Results for “rules”

Understand how Codex Rules adds project instructions and file-specific rules to agent context. Check supported rule locations, when rules are applied, and environment settings for modes, sources, and text limits.

• Checking supported locations for rule files
• Understanding when file-specific rules are applied
• Configuring rule modes and text limits

Understand how Codex Rules adds project instructions and file-specific rules to agent context. Check supported rule locations, when rules are applied, and environment settings for modes, sources, and text limits.

• Checking supported locations for rule files
• Understanding when file-specific rules are applied
• Configuring rule modes and text limits

Audit Claude Code instruction rules against actual project files to find broken settings, overly broad scopes, and loading issues. Review each rule’s usefulness with the user, then apply confirmed changes or produce an audit report.

• Setting up Claude Code rules for the first time
• Investigating rules that apply too often or never
• Reviewing outdated instructions

Orchestrate the remote rules registry — the shared registry (often connected as a local folder or git repo) that the project's rules are published to and pulled from. Three modes, picked from the user's wording: create scaffolds a new local rules registry ("create a local rules registry", "init a rules registry"); update migrates/promotes local knowledge-base rules from .unikit/memory/ into the remote registry with automatic semver bumps ("migrate local rules to the remote registry", "promote my rules to the registry", "push local rules", "publish local rules", "update the rules registry"); sync pulls registry updates back into memory ("sync the rules registry", "pull registry updates"). Pass "--module <id>" to scope to one knowledge-base module. This skill ONLY orchestrates the CLI (`unikit-ai rules *`) — it never edits .unikit.json or .unikit/memory/ sources directly.

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CLAUDE.mdを分割ルールファイル(.claude/rules/)で管理するスキル。学習トライアドの書き込み先を分割ファイルにリダイレクトし、CLAUDE.mdには目次だけを生成する（本文は .claude/rules/ を Claude Code が自動で読み込む）。/rules-init, /rules-merge, /rules-add, /rules-status コマンドを提供。CLAUDE.mdが肥大化している場合や、学習トライアドの蓄積を分割管理したい場合に使用。

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Analyze TRES Finance transactions to find repeated fees, rewards, and small entries worth combining. Propose and validate daily or monthly aggregation rules, create approved rules, and list existing rules or delete pending ones to simplify ERP records.

• Consolidating frequent gas fees
• Grouping staking rewards into monthly entries
• Reviewing aggregation proposals before ERP integration

Review transaction grouping rules in TRES Finance to see how many transaction entries they would affect. Identify zero matches and overlapping rules, and compare existing rules’ new matches with the rollups they have already produced.

• Preview a rule’s matching entries before creation
• Review pending rollup requests
• Check overlaps with active rules

Add a short, project-specific rule, convention, or override to .unikit/RULES.md — the quick-capture inbox for this project's rules (common rules always load; topic files in .unikit/rules/ load when the work matches), automatically loaded by /unikit-implement (later promotable via /unikit-memory migrate-rules). Works only with a rule typed as a prompt — it does NOT read files, folders, URLs, or PDFs. Cross-checks against the knowledge base (RULES_INDEX.md) to avoid duplicating core/stack entries. Use for fast, one-line conventions and corrections: "add a rule", "remember this", "convention", "always do X", "never use Y", "from now on do Z", or when the user corrects agent behavior and wants it remembered; also "split the rules into topics" (optimise), "clean up the rules" (prune), "shorten RULES.md" (compact). If the user points to a source (file, folder, URL, PDF, article, book) or wants to research/document framework usage, use /unikit-memory; for architecture decisions use ARCHITECTURE.md.

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Create project-specific rules for turning Figma designs into code. The skill examines existing components and styling, then saves conventions for naming, file locations, reusable design values, and implementation steps in the coding agent's rule file.

• Setting up Figma implementation rules for a new project
• Teaching an AI agent existing component conventions
• Standardizing Figma implementation across a team

Build and test threat detection rules in Google SecOps using YARA-L 2.0, its rule language. The skill also checks existing coverage with simulated attack events, proposes rules for confirmed gaps, and deploys approved rules.

• Creating detection rules from known attack behavior
• Tuning existing rules to reduce false positives
• Checking detection gaps against threat reports and CVE disclosures

Learn project rules from accumulated fix patches in .unikit/code/patches/. Analyzes past mistakes, extracts prevention points, classifies each as a coding rule (→ RULES.md) or a per-skill workflow rule (→ skill-context), and proposes them for approval. Use when the user wants to turn past fixes into rules — "evolve", "evolve the rules", "learn from past mistakes", "learn from the fixes", "analyze the patches", "what rules should we add from recent fixes". Best run after several /unikit-fix sessions have left patches behind. This derives rules from accumulated patch history — to add a single rule by hand use /unikit-rules, and to promote mature RULES.md entries into the knowledge base use /unikit-memory migrate-rules.

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Create threat detection rules in Sigma, a shared format for security log searches, and convert them for Splunk, Elastic, or Microsoft Sentinel. Validate syntax, test expected matches and false positives, and map coverage to MITRE ATT&CK.

• Turning threat reports into detection rules
• Reusing detection logic across SIEM platforms
• Testing rule matches and reducing false positives

Turn recurring lessons in Claude's automatic memory into concise project rules. Choose a project-wide, file-specific, or personal rule location, include concrete commands, and clean up the original memory entry after confirmation.

• Make repeated corrections into lasting rules
• Record the project's package manager
• Apply API rules to selected file paths

Write and review YARA-X rules, which identify malware through file patterns. Refine detection strings and conditions, check rule quality with included scripts, test against clean files, improve performance, and migrate rules from legacy YARA.

• Write rules targeting a specific malware family
• Review false positives and slow detection rules
• Migrate existing YARA rules to YARA-X

DDDの集約(Aggregate)設計ルールに基づくコードレビュー・設計支援・リファクタリングを行う。 Evans Rules、Vernon's 4 Rules、Design by Contractに基づき、集約の境界定義、不変条件の検証、 不変(Immutable)設計、ID参照、結果整合性、ドメインイベント連携を包括的にガイドする。 以下のいずれかに該当する場合は必ずこのスキルを使用すること： - 集約(Aggregate)の新規設計・実装・リファクタリング（どの言語でも） - 既存の集約やエンティティクラスのDDD観点でのコードレビュー - 集約の境界決定（「AとBは同じ集約にすべきか？」「この集約は大きすぎるか？」） - 集約内の不変条件・整合性境界の設計 - 集約間の連携方式の判断（ドメインイベント、結果整合性、Sagaパターン） - 可変(Mutable)な集約コードを不変(Immutable)設計にリファクタリングする - publicフィールド、直接参照、push/appendなどカプセル化違反の検出・修正 キーワード例：集約、Aggregate、aggregate boundary、集約ルート、AggregateRoot、 エンティティ設計、DDD実装、Vernon Rules、Evans Rules、集約の分割、真の不変条件

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Extract unwritten coding conventions from an existing project and turn them into an AI style rules file. Resolve conflicting patterns with the user, identify example files, and update the rules as the codebase changes.

• Introducing an AI agent to a legacy codebase
• Documenting unwritten coding conventions
• Resolving conflicting naming and structural patterns

Create project-specific rules for turning Figma designs into code. Bring design tokens, shared values such as colors and spacing, together with naming conventions and lint rules for automated checks in one reference.

• Defining rules for implementing Figma designs
• Documenting shared design values and naming conventions
• Consolidating automated checking rules in one reference

Deploy and configure Snort 3 to detect suspicious network traffic. Create custom detection rules, tune community rules to reduce false alerts, test them against captured traffic, and forward alerts to a central security monitoring platform.

• Deploy intrusion detection at network boundaries
• Write rules for organization-specific threats
• Reduce noisy or false alerts

Evaluate whether Google SecOps rules detect threats described in articles or supplied text. Generate simulated attack logs, identify coverage gaps, summarize matching rules, and create rules for uncovered threats, with user approval before adding them.

• Evaluate detection coverage for threats described in a blog
• Test existing rules with simulated attack logs
• Review matching rules and their alerting status

[omh] Turn repeated lessons into written rules: extract repeated principles from skills, prompts, traces, reviews, and failures into reviewed rule candidates without auto-mutating guidance. Use when the user says: rules-distill, rules distill, distill rules, rule distillation, principle distill, skill principles, extract agent rules, turn traces into rules.

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Use this skill when configuring or troubleshooting Data Cloud identity resolution rulesets — matching rules, reconciliation rules, unified profiles, and cross-device identity linking. Trigger keywords: identity resolution ruleset, unified individual, match rule, reconciliation rule, unified profile, cross-device identity, Data Cloud deduplication, cluster creation. NOT for CRM duplicate and matching rules — use admin/duplicate-management. NOT for overall Data Cloud layer and activation design — use architect/data-cloud-architecture.

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Find and identify the CCSGameRules_PostCleanUp function in CS2 server binary using IDA Pro MCP. Use this skill when reverse engineering CS2 server.dll or libserver.so to locate the post-round entity-cleanup routine. It is non-virtual and has no unique string of its own, so it is found via the CCSGameRules_ResetRound predecessor: PostCleanUp is the entity-cleanup call ResetRound makes. Emits a unique IDA-style byte signature and a func YAML. Mirrors the find-CCSGameRules_PostCleanUp.py preprocessor (LLM_DECOMPILE CCSGameRules_ResetRound). Trigger: CCSGameRules_PostCleanUp, PostCleanUp, CGameRules::PostCleanUp

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Agent fallback for C_GameRules_ctor-decompiles (auto-generated, category: func). Locate C_GameRules::C_GameRules in the CS2 client module via IDA Pro MCP and emit a fresh, minimal-unique artifact. The deterministic preprocessor could not resolve this symbol on the current gamever - your job is the re-sign. Trigger: C_GameRules_ctor-decompiles, C_GameRules::C_GameRules

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Agent fallback for C_GameRules_ctor (auto-generated, category: func). Locate C_GameRules::C_GameRules in the CS2 client module via IDA Pro MCP and emit a fresh, minimal-unique artifact. The deterministic preprocessor could not resolve this symbol on the current gamever - your job is the re-sign. Trigger: C_GameRules_ctor, C_GameRules::C_GameRules

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