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fusion-rule-author融合规则作者

Agent Skill

用于辅助安全审计、权限检查、凭据风险、认证流程和常见漏洞排查。它适合让 Agent 梳理敏感配置、检查依赖风险、分析鉴权逻辑或生成安全复核清单。使用时不能把工具输出直接当最终结论,涉及密钥、令牌、用户数据或生产系统时,应先确认最小权限、脱敏方式和操作边界。

总安装

3,744

周安装

150

GitHub Stars

公开资料未说明

下载量

1,212
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:fusion-rule-author(融合规则作者)
来源仓库:https://github.com/equinor/fusion-skills
仓库路径:skills/fusion-rule-author
安装命令:
npx skills add https://github.com/equinor/fusion-skills --skill fusion-rule-author
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/equinor/fusion-skills --skill fusion-rule-author

简介

用于辅助安全审计与权限策略制定工作。适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

  • 适合梳理敏感操作配置、检查依赖风险或生成复核清单。
  • 不能将工具输出直接作为最终结论,需人工二次确认。
  • 涉及生产环境规则时应严格遵循最小权限原则。
  • fusion-rule-author 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

Rule Author

Canonical workflow, templates, and references for authoring AI coding assistant rules. Supports GitHub Copilot, Cursor, and Claude Code.

Internal skill. Users interact via the fusion-rules gateway, which routes to editor-specific agents that follow the workflow defined here.

Scope

This skill provides:

  • Workflow (Steps 1–7 below) — the guided authoring process
  • Templates (assets/) — starter files for each editor format
  • References (references/) — tech-stack examples

It does not provide agents. Agents live in fusion-rules/agents/ and reference this skill's assets and workflow.

Required inputs

Mandatory

  • Repository context (working directory or repo URL)

Gathered during interview

  • Tech stack (languages, frameworks, build tools)
  • Code conventions (naming, formatting, patterns)
  • Testing expectations (framework, coverage, style)
  • Documentation preferences (comments, TSDoc/JSDoc, README standards)
  • Commit and PR conventions
  • Error handling patterns
  • Security or compliance constraints
  • File/path-specific conventions that need scoped rules

Instructions

Step 1 — Assess current state

Check the repository for existing rule files:

.github/copilot-instructions.md
.github/instructions/*.instructions.md
.cursor/rules/*.md
.cursor/rules/*.mdc
.cursor/rules/**/*.md
.cursor/rules/**/*.mdc
CLAUDE.md
.claude/CLAUDE.md
.claude/rules/*.md
.claude/rules/**/*.md

Report what exists, what is missing, and whether updates or new files are needed.

Step 2 — Scan repository

Before interviewing, scan the repository for existing documentation and configuration that encodes conventions. Extract actionable directives from:

Documentation files:

  • README.md — project overview, setup instructions, tech stack
  • CONTRIBUTING.md — code style, PR workflow, commit conventions
  • AGENTS.md, CLAUDE.md — existing AI instructions
  • docs/adr/**, adr/**, docs/decisions/** — Architecture Decision Records
  • docs/**/*.md — any developer guides, onboarding docs, style guides
  • SECURITY.md — security policies and constraints
  • CODE_OF_CONDUCT.md — collaboration guidelines (rarely rule-relevant)

Configuration files:

  • package.json / pyproject.toml / *.csproj — tech stack, scripts, dependencies
  • tsconfig.json / jsconfig.json — language settings, strictness
  • biome.json / .eslintrc* / .prettierrc* / ruff.toml / .editorconfig — formatting and linting rules
  • .github/workflows/*.yml — CI checks, required validations, test commands
  • Dockerfile / docker-compose.yml — runtime environment
  • Makefile / Justfile / Taskfile.yml — build and task commands

Code patterns (sample, do not exhaustively read):

  • Entry points (src/index.*, src/main.*, app.*) — architecture patterns
  • Test files — testing framework, naming conventions, file placement
  • Directory structure — architectural boundaries, feature organization

For each source, extract:

  1. Concrete conventions that can become imperative directives
  2. Build/test/lint commands the AI should know
  3. Architecture boundaries or patterns to follow
  4. Explicit "do this / don't do that" rules

Skip:

  • Implementation details that change frequently
  • Content that restates language/framework defaults
  • Aspirational rules not enforced by CI or team practice

Present a summary of discovered conventions to the developer, organized by area, before proceeding to the interview.

Step 3 — Interview (fill gaps)

Use the scan results to skip areas already well-documented. Ask focused questions only for gaps. Cover these areas one at a time:

  1. Tech stack — languages, frameworks, runtime, package manager
  2. Code style — naming conventions, formatting rules, import ordering
  3. Architecture — project structure, key patterns (MVC, hexagonal, etc.)
  4. Testing — framework, conventions, coverage expectations
  5. Documentation — inline comments style, doc generation, README standards
  6. Git workflow — branch naming, commit message format, PR expectations
  7. Security — sensitive data handling, auth patterns, compliance rules
  8. Path-specific concerns — any directories or file types that need specialized guidance

For each area, present what the scan found and ask: "Is this accurate? Anything to add or correct?" Do not re-ask for information already captured.

For each convention that needs deeper context, use the follow-up questions in assets/creation-follow-up.md — purpose, exceptions, boundaries, voice.

Step 4 — Classify guidance

Separate the gathered conventions into buckets:

GitHub Copilot:

BucketTarget fileWhen it activates
Always-on conventions.github/copilot-instructions.mdEvery Copilot interaction
Scoped conventions.github/instructions/<name>.instructions.mdOnly when matching files are open/referenced

Cursor:

BucketTarget fileWhen it activates
Always-on conventions.cursor/rules/<name>.mdc with alwaysApply: trueEvery Cursor Agent session
Auto-attached conventions.cursor/rules/<name>.mdc with globsWhen matching files are in context
Agent-selected conventions.cursor/rules/<name>.mdc with description onlyWhen the Agent decides it is relevant
Manual conventions.cursor/rules/<name>.mdc (no alwaysApply, no globs)Only when @-mentioned in chat

Claude Code:

BucketTarget fileWhen it activates
Always-on conventionsCLAUDE.md or .claude/CLAUDE.mdEvery Claude Code session
Scoped conventions.claude/rules/<name>.md with paths frontmatterWhen Claude reads matching files
Unconditional rule.claude/rules/<name>.md (no paths)Every session (like always-on)

Decision rule: If a convention applies to all files in the repo, it belongs in root / always-on instructions. If it applies only to specific paths or file types, create a scoped rule.

When targeting multiple editors, generate parallel files with equivalent content — do not duplicate guidance within a single editor's files.

Step 5 — Draft rule files

Generate files using the templates in assets/:

GitHub Copilot:

  • For root instructions: use assets/copilot-instructions-template.md as the starting structure
  • For scoped instructions: use assets/scoped-rule-template.md and fill in the correct applyTo glob pattern

Cursor:

  • For always-on or scoped rules: use assets/cursor-rule-template.mdc and set frontmatter accordingly

Claude Code:

  • For project instructions: use assets/claude-rule-template.md as the starting structure
  • For scoped rules: place in .claude/rules/ with paths frontmatter

Quality rules (enforced during drafting):

  • Keep instructions concise — aim for actionable directives, not explanations
  • Use imperative voice ("Use camelCase for variables", not "Variables should use camelCase")
  • Avoid duplicating guidance between root and scoped files
  • Validate applyTo / globs / paths glob patterns match the intended files
  • Warn if total root instructions exceed ~80 lines (risk of context dilution)
  • Warn if a scoped instruction file exceeds ~50 lines (GitHub Copilot), ~500 lines (Cursor), or ~200 lines (Claude Code CLAUDE.md)
  • See references/examples.md for concrete good and bad examples
  • See assets/quality-checklist.md for the full checklist

Step 6 — Review and refine

Present the drafted files to the developer for review. For each file:

  1. Show the full content
  2. Highlight any quality warnings (length, broad globs, duplication)
  3. Ask for approval or edits

Step 7 — Write files

After approval, write the rule files to the repository. Create the .github/instructions/, .cursor/rules/, and/or .claude/rules/ directories as needed.

Confirm the final file list and paths before writing.

Expected output

  • .github/copilot-instructions.md — root instructions file (created or updated)
  • .github/instructions/*.instructions.md — zero or more scoped instruction files
  • .cursor/rules/*.mdc — zero or more Cursor rule files (when Cursor is targeted)
  • CLAUDE.md or .claude/CLAUDE.md — project instructions (when Claude Code is targeted)
  • .claude/rules/*.md — zero or more Claude Code scoped rule files
  • Summary of what was created/updated and why

Instructions vs skills vs rules — when to use which

NeedGitHub CopilotCursorClaude Code
Always-on coding conventionscopilot-instructions.md.cursor/rules/*.mdc with alwaysApply: trueCLAUDE.md
File/path-specific guidance.github/instructions/*.instructions.md.cursor/rules/*.mdc with globs.claude/rules/*.md with paths
Task-specific multi-step workflowsA skill (SKILL.md).cursor/rules/*.mdc (manual or agent-selected)Skills / subagents
Agent routing and orchestrationAgent definitions (.agent.md).cursor/rules/*.mdc with descriptionSubagent configs
Simple project-wide instructionscopilot-instructions.mdAGENTS.mdCLAUDE.md

Instructions and rules shape how the AI writes code. Skills define what it can do as structured tasks.

Safety and constraints

Never:

  • Embed secrets, tokens, or credentials in rule files
  • Generate rules that contradict repository security policies
  • Overwrite existing files without showing the diff and getting approval
  • Invent conventions — only document what the developer confirms

Always:

  • Show drafts before writing any files
  • Validate glob patterns against actual repository paths
  • Warn on overly broad globs (e.g., **/* captures everything)
  • Keep instructions concise and actionable
  • Preserve existing content when updating (append or merge, never replace silently)

References

  • references/examples.md — concrete examples for different tech stacks
  • assets/creation-follow-up.md — per-rule follow-up questions (purpose, exceptions, boundaries, voice)
  • assets/frontmatter-scenarios.md — scenario-based frontmatter guide for GitHub Copilot, Cursor, and Claude Code
  • assets/copilot-instructions-template.md — starter template for root instructions
  • assets/scoped-rule-template.md — starter template for scoped rules
  • assets/cursor-rule-template.mdc — starter template for Cursor rules
  • assets/claude-rule-template.md — starter template for Claude Code rules
  • assets/quality-checklist.md — quality review checklist

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

34.11%
按下载量换算413

Claude

30.18%
按下载量换算366

Cursor

19.72%
按下载量换算239

Gemini CLI

9.44%
按下载量换算114

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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