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
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。
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.
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。
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。
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。
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。
安装前可以查看交给 AI 代理的指令内容。
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.
.agents/skills/ or skills/, with no catalog machinery.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
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.
Search in this order; stop when you have a clear winner.
grep -i '<topic>' packages/skills-catalog/generated/catalog.json
ls packages/skills-catalog/catalog/{bundled,optional}/*/
.agents/skills/, skills/, and issue
history (gh search issues / Paperclip board) for prior work on the topic.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:
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.browser,
content, docs, finance, paperclip-operations, product, quality,
research, software-development). New categories are allowed but must be
lowercase kebab-case slugs.^[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>.
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.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.
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.
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.
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:
EXPECTED_BUNDLED_KEYS or EXPECTED_OPTIONAL_KEYS
(alphabetical order);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.
Follow the prepare-paperclip-pr skill (.agents/skills/prepare-paperclip-pr/)
against paperclipai/paperclip master. The diff should contain exactly:
generated/catalog.json,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.
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).slug across bundled and optional fails the build, not just
duplicate keys.bundled kind and defaultInstall are independent axes; don't set
defaultInstall: true casually on optional skills.commit is
pinned — prefer upstream tags for ref.还没有评价。欢迎分享你的使用感受。
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。
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。
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。
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。
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。
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。
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。