Zeek, Suricata, Arkime), SIEM correlation rules, and threat intelligence feeds to identify ransomware precursor patterns such as Cobalt Strike beacons, Mimikatz network signatures, and RDP brute-force attempts. Activates for requests involving pre-ransomware detection, network-based ransomware indicators, or early warning ransomware monitoring.

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。

Identifies lateral movement techniques in enterprise networks by analyzing authentication logs, network flows, SMB traffic, and RDP sessions using Zeek, Velociraptor, and SIEM correlation rules to detect attackers moving between systems.

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。