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Add Product E2e Eval

OrganizationPopular
paperclipai
add-product-e2e-eval

Add or extend a Paperclip full-stack runner E2E workflow, fixture, matcher, or report evidence path for local or Daytona execution.

Overview

Publisherpaperclipai
Repositorypaperclip
Skill nameadd-product-e2e-eval
Stars
95.4K
Forks
16.2K
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by paperclipai on GitHub. Read the source before you install it.

Installation

Install the Add Product E2e Eval AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/paperclipai/paperclip/tree/master/.agents/skills/add-product-e2e-eval
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/paperclipai/paperclip.git /tmp/paperclip
mkdir -p .claude/skills
cp -r /tmp/paperclip/.agents/skills/add-product-e2e-eval .claude/skills/add-product-e2e-eval
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Add Product E2e Eval in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Add Product E2e Eval on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Add Product E2e Eval is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Add a Product E2E Eval

Use this skill for Product E2E Evals: real Chromium, Paperclip server, database, runner, provider, and optionally Daytona. Everyday Workflows are in this family even when their packaged results are imported into Evalbook. Runner protocol cases against the mock control plane belong in add-runner-eval.

Locate the repository using PAPERCLIP_ROOT when supplied, or git rev-parse --show-toplevel from a checkout. From outside Git, inspect workspace roots such as ~/paperclipai/paperclip; verify the selected root contains tests/runner-e2e and packages/paperclip-runner. Run commands from that repository root. The copied skill may live outside the checkout. Read doc/evals.md, then the authoritative tests/runner-e2e/README.md, FIXTURES.md, SECURITY.md, and EVERYDAY-WORKFLOWS.md for the selected area. Inspect the nearest existing catalog entry, case, harness flow, matcher, evidence writer, and report test before changing anything. Keep the user journey on production browser/API surfaces; do not add private runner hooks or direct fixture database writes.

Define one bounded workflow with a clear user outcome, durable state assertions, and independent evidence. Declare profile, environment, expected provider turns, timeout, cleanup, screenshots, artifact checks, and billing scope. Keep credentials and secrets out of catalog data, screenshots, fixture metadata, logs, and tracked files. Daytona images must be immutable digest references. Use the existing result validator, failure classifier, screenshot policy, and Product E2E report pipeline rather than duplicating them.

Register the workflow in tests/runner-e2e/catalog.ts and the relevant fixture-registry.ts paths. Everyday cases and actions live in everyday-cases.ts and everyday-flow.ts; match the existing suite's structure. Wire its profile/environment/case IDs through existing selector and matcher tables, and add report/catalog coverage tests where the surrounding suite does so. Calibrate new grading assertions with a valid outcome and a plausible wrong outcome; missing evidence must not produce a pass. Confirm discovery before running it:

sh
pnpm test:e2e:runner -- --list --suite <suite-name>
pnpm test:e2e:runner -- --list --suite everyday-workflows

--all intentionally excludes the manual everyday-workflows suite and other explicit-only cells. Use the exact suite or execution ID for those. The Product E2E generator is pnpm test:e2e:runner:report; an Everyday Workflows result may also be imported into Evalbook with its canonical importer, but it remains a Product E2E run and should use its packaged dashboard/report first.

Run credential-free checks first:

sh
pnpm test:e2e:runner:typecheck
pnpm test:e2e:runner:unit
pnpm test:e2e:runner -- --list

For an authorized live check, use the smallest explicit local --id selector. Select Daytona only with the configured immutable image and credentials. A full --all campaign is paid and is for the governed workflow. Verify results from the packaged attempt evidence and dashboard; passing model-authored tests cannot override the independent oracle.

Classify a completed wrong workflow as product or model/provider behavior as the assertions warrant. Startup, transport, and timeout symptoms require evidence-based attribution: they may indicate a Paperclip/Runner product bug, provider behavior, or infrastructure. Preserve the observed failure and cause separately, keep the existing machine grade/classifier unchanged, and retain partial attempts, retries, source SHA, catalog/definition digest, model/profile, environment, grader version, timing, tokens, runtime estimates, and cost coverage. Do not claim qualification from a partial/manual selection.

Update README.md, FIXTURES.md, SECURITY.md, or EVERYDAY-WORKFLOWS.md when their authoritative contract changes, and link from doc/evals.md. Do not recreate Evalbook HTML or publish raw trusted traces, videos, archives, databases, workspaces, credentials, SVG, provider session IDs, or hidden reasoning. Public sanitized fixture conversation, marked screenshots, and allowlisted structured evidence are expected when the existing publisher permits them.

Frequently asked questions

What does the Add Product E2e Eval AI skill do?

Add or extend a Paperclip full-stack runner E2E workflow, fixture, matcher, or report evidence path for local or Daytona execution.

Why use Add Product E2e Eval on TypingMind?

Because you install it once and use it with any model. Add Product E2e Eval is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Add Product E2e Eval in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/paperclipai/paperclip/tree/master/.agents/skills/add-product-e2e-eval. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Add Product E2e Eval?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Add Product E2e Eval?

As many as you like. As long as a model supports skills, you can use Add Product E2e Eval with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Add Product E2e Eval AI skill free?

Yes. It is published on GitHub by paperclipai under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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