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Agent Skill · 研究检索

fusion-skill-authoring(融合技能创作)

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。

fusion-skill-authoring 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为调用外部服务;许可证信息还不明确,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 12,390。

总安装 10,312周安装 434GitHub Stars 公开资料未说明下载 3,611核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

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-skill-authoring(融合技能创作)
来源仓库:https://github.com/equinor/fusion-skills
仓库路径:skills/fusion-skill-authoring
安装命令:
npx skills add https://github.com/equinor/fusion-skills --skill fusion-skill-authoring
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-skill-authoring

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

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-skill-authoring 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

来源仓库内路径:skills/fusion-skill-authoring

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/equinor/fusion-skills --skill fusion-skill-aut…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要调用外部服务,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

fusion-skill-authoring 怎么安装?

在终端执行 npx skills add https://github.com/equinor/fusion-skills --skill fusion-skill-authoring。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

fusion-skill-authoring 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

fusion-skill-authoring 需要什么权限?

权限级别是调用外部服务。该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

fusion-skill-authoring 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

fusion-skill-authoring 用的人多吗?

公开数据是累计安装 10,312 次,近一周安装 434 次,下载 3,611 次。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

fusion-skill-authoring 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
fusion-skill-authoring

融合技能创作 · 本页

10,312—调用外部服务未明确通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

Create or Modernize Skills

When to use

Use this skill when you need to create a new skill under skills/, or when an existing skill needs a material authoring refresh instead of a small copy edit.

Typical triggers:

  • "Create a skill for..."
  • "Scaffold skills/<name>/SKILL.md"
  • "Turn this workflow into a reusable skill"
  • "Improve this skill's metadata and activation cues"
  • "Make this skill easier for agents to discover and follow"
  • "Set up references/assets/helper agents for a skill"

Implicit triggers:

  • A recurring task keeps requiring the same instructions, context, or safety boundaries
  • An existing skill is valid but too vague, too long, poorly routed, or missing structure
  • The user wants a reusable workflow package rather than a one-off prompt

When not to use

Do not use this skill for:

  • Editing product or application code outside skills/
  • Tiny typo-only skill edits that do not need authoring workflow help
  • Requests better solved as docs, templates, or scripts without creating an installable skill
  • Large unrelated repository refactors
  • Destructive commands or hidden network automation

Required inputs

If required inputs are missing, ask concise targeted questions first. Use assets/follow-up-questions.md as the default question bank.

Mandatory

Collect before drafting:

  • Whether this is a new skill, an update to an existing skill, or not a skill at all
  • Target repository path and intended final skill directory
  • Base skill name in kebab-case before any prefix or namespace
  • Final skill name in kebab-case; default to custom-<base-skill-name> unless the target repository has a different naming convention
  • One-sentence purpose for the skill and the user outcome it should unlock
  • Concrete activation cues: trigger phrases, domain keywords, and anti-triggers
  • Expected output: which files, commands, or decisions the skill should produce
  • Safety boundaries and approval requirements

Conditional

Collect when relevant:

  • Naming prefix, namespace, or catalog convention if the target repository uses one
  • Skill category: capability uplift or workflow / encoded preference
  • Composition: standalone, orchestrator, or subordinate
  • Repository-specific ownership, lifecycle, or release policy if the target catalog enforces one
  • Orchestrator relationship: metadata.orchestrator for subordinates, metadata.skills for orchestrators
  • Target status (active, experimental, deprecated, archived) when the target catalog tracks lifecycle state
  • compatibility text when the skill has real environment constraints
  • MCP requirements (metadata.mcp.required / metadata.mcp.suggested) when the skill depends on specific servers
  • Whether a small agents/ helper set would sharpen scoping, review, or trigger tuning
  • Whether deterministic automation justifies a scripts/ directory

Optional

Capture if useful:

  • metadata.sponsor as backup accountability if the target catalog uses backup ownership
  • Starter assets, checklists, examples, or templates
  • Related issue follow-up if an almost-match exists and should be improved instead of duplicated

Metadata and structure constraints

Validate before writing files:

  • name: 1-64 characters, lowercase letters/numbers/hyphens only, must match the folder name, no leading/trailing hyphen, no consecutive hyphens, no XML tags, and no reserved words
  • If the target repository has no prefix or namespace convention, default new skills to custom-<base-skill-name>
  • description: non-empty, <= 1024 chars, third-person, no XML tags, states both what the skill does and when to use it

- Prefer a single-quoted YAML string with inline USE FOR: and DO NOT USE FOR: cues - Example: description: 'Drafts release notes from validated repository context. USE FOR: release summaries, changelog preparation. DO NOT USE FOR: publishing releases or editing product code.'

  • metadata.version: follow the target catalog's starting-version rule; if no local rule exists, "0.0.0" is a safe default for a new skill
  • metadata.owner: include only when the target catalog requires explicit ownership; use a stable GitHub identity or equivalent team handle
  • metadata.status: include only when the target catalog tracks lifecycle state; if used, keep it to active, experimental, deprecated, or archived
  • metadata.tags: keep tags relevant, lowercase, and kebab-case
  • metadata: use simple key/value metadata unless a relationship field explicitly needs a list or map
  • license: optional top-level field
  • compatibility: optional top-level field; only include it when the skill has real runtime, network, tool, or product constraints

Repository-specific prefix rules, ownership/lifecycle requirements, release policy, and validation commands belong in repo-local instructions or catalog docs, not in the portable skill package.

Instructions

Step 1 — Decide whether this should be a skill at all

  1. Check current coverage first:

- Inspect the existing skill catalog first, using repository tooling, directory layout, or catalog docs already available in the target environment - If an existing skill already covers the request, recommend reuse or update instead of creating a duplicate - If a skill almost matches, recommend improving that skill or opening an issue rather than creating a one-off clone

  1. Stop and do not scaffold a new skill if the request is better handled as:

- plain repository documentation, - a template/checklist with no reusable agent behavior, - a standalone script with no skill-routing value, or - a tiny copy edit to an existing skill

Step 2 — Define representative requests before drafting

Capture at least three representative requests before writing long instructions:

  • the user request or trigger phrase,
  • the behavior the skill should produce,
  • the mistake or gap the skill must prevent.

Use these requests as the acceptance criteria for the final skill. If you cannot define realistic requests, the scope is probably underspecified or not reusable enough to become a skill.

Step 3 — Classify the skill and choose the smallest valid structure

Decide the skill type up front:

  • capability uplift: packages domain knowledge, tools, or reference material the agent does not already have
  • workflow / encoded preference: packages sequencing, review gates, style rules, or mutation order the user wants repeated consistently

Decide the composition model:

  • standalone: no coordinating skill required
  • orchestrator: routes to companion skills and owns shared gates
  • subordinate: runs only under its orchestrator and should document that dependency clearly

Choose the minimum folder structure that supports the task:

  • SKILL.md always
  • references/ for long guidance, examples, tables, or platform-specific details
  • assets/ for templates, checklists, sample outputs, and static files
  • agents/ for a small number of specialized helper roles when the runtime supports skill-local agents and a second pass materially improves scoping, review, or trigger quality
  • scripts/ only when deterministic automation materially improves safety or reliability

Keep references one level deep from SKILL.md. Do not create nested reference chains that force partial reads.

Step 4 — Draft the minimum viable SKILL.md

Write the smallest useful main document first:

  • concise frontmatter with strong discovery cues
  • When to use and When not to use
  • Required inputs
  • Instructions
  • Expected output
  • Safety & constraints

Keep the draft under 300 lines. Different agent runtimes have different context window limits — SKILL.md files over 300 lines risk degradation on smaller-window runtimes and trigger CI warnings. Files over 500 lines fail CI. Move overflow to references/ early rather than trimming later.

Prefer concise, specific instructions over background explanation. Assume the agent is already capable and only add context it would not reliably infer.

Set the degree of freedom intentionally:

  • high freedom for context-dependent analysis or review work
  • medium freedom when a preferred pattern exists but adaptation is expected
  • low freedom when the workflow is fragile, safety-critical, or sequence-sensitive

Include at least one concrete example in SKILL.md or link directly to one in references/.

Step 5 — Add supporting files only when they reduce ambiguity

Move long or specialized content out of SKILL.md when it improves clarity:

  • use references/ for deep guidance, large examples, API/platform notes, or long checklists
  • use assets/ for templates and reusable artifacts the skill should point at directly
  • use agents/ for a small helper set when the runtime supports skill-local agents or subagents and the workflow benefits from a scoped second opinion
  • use scripts/ only for deterministic operations that should be executed instead of regenerated

If you add scripts:

  • document dependencies and side effects,
  • validate inputs and fail with actionable errors,
  • keep network access explicit and justified,
  • never use remote-code execution patterns such as download-and-run.

If the runtime ignores bundled helper agents, follow the same roles inline instead of skipping the evaluation step.

If the skill depends on MCP, declare the requirement in metadata.mcp and document client-specific tool naming expectations in the skill content instead of assuming all runtimes behave the same way.

Step 6 — Validate discovery, structure, and local policy

Run the validation supported by the target environment after authoring changes:

  • inventory or schema validation for the skill catalog, if available
  • repository or catalog policy checks for naming, ownership, lifecycle, or composition metadata
  • script, GraphQL, lint, or test validation when the skill touches those surfaces

If the environment has no dedicated skill tooling:

  • read SKILL.md and every directly referenced file end-to-end
  • verify that each representative request would trigger the skill for the right reason
  • verify that every referenced file path and workflow assumption still makes sense in the target repository

Use the representative requests from Step 2 to review the final result:

  • Does the description trigger on the right requests and avoid obvious false positives?
  • Can the agent locate all directly referenced files without chasing nested links?
  • Are outputs, approval gates, and safety constraints explicit?

If subagents are available, use the bundled role files when they help:

  • agents/scoper.md before drafting to decide create vs update vs not-a-skill and to choose the smallest useful structure
  • agents/devils-advocate.md during scoping and drafting to surface key concerns (moderate mode), or before drafting for a full structured interview when the user asks to be grilled or when the orchestrator detects significant ambiguity (interrogator mode)
  • agents/reviewer.md after drafting to review discovery, structure, safety, and validation evidence like a strict maintainer
  • agents/trigger-tuner.md when the main risk is weak activation cues or when choosing between two description variants

After the portable package is correct, apply any repository-specific release or versioning rules from repo-local instructions or equivalent catalog policy.

Step 7 — Report what changed and what still needs input

Return the authoring result as an explicit contract:

  • what was created or updated,
  • how the skill was classified,
  • which representative requests were used as acceptance criteria,
  • which helper agents were used, if any,
  • which validation commands ran and what they proved,
  • any unresolved questions or recommended follow-up issues.

Core behavior to preserve

  • Reuse before creation
  • Portable first, repository overlays second
  • Representative requests before long-form wordsmithing
  • Progressive disclosure instead of overloading SKILL.md
  • Explicit safety and approval gates for risky actions
  • Real validation evidence instead of assumed correctness

Optional helper agents

This skill borrows Anthropic skill-creator's pattern of bundling a small agents/ helper set, but narrows the roles to Fusion-specific scoping, review, and trigger tuning.

  • agents/scoper.md — decide whether the request should become a new skill, an update, or not a skill at all; choose the smallest folder structure that still solves the problem
  • agents/reviewer.md — review a drafted skill package against discovery, structure, safety, and validation expectations
  • agents/trigger-tuner.md — sharpen description wording and compare activation-cue variants against realistic prompts
  • agents/devils-advocate.md — always-on quality collaborator that raises key concerns during authoring (moderate mode) and runs a full structured interview when explicitly asked or when the orchestrator flags significant ambiguity (interrogator mode)

If a runtime offers no subagents, keep the same review loop inline and do not skip the agent-shaped reasoning just because the packaging is ignored.

Examples

  • User: "Create a skill for drafting incident retrospectives."

- Result: create a new workflow-oriented skill in the target catalog, define at least three retrospective authoring scenarios, scaffold SKILL.md, and add assets/ only if templates are needed.

  • User: "Improve the activation cues and structure of fusion-skill-authoring."

- Result: update the existing skill, refresh supporting references/assets, and run the target repository's validation flow.

  • User: "Add a new CLI flag to the application."

- Result: do not use this skill because the request is product-code work, not skill authoring.

Expected output

Return:

  • Created or updated file paths
  • Skill classification: new/update/not-a-skill, capability vs workflow, and standalone/orchestrator/subordinate when relevant
  • Final activation cues and anti-triggers used in the description
  • Chosen folder structure and rationale
  • At least three representative requests used as acceptance criteria
  • Which optional helper agents were used, if any
  • Validation commands run, pass/fail status, and interpretation
  • Any repository-specific overlays applied after the portable draft
  • Any follow-up actions, unresolved questions, or recommended issue links

See references/skill-template-baseline.md for the default folder structure and SKILL.md baseline template.

Validation

See references/validation-signals.md for success signals, common failure signals, and recovery steps.

Skill Readiness Checklist

Use assets/skill-readiness-checklist.md as the final-quality checklist for skill changes. Repository-specific PR requirements belong in repository instructions, not in the installable skill asset.

Safety & constraints

Never:

  • Request or expose secrets or credentials
  • Run destructive commands without explicit user confirmation
  • Invent validation results or evaluation evidence
  • Modify unrelated files outside the requested scope
  • Add hidden network access, remote-code execution patterns, or unsafe script guidance

Always:

  • Keep SKILL.md concise and move overflow to direct references
  • Make the discovery contract explicit in the description
  • Prefer deterministic validation loops over hand-wavy advice
  • Keep helper agents tightly scoped and ensure the core workflow still works when the runtime does not invoke them
  • Respect the target catalog's naming, ownership, lifecycle, and release policy instead of hard-coding one repository's defaults

平台分布

Codex

36.6%
按下载量换算1,322

Claude

28.8%
按下载量换算1,040

Cursor

18.39%
按下载量换算664

Gemini CLI

9.66%
按下载量换算349

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

调用外部服务

该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

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。目前只有一个公开来源,建议同时核对源仓库。

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