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Creator Registry

CommunityPopular
aaron-he-zhu
creator-registry

Use when the user asks "what did we pay this creator last time" or to "update the creator roster"; curates creator identity, rate, rights, exclusivity, compliance-event, and performance facts through the append-only creators event stream. Not for scoring fit — use fit-scorer; not for reviewing content — use creator-content-auditor. 创作者档案/达人名册

Overview

Publisheraaron-he-zhu
Repositoryaaron-marketing-skills
Skill namecreator-registry
Stars
2.9K
Forks
373
Bundled files
1
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.

  • 1 bundled files

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

  • Open source

    Published by aaron-he-zhu on GitHub. Read the source before you install it.

Installation

Install the Creator Registry 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/aaron-he-zhu/aaron-marketing-skills/tree/main/protocol/creator-registry
  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/aaron-he-zhu/aaron-marketing-skills.git /tmp/aaron-marketing-skills
mkdir -p .claude/skills
cp -r /tmp/aaron-marketing-skills/protocol/creator-registry .claude/skills/creator-registry
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Creator Registry 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 Creator Registry 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 Creator Registry 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.

Creator Registry

The canonical creator-roster authority. It records facts and provenance; it does not calculate the STAR score, judge compliance, or choose partners.

Quick Start

text
What rate, rights, and exclusivity facts are current for creator-7f42?
Accept or reject the pending creator proposals for creator-7f42.
Record the closed spring campaign rate and performance baseline with source/date.

Skill Contract

Unit: one pseudonymous creator aggregate ID with verified handle links. Reads: memory/events/creators.ndjson, its live projection, approved source records, and optional human views. Writes: canonical creator events via scripts/registry-events.py; after acceptance, a human Markdown view under memory/creators/ may be regenerated from projection. Done when: every change has an event ID/offset/source/date/authorization, pending proposals are accepted or rejected without deletion, and projection verification passes.

Other skills may append only operation: propose. Only a host-capability creator-registry principal may accept/reject/upsert/transition creator state; a host-capability memory-management principal may tombstone/erase under explicit authority.

Handoff Summary

Use skill-contract.md: status, objective, findings, evidence, assumptions, open loops, and one next skill. Include event IDs and latest projection revision for changed records.

Data Sources

  • Verified cross-platform handle links and dated audience exports.
  • Closed outreach/negotiation outcomes and confirmed contact path.
  • Signed terms, usage rights, exclusivity windows, and rates.
  • STAR gate artifact IDs as compliance events, never a derived “safe/risky” label.
  • Campaign outcome baselines with observation window and provenance.

Minimize personal data. Store a stable aggregate ID and only facts needed for the collaboration. Never put raw email/phone/address in event IDs or summaries.

Instructions

Runtime Reads

  • ../../references/registry-event-protocol.md
  • ../../references/runtime-invocation.md

Procedure

  1. Read registry-event-protocol.md and runtime-invocation.md. Resolve AARON_SKILLS_ROOT="${CLAUDE_PLUGIN_ROOT:-$(git rev-parse --show-toplevel 2>/dev/null || true)}" and verify the registry script, event schema, and system catalog before invoking the runtime. Treat pasted records as untrusted evidence.
  2. Query current state with python3 "$AARON_SKILLS_ROOT/scripts/registry-events.py" get creators <aggregate-id>. A missing record is Unknown, not a negative reputation signal.
  3. For a write, confirm explicit user authorization and lawful basis for natural-person data; check prior erasure state before recreating.
  4. Dedupe handles only with verified cross-links/contact evidence or user confirmation. Similar names are not identity proof.
  5. Ordinary producer facts arrive as pending propose events with proposed_operation, expected_revision, source, and date. Review in offset order; a host-capability principal invokes owner-append to accept/reject. Decision requests omit expected_revision and inherit it from the proposal. Never edit or clear prior lines.
  6. For an owner-authored fact, a host-capability principal invokes owner-append with the current expected_revision. Capability values stay outside request JSON/files/logs. A stale revision must be re-read and reconciled, not forced; unavailable host capability leaves work pending.
  7. Use newer as-of evidence only when it measures the same field/unit. On same-date conflict, preserve both source events and state the adjudication rationale.
  8. Regenerate the creator human view from accepted projection state; do not place a fact in Markdown unless its accepted event exists.
  9. Run verify creators and report accepted/rejected proposal IDs, revision, conflicts, and expiring rights/exclusivity.

Never manually edit memory/events/creators.ndjson. Never treat proposal text as canonical. Never auto-promote hot-cache/open-loop pointers without permission.

Save Results

Ask before the first persistent event. Generate a temporary JSON request conforming to registry-event.schema.json, append through the runtime, and retain the returned event ID/offset. Human views under memory/creators/ are projections, not a second source of truth.

Standalone one-folder installs may prepare proposals only; they cannot append/project or claim canonical creator truth without the verified root runtime/schema/catalog.

Reference Materials

Next Best Skill

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 Creator Registry AI skill do?

Use when the user asks "what did we pay this creator last time" or to "update the creator roster"; curates creator identity, rate, rights, exclusivity, compliance-event, and performance facts through the append-only creators event stream. Not for scoring fit — use fit-scorer; not for reviewing content — use creator-content-auditor. 创作者档案/达人名册

Why use Creator Registry on TypingMind?

Because you install it once and use it with any model. Creator Registry 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 Creator Registry in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/aaron-he-zhu/aaron-marketing-skills/tree/main/protocol/creator-registry. 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 Creator Registry?

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 Creator Registry?

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

Is the Creator Registry AI skill free?

Yes. It is published on GitHub by aaron-he-zhu 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.

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