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Add Runner Eval

OrganizationPopular
paperclipai
add-runner-eval

Add or extend a Paperclip Runner protocol evaluation definition, roster, assertion, or report fixture with provenance and narrow validation.

Overview

Publisherpaperclipai
Repositorypaperclip
Skill nameadd-runner-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 Runner 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-runner-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-runner-eval .claude/skills/add-runner-eval
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Add Runner 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 Runner 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 Runner 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 Runner Eval

Use this skill for the Runner Evals family: a real Runner/provider session against a seeded mock control plane. Product browser/server/database/Daytona coverage belongs in add-product-e2e-eval.

Locate the Paperclip checkout using PAPERCLIP_ROOT when supplied, or git rev-parse --show-toplevel from a checkout. From outside Git, inspect the workspace roots (for example ~/paperclipai/paperclip) and verify that the selected root contains packages/paperclip-runner and tests/runner-e2e. Locate paperclip-evals using PAPERCLIP_EVALS_ROOT or a discovered sibling; a worktree's parent directory need not contain that repository. Read doc/evals.md and packages/paperclip-runner/docs/runner-protocol-live-evals.md, then inspect the nearest existing case, roster, schema, and report test before editing. Definitions and authored cases belong in the sibling paperclip-evals/evals/paperclip-runner; Runner integration, aggregation, viewer, and publication behavior belongs in packages/paperclip-runner. Keep the control-plane boundary explicit in names and documentation.

The sibling eval README is the concrete map: cases live under cases/, company fixtures under fixtures/, runtime/model settings under configs/, selections under rosters/, and maintained campaign membership under campaigns/live-direct-full.json. Update inventory/coverage mappings when the program requires them; a new file alone does not join the maintained campaign. From the Evals repository root, adapt these provider-free checks to the case and roster you changed. Run the reliability-plan validator only when that separate plan changes:

sh
python3 evals/paperclip-runner/tools/eval_program.py validate \
  --case evals/paperclip-runner/cases/get-task-context.json \
  --config evals/paperclip-runner/configs/live-codex-pinned.json
python3 evals/paperclip-runner/tools/run_live_roster.py validate \
  --roster evals/paperclip-runner/rosters/live-mini.json --run-id validate-new-case
python3 evals/paperclip-runner/tools/run_live_campaign.py validate \
  --campaign evals/paperclip-runner/campaigns/live-direct-full.json
python3 evals/paperclip-runner/tools/reliability_campaign.py validate \
  --plan evals/paperclip-runner/campaigns/paperclip-runner-reliability.json

Use nearby positive and negative grader cases/fixtures to calibrate the new assertion, including malformed or missing evidence where the grader must fail closed. Preserve the existing machine disposition and grade; product, model/provider, grading, and infrastructure labels are analytical annotations, not instructions to rewrite classifiers.

Author one bounded case with a deterministic semantic assertion and an inspectable result. Declare its expected operation, state effect, provider lane/profile, timeout and retry policy, and any required evidence. Do not grade hidden reasoning, infer success from a provider terminal message, or invent conversation/tool evidence. Public output follows the reviewed projection: sanitized fixture conversation and allowlisted tool outcomes may be published; raw trusted artifacts, credentials, secrets, private references, and hidden reasoning may not.

Validate without provider calls first using the commands above and the relevant report/render validation documented in the Runner docs. When a live run is authorized, pin the Paperclip commit and exact 40-character paperclip-evals commit, select the smallest useful roster, and retain the complete provenance and cost record.

Update authoritative detailed docs when the contract or command changes, then link from doc/evals.md rather than duplicating the Runner runbook. Keep public reports immutable and use the reviewed projection; sanitized fixture conversation and allowlisted tool outcomes may be public, while credentials, secrets, private references, raw trusted payloads, and hidden reasoning must not be exposed.

Frequently asked questions

What does the Add Runner Eval AI skill do?

Add or extend a Paperclip Runner protocol evaluation definition, roster, assertion, or report fixture with provenance and narrow validation.

Why use Add Runner Eval on TypingMind?

Because you install it once and use it with any model. Add Runner 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 Runner Eval in TypingMind?

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

Which AI models can use Add Runner 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 Runner Eval?

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

Is the Add Runner 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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