跳到正文
cccskills
免费在 GitHub 公开中文介绍

create-paperclip-bundled-skill

将想法或文章整理为Paperclip公开目录中的技能,先查找已有技能,再选择引用、改编或新建,并准备使用示例、验证、测试及拉取请求。

原文查看原文说明

Turn an idea, tweet, or task into a skill in the Paperclip skills catalog (packages/skills-catalog). Use when asked to FIND or MAKE a skill and publish it as a bundled/optional catalog skill: research prior art, reference or author it, add examples, regenerate the manifest, open a PR.

集成工具X (Twitter)
AI 评分70分/ 100查看安装方法

适用场景

  • 将想法或文章整理成技能
  • 把外部技能登记到目录
  • 注明出处并改编相似技能
  • 提交附示例和验证的拉取请求

中文介绍

功能

将想法、帖子、文章或工作请求整理成可加入Paperclip技能目录的内容。先在目录、GitHub等处搜索已有技能;找到合适的就登记为外部引用,部分适合的则注明出处后改编,没有可用技能时再新建。自行编写时,会准备SKILL.md和使用示例,重新生成目录信息,执行验证与测试,并创建拉取请求。

适用场景

适合把反复使用的操作流程整理成通过Paperclip分发的技能。也适合将外部技能加入目录,避免重复编写,或附上从输入到成品的完整示例后提交审核。

使用示例

  • “请把这篇文章中的操作流程整理成技能,加入目录。”
  • “请查找用途相同的已有技能,并登记为外部引用。”

注意事项

需要Paperclip开发环境及pnpm等工具。验证外部引用需要连接GitHub,并确认许可证允许再分发。默认类型为optional,只有明确指定时才使用bundled。不处理公司私有技能。

本介绍由 AI(OpenAI Codex)根据公开的 SKILL.md 撰写。准确的规格请查看下方原文。

客观评估

AI 仅根据 SKILL.md 的内容,从 4 个方面进行了评估。星标数和热度不计入评估。

指令清晰度能否看出何时使用、做什么
A良好明确用于向公开目录添加技能,并要求先搜索已有技能再新建。
步骤具体性步骤、输入输出和示例是否具体
B部分不足搜索命令和登记格式具体,但所提供节选没有验证和创建拉取请求的详细步骤。
安全性发送数据、删除文件、执行命令等是否有说明
B部分不足涉及从GitHub获取内容和创建拉取请求,但所提供资料没有外部写入前的确认步骤。
安装难易度所需工具和 API 密钥是否少且有说明
B部分不足提供xc可用性检查及替代方案,但没有gh等开发工具的安装步骤。

总分按每项 A=25 分、B=15 分、C=5 分相加。 由 AI(OpenAI Codex)于 2026年10月20日 评估。仅供参考,安装前请同时查看原文。

如果您认为评估有误,请申请复核

请写明哪一项有误以及原因。无需登录。我们会对照 SKILL.md 重新评估,并在此处更新结果。

此表单受 reCAPTCHA 保护,适用 Google 的隐私权政策和服务条款。

包含的文件(3)

  • SKILL.md10.6 KB
  • examples/external-reference.md2.7 KB
  • examples/new-local-skill.md3.1 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

Create a Paperclip Bundled Skill

Take source material — a tweet, a task description, a blog post, "make a skill that does X" — and land it as a skill in the Paperclip skills catalog (packages/skills-catalog/), delivered as a reviewed PR. The catalog is the shelf every Paperclip company browses and installs from, so the bar is: correct metadata, useful instructions, worked examples, and a clean validation run.

The core rule is FIND before MAKE: if a good skill already exists (in the catalog, in this repo, or published on GitHub), reference or adapt it instead of writing a duplicate from scratch.

When to use

  • A human sends a tweet/link/idea and asks for it to become a Paperclip skill.
  • A task asks to bundle an existing repo skill into the catalog.
  • A task asks to add an external published skill to the catalog.

When not to use

  • The skill is company-private (belongs in that company's library via the Skills UI/API, not the shipped catalog).
  • You only need a repo-internal agent skill for working on Paperclip itself — that goes in .agents/skills/ or skills/, with no catalog machinery.

Step 0 — Capture the source material

Understand exactly what the skill should teach before writing anything.

Tweets / X links. Use the xc CLI (X API client). Paperclip engineering agent environments ship it preinstalled and pre-authenticated; it is not a tool you install or mint credentials for yourself. Check availability before relying on it:

command -v xc && xc whoami    # on PATH and authenticated? if not, use the fallback below
xc get <post-url-or-id> --json        # the post itself (conversation_id, author)
xc search 'conversation_id:<id>' --archive --json   # rest of the thread (>7 days old needs --archive)
xc user <username>                    # author context
xc search '<topic keywords>' -n 30    # related discussion

If xc is not on PATH, is unauthenticated, or the account lacks read access (the check above fails for any reason), delegate the fetch to a teammate with X/Twitter access (e.g. the Content Strategist agent) via a child issue: give them the URL and ask for full text of the post + thread

  • any linked content.

Other sources. Fetch linked articles/READMEs directly. Record the source URL — it goes in the skill body or PR description as attribution.

Distill: what is the repeatable procedure? What inputs does it take? What does "done" look like? If the source is just an aspiration ("agents should write better commit messages"), you are authoring the procedure yourself — say so in the PR.

Step 1 — FIND: search for an existing skill

Search in this order; stop when you have a clear winner.

  1. Already in the catalog? Avoid duplicates (duplicate slugs fail the build):
    grep -i '<topic>' packages/skills-catalog/generated/catalog.json
    ls packages/skills-catalog/catalog/{bundled,optional}/*/
    
  2. Already in this repo? Check .agents/skills/, skills/, and issue history (gh search issues / Paperclip board) for prior work on the topic.
  3. Published on GitHub? Skills are conventionally a directory with a SKILL.md:
    gh search code --filename SKILL.md "<topic>" --limit 20
    gh search repos "<topic> skill" --limit 20
    
    Also check known collections (e.g. anthropics/skills) and do a web search for <topic> agent skill SKILL.md.

Judge candidates by: does the SKILL.md actually contain the procedure (not a stub)? Is it maintained? What does it bundle (scripts raise the trust level)? Is the license compatible with redistribution? Then pick a path:

  • Good external skill exists → add it as an external reference (Step 2A). It stays attributed to and pinned at the upstream repo.
  • Partial match → author a local skill (Step 2B) that adapts the idea; credit the source with a link in the SKILL.md body.
  • Nothing usable → author a new local skill (Step 2B).

Step 2 — Choose kind, category, and slug

  • kind: default to optional. Use bundled only when the skill should ship to every Paperclip company by default — that needs explicit human/board direction, not your judgment call.
  • category: reuse an existing directory when one fits (browser, content, docs, finance, paperclip-operations, product, quality, research, software-development). New categories are allowed but must be lowercase kebab-case slugs.
  • slug: lowercase kebab-case (^[a-z0-9]+(-[a-z0-9]+)*$), unique across the whole catalog (both kinds).

The skill lives at packages/skills-catalog/catalog/<kind>/<category>/<slug>/ and its canonical key is paperclipai/<kind>/<category>/<slug>.

Step 2A — External reference path (catalog-ref.json)

The directory contains only catalog-ref.json (a directory with both catalog-ref.json and SKILL.md fails the build). The manifest builder fetches the pinned files from GitHub at build time and inventories them.

# Pin the exact commit for the chosen ref (tag or branch)
gh api repos/<owner>/<repo>/commits/<ref> --jq .sha
{
  "source": {
    "type": "github",
    "hostname": "github.com",
    "owner": "<owner>",
    "repo": "<repo>",
    "ref": "<tag-or-branch>",
    "commit": "<40-char sha from above>",
    "path": "<dir inside the repo containing SKILL.md, or ''>"
  },
  "files": ["SKILL.md", "references/**", "scripts/run.py"],
  "defaultInstall": false,
  "recommendedForRoles": ["researcher"],
  "requires": ["python3"],
  "tags": ["topic", "keywords"]
}

Rules the builder enforces:

  • files entries are exact relative paths or dir/** globs; SKILL.md must be included and must have frontmatter with name and description.
  • If the upstream frontmatter declares key/slug, they must match the catalog placement — otherwise pick a matching slug or use the local path.
  • commit must be a full 40-hex SHA; every listed file must be ≤ 1 MiB.
  • recommendedForRoles, requires, tags live in the JSON (there is no local SKILL.md to carry them).

See catalog/optional/research/last30days/catalog-ref.json for the live example, and examples/external-reference.md next to this skill.

Step 2B — Author a local catalog skill

Layout:

catalog/<kind>/<category>/<slug>/
├── SKILL.md          # required entrypoint
├── examples/         # 1–2 worked examples (Step 3)
├── references/       # optional deep-dive docs
├── scripts/          # optional — raises trust level, avoid unless needed
└── assets/           # optional templates/images

SKILL.md frontmatter (all validated by the builder):

---
name: <slug>
description: >
  40–300 chars. Routing logic, not marketing: what it does, when to use it,
  when not to.
key: paperclipai/<kind>/<category>/<slug>
recommendedForRoles:
  - engineer            # non-empty; used for staffing suggestions
tags:
  - topic               # non-empty; used for browse/search
---

Optional frontmatter: defaultInstall: true (only for skills every new company should get), requires: [node, python3, ...] for runtime deps.

Body: follow docs/guides/agent-developer/writing-a-skill.md — "When to use" / "When not to use" sections, concrete commands over prose, supporting detail in references/. If the skill came from a tweet or external source, link it in the body for attribution.

Trust level is derived from files, not declared: any scripts/ file makes the skill scripts_executables (install becomes audit-gated and you must extend the scriptBearing expectation in src/shipped-catalog.test.ts); assets/ or non-markdown files make it assets; markdown-only skills stay markdown_only. Prefer markdown-only.

Step 3 — Write 1–2 worked examples

Create examples/ inside the skill directory with one or two markdown files, each a complete input → application → output walkthrough (realistic input, the skill's steps applied, the finished artifact). These ship with the skill so installers can judge it before running it, and they keep the trust level at markdown_only because they are .md files.

Name them by scenario, e.g. examples/rewrite-release-note.md.

Step 4 — Regenerate the manifest and update tests

Never hand-edit generated/catalog.json; it is deterministic build output.

pnpm --filter @paperclipai/skills-catalog build:manifest   # regenerates generated/catalog.json
pnpm --filter @paperclipai/skills-catalog validate         # must report no errors

(External references need network access to GitHub during these steps.)

Then update packages/skills-catalog/src/shipped-catalog.test.ts:

  • add the new key to EXPECTED_BUNDLED_KEYS or EXPECTED_OPTIONAL_KEYS (alphabetical order);
  • if the skill bears scripts, add it to the scriptBearing expectation.
pnpm --filter @paperclipai/skills-catalog test

The test suite also enforces the ≤300-char frontmatter description budget across the repo and the ≥40-char description / non-empty roles+tags rules for every catalog skill.

Step 5 — Open the PR

Follow the prepare-paperclip-pr skill (.agents/skills/prepare-paperclip-pr/) against paperclipai/paperclip master. The diff should contain exactly:

  1. the new skill directory (SKILL.md + examples/ + supporting files, or catalog-ref.json),
  2. the regenerated generated/catalog.json,
  3. the shipped-catalog.test.ts expectation update.

In the PR body: link the source material (tweet URL, upstream repo), state whether this is a new skill / adaptation / external reference, and note the trust level. Reference PR #10410 (simplified-english) as the shape of a minimal optional-skill PR.

Gotchas

  • generated/catalog.json staleness is a validation error — always rerun build:manifest after any file change inside the skill directory (the inventory carries per-file sha256 hashes).
  • Duplicate slug across bundled and optional fails the build, not just duplicate keys.
  • Symlinks inside a skill directory must resolve within it; directory symlinks are rejected — copy files in.
  • The bundled kind and defaultInstall are independent axes; don't set defaultInstall: true casually on optional skills.
  • For external references the builder fetches from GitHub on every manifest build; a moved/deleted upstream breaks the build, which is why commit is pinned — prefer upstream tags for ref.

评价

还没有评价。欢迎分享你的使用感受。

评论

无需登录即可发表。欢迎分享使用感受、提问,或说明在哪些环境下无法运行。

还没有评论。来写第一条吧。

提交后会立即公开。请勿填写邮箱、电话等个人信息。URL 最多 2 个。

0 / 1000 字

此表单受 reCAPTCHA 保护,适用 Google 的隐私权政策和服务条款。

同一仓库的技能

add-product-e2e-eval

免费有简介AI 评分 90分

为Paperclip新增或扩展贯穿真实浏览器、服务器、数据库和runner的端到端评估,整理测试数据、判定条件与证据,并在现有报告中查看操作结果。

  • 新增完整用户操作流程的评估
  • 更新测试数据与判定条件
  • 在评估报告中加入截图和产物
paperclipai/paperclip10.6万2026年10月22日 更新

add-runner-eval

免费有简介AI 评分 90分

为Paperclip Runner添加或扩展行为评估,定义判定条件、执行目标和报告验证数据,利用正常及异常证据验证结果,并保留执行记录与费用。

  • 添加Runner行为评估
  • 用有效与无效证据检验判定
  • 更新执行列表和持续评估
paperclipai/paperclip10.6万2026年10月22日 更新

agent-browser

免费有简介AI 评分 90分

通过实际浏览器操作网页和应用,检查界面显示与运行情况。结合截图、简单表单填写、错误及网络请求记录,帮助复现问题并确认操作结果。

  • 检查界面修改后的显示效果
  • 读取由JavaScript渲染的内容
  • 复现界面问题并记录错误
paperclipai/paperclip10.6万2026年10月22日 更新

agentmail

免费有简介AI 评分 100分

通过 Paperclip 分配的 AgentMail 收件箱,查看任务关联邮件、发送新邮件或回复,并核查投递结果;重试时保留相同标识,避免重复发送。

  • 查看所负责任务关联的邮件
  • 从分配的收件箱发送新邮件
  • 回复会话中的指定邮件
paperclipai/paperclip10.6万2026年10月22日 更新

check-pr

免费有简介AI 评分 80分

检查GitHub、GitLab和Perforce变更中的评审意见、自动检查失败及说明缺漏,整理待办事项,并按请求协助修改和解决已处理的讨论。

  • 提交变更前进行检查
  • 确认失败的检查和待修改项
  • 整理人工及机器人评审意见
paperclipai/paperclip10.6万2026年10月22日 更新

company-creator

免费有简介AI 评分 100分

根据构想或现有代码仓库,梳理AI智能体的职责、汇报关系与工作交接,生成符合规范的公司或团队文件包,供组织协作和导入Paperclip使用。

  • 从构想建立智能体公司
  • 将代码仓库整理为团队
  • 设计职责分工与工作交接
paperclipai/paperclip10.6万2026年10月22日 更新

查看 paperclipai 的全部技能

报告此技能的问题