Prior Art logo

Prior Art

Organization
runxhq
prior-art

Inspect bounded local repository and Runx catalog evidence before a design, draft, or operator decision, then produce citation-bound findings and a reuse, amendment, new-work, or stop recommendation. Use when existing tools, skills, standards, or project patterns must constrain downstream work; use research or web-fetch first for external sources.

Overview

Publisherrunxhq
Repositoryrunx
Skill nameprior-art
Stars
105
Forks
109
Bundled files
6
LicenseApache-2.0
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.

  • 6 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

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

Installation

Install the Prior Art 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/runxhq/runx/tree/main/skills/prior-art
  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/runxhq/runx.git /tmp/runx
mkdir -p .claude/skills
cp -r /tmp/runx/skills/prior-art .claude/skills/prior-art
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Prior Art 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 Prior Art 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 Prior Art 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.

Prior Art

Ground the next decision in sources that were actually inspected.

Direct use

Do the inspection and return the recommendation in the same task. Do not stop after announcing a search plan, ask the operator to enumerate obvious files, or turn bounded discovery into a repository-wide grep.

  1. Start from the operator's objective and current workspace. When exact source_paths are already supplied, reuse them.
  2. Otherwise, make one targeted discovery pass for the smallest likely owning set: repository instructions and conventions, the named product or package, exact active plans or specs implicated by the objective, and adjacent Runx skill manifests. Select at most sixteen files. Listing filenames is discovery, not the prior-art result; continue through inspection.
  3. Invoke the default runner with the objective, chosen paths, and any existing decomposition. Complete its bounded research request, resume it in memory, and return the validated prior-art report—not a narration of what you intend to read.
  4. If the selected evidence is insufficient, return one precise needs_more_evidence result naming the missing source. Do not broaden the search repeatedly or cycle through unrelated skills.

Routine local discovery and reads use the host's normal authenticated tools and need no approval. Runx adds the catalog projection, digest-bound source bundle, citation validation, reusable packet, and receipt. If Runx is unavailable, preserve the selected paths and findings in the documented output shape so the operator can continue locally without repeating discovery; do not claim a Runx receipt.

Composed use

A parent should pass its objective, decomposition, and already selected source paths. Reuse that evidence boundary exactly. Do not enumerate the repository, reacquire the catalog, or repeat a prior evidence-selection step unless the packet is missing, stale, or outside the current objective.

Executable procedure

  1. Supply a bounded objective, optional code-plan decomposition, and the repo-relative source_paths selected by direct discovery or a parent chain.
  2. Native runx.skill.inspect indexes the local Runx catalog and fs.read_bundle reads and hashes at most sixteen requested files under the workspace boundary. Missing files are recorded; escaping, duplicate, or oversized paths fail closed.
  3. Read only the indexed sources needed for the objective. State each finding as claim, source, relevance, and confidence (verified, likely, or unverified).
  4. Name adjacent catalog skills and the boundary each already owns. Recommend reuse, amend, new_work, or stop; do not create a duplicate primitive because an existing package is imperfect.
  5. The domain validator checks every verified citation and adjacent skill against those native projections. Unsupported verified claims or missing requested sources force needs_more_evidence.

External URLs are not inspected by this runner. Fetch them through a governed source skill, persist the bounded evidence in the workspace, then pass its path here.

Output

yaml
decision: ready | needs_more_evidence
findings:
  - claim: string
    source: repo-relative path
    relevance: string
    confidence: verified | likely | unverified
catalog_fit:
  decision: reuse | amend | new_work | stop
  adjacent_skills: array
  rationale: string
recommended_flow: array
quality_bar: object
sources: array
risks: array
evidence: object
validation: object

Inputs are objective, optional decomposition, graph_purpose, audience, artifact_contract, and bounded source_paths.

Agent task contracts

prior-art-research

Use the deterministic evidence index to select and read only the bounded local sources needed for the objective. Return prior_art_draft with decision, findings, catalog_fit, quality_bar, recommended_flow, sources, and risks. Cite repo-relative paths exactly. Mark a finding verified only when its source appears in inspected_sources or the catalog index. Prefer reuse or amendment over duplicate primitives. Return needs_more_evidence when requested sources are missing.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Prior Art AI skill do?

Inspect bounded local repository and Runx catalog evidence before a design, draft, or operator decision, then produce citation-bound findings and a reuse, amendment, new-work, or stop recommendation. Use when existing tools, skills, standards, or project patterns must constrain downstream work; use research or web-fetch first for external sources.

Why use Prior Art on TypingMind?

Because you install it once and use it with any model. Prior Art 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 Prior Art in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/runxhq/runx/tree/main/skills/prior-art. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Prior Art?

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 Prior Art?

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

Is the Prior Art AI skill free?

Yes. It is published on GitHub by runxhq under the Apache-2.0 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇