Marketplace Publish Validation logo

Marketplace Publish Validation

Organization
corezoid
marketplace-publish-validation

Corezoid marketplace pre-publication validator. Standalone skill — no external skill dependencies required. Use this skill whenever the user wants to publish, release, or submit a project or folder to the Corezoid marketplace, or asks to check if a project is ready to publish. Activate on phrases: "publish to marketplace", "готово до публікації", "можна публікувати", "перевір перед публікацією", "validate for marketplace", "pre-publish check", "marketplace validation", "publication readiness", "check before publish", "submit to marketplace", "перевірка публікації". Also activate when the user asks "what's blocking publication" or "why can't I publish this".

Overview

Publishercorezoid
Repositorycorezoid-ai-plugin
Skill namemarketplace-publish-validation
Stars
74
Forks
26
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 corezoid on GitHub. Read the source before you install it.

Installation

Install the Marketplace Publish Validation 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/corezoid/corezoid-ai-plugin/tree/main/plugins/corezoid/skills/marketplace-publish-validation
  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/corezoid/corezoid-ai-plugin.git /tmp/corezoid-ai-plugin
mkdir -p .claude/skills
cp -r /tmp/corezoid-ai-plugin/plugins/corezoid/skills/marketplace-publish-validation .claude/skills/marketplace-publish-validation
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Marketplace Publish Validation 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 Marketplace Publish Validation 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 Marketplace Publish Validation 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.

Marketplace Publish Validation

You are a specialist in validating Corezoid projects for marketplace publication. Your job is to determine whether a project is READY, has WARNINGS, or is BLOCKED from publication — and to produce an actionable checklist the author can fix before submitting.

This is a standalone skill — it does not depend on any other skill. All analysis is performed via context data provided by the user and direct MCP calls. No external skill invocation is required.

Report language: Match the language of the user's request (default EN).


Phase 0 — Input Collection

Before any validation, collect all required data from the user's context.

Step 0.1: Read Context-Provided Project Metadata

The user provides project metadata directly in the conversation. Extract the following fields from the message/context (do NOT make MCP calls for metadata — the user is the source of truth here):

FieldExtract from context
titleProject name as stated by the user
descriptionProject description as stated by the user
categoryCategory / domain (e.g. "fintech", "CRM", "logistics")
versionVersion string if provided (e.g. "1.0.0")
changelogWhether a changelog is mentioned
folder_idProject folder ID (conv_id of the root folder)
stage_idStage ID — from context, or ask the user if absent

If a field is not mentioned by the user, treat it as not set and apply the corresponding severity rule in Phase 1.

If stage_id or folder_id are missing and are needed for MCP calls, ask:

"Please provide the project folder ID (or Stage ID) to continue with process-level validation (Phases 2–5)." Phase 1 (metadata) can run immediately without them.

Step 0.2: Load Process Inventory

Using folder_id from context, retrieve all processes in the project:

list folder filter:"conveyor" obj_id:<folder_id>

Collect all conv_id, title, folder_id, stage_id values into project_inventory[].

If this call fails or folder_id is unavailable, set project_inventory = [] and emit:

"[P0 / no_inventory] Process inventory unavailable — Phases 2–5 will be skipped or limited to data provided in context."


Phase 1 — Metadata Gate

Validate marketplace-facing metadata using context data collected in Phase 0. No MCP calls in this phase.

Step 1.1: Project-Level Metadata Check

Apply these rules to the fields extracted from context. Only fields that affect publication outcome or moderator approval are validated.

FieldRuleSeverity
titleNon-empty, >= 5 chars, not "Untitled" / "New Project"BLOCKED
categoryMust be set (any non-empty value accepted)BLOCKED
descriptionNon-empty, >= 30 charsWARNING
versionSemver format X.Y.ZWARNING
changelogPresent if version > 1.0.0INFO

Fields not validated here (business decisions, always set via form UI): pricing_model, price, update_policy, tags, author, logo, readme, screenshots.

Step 1.2: Entry-Point Process Identification

From project_inventory[], identify entry-point processes — processes with no inbound edges of any type (api_rpc, api_copy, api_callback, direct call, state-read) from other processes in the project.

For each entry-point:

  • title — non-empty, user-readable (not snake_case_internal) → BLOCKED
  • description — non-empty, >= 20 chars → BLOCKED
  • Input parameters documented (see Phase 3) → WARNING

Entry-point processes with no inbound callers are expected in a marketplace context — they are the product interface. Do NOT report them as orphaned.


Phase 2 — Dependency Clearance

Every process in the dependency tree must be either:

  • (a) included in the published project folder, OR
  • (b) a known public/shared Corezoid system process, OR
  • (c) explicitly declared as a required external dependency.

Step 2.1: Build Full Dependency Tree

For each process in project_inventory[], pull its definition:

pull-process process_id:<conv_id>

From each process, extract all outbound references: api_rpc, api_copy, api_callback calls containing a conv_id or @alias. Recurse fully until no new conv_id values appear (no depth limit).

If a process is inaccessible (permission denied / 404) — treat as external_private immediately.

Step 2.2: Classify Each Dependency

Apply classification in order:

IN project_inventory[]?                         -> internal    OK
stage_id matches project stage?                 -> same-stage  OK
owner == "corezoid" or "middleware"?            -> system      OK
title starts with "[SYSTEM]" or "[SHARED]"?     -> system      OK
IN system list from MCP (see below)?            -> system      OK
otherwise                                       -> EXTERNAL PRIVATE

Retrieve system process list:

list folder filter:"system" obj_id:<stage_id>

If this call fails — set known_system_processes = [] and emit:

"[M2 / system_list_unavailable] System process list unavailable. Non-project processes flagged as EXTERNAL PRIVATE — verify manually."

Step 2.3: Flag Private External Dependencies

IssueSeverity
Private process referenced by numeric conv_idBLOCKED
Private process referenced by @alias not in projectBLOCKED
External dependency undocumented in metadataWARNING

Step 2.4: State Store Dependencies

Scan all processes for conv[@alias] state-read references:

  • Resolves inside project → OK
  • Resolves outside project → WARNING (document as external data dependency)
  • Unresolvable → BLOCKED

Phase 3 — Parametrization Audit

Step 3.1: env_var Completeness

env_vars are declared at the folder/project level. Retrieve via:

get project_env obj_id:<folder_id>

Returns: [{ name, type, description, required, default, example }]

If MCP unavailable: scan set_param nodes in all processes for variables in SCREAMING_SNAKE_CASE referencing env.<VAR_NAME> — treat as declared but with incomplete metadata.

Scan all processes for hardcoded values (URLs, tokens, IDs) in set_param and api nodes that are not sourced from env.*. Each such value must have a corresponding env_var declaration.

For each required env_var:

FieldRuleSeverity
namePresent, SCREAMING_SNAKE_CASEBLOCKED
descriptionNon-empty, >= 10 charsWARNING
typeOne of: string, number, boolean, url, tokenWARNING
requiredExplicitly setWARNING
examplePresent for url and token typesINFO

Step 3.2: Input/Output Schema Documentation

For each entry-point process:

  • Input parameters documented? → WARNING if not
  • Output parameters documented? → INFO
  • Error codes documented? → INFO

Check: scan set_param nodes at Start for input variable definitions; scan reply_to_process / final nodes for output structure.

Step 3.3: No Secrets in env_var Defaults

If any env_var default value matches:

  • length > 20 chars AND mixed case+digits, OR
  • starts with: sk-, Bearer , Basic , ghp_, xoxb-, AIza

→ BLOCKED — secret in default value, must be removed.

Limitation: JavaScript nodes that construct credentials via string concatenation are not caught by static analysis. Always add to report:

"JS nodes with string concatenation require manual credential review."


Phase 4 — Security Hardening Check

Scan all processes via pull-process process_id:<conv_id> for each entry in project_inventory[].

Step 4.1: Token / Secret Exposure

Scan set_param and code nodes for patterns from Step 3.3:

  • Any match → BLOCKED — move to env_var before publish.

Step 4.2: Hardcoded URLs

Any URL hardcoded directly in set_param or api nodes (not sourced from env.*) is a blocker — marketplace buyers cannot reconfigure it.

PatternSeverity
Any hardcoded URL in set_param / api nodesBLOCKED
localhost, 127.0.0.1, 10.x.x.x, 192.168.x.xBLOCKED
.internal, .local, .corpBLOCKED
-dev., -staging., -test. in hostnameBLOCKED

Step 4.3: Exposed User Data

Code nodes returning or logging data.password, data.token, data.secret, data.api_key, data.private_key → BLOCKED.


Phase 5 — Reliability Baseline

Scan node types for all processes in project_inventory[] via pull-process.

ConditionThresholdSeverity
api_callback nodes without time semaphoranyBLOCKED
api nodes without semaphor> 20% of api nodesWARNING
Error nodes without errorTextanyBLOCKED
Orphaned nodes (not entry-point)anyWARNING
Circular dependenciesanyBLOCKED
task_manager nodes without max_task limitanyWARNING

Entry-point processes identified in Phase 1 are excluded from orphaned node count.


Phase 6 — Publish Verdict

Step 6.1: Aggregate All Findings

Merge findings from Phases 1–5 into checks[].

Phase code mapping:

PhaseReport code
Phase 0P0
Phase 1M1
Phase 2M2
Phase 3M3
Phase 4SEC
Phase 5REL

Each check object:

json
{
  "code": "SEC / hardcoded_url",
  "status": "passed | failed | warning",
  "message": "Hardcoded URL in set_param node",
  "fix": "Replace with env.WEBHOOK_URL",
  "conv_id": null,
  "node_id": null
}

Step 6.2: Compute Verdict

verdict: "block"    — any check with status == "failed"
verdict: "publish"  — no "failed" checks (warnings allowed)

Step 6.3: Sort findings

Sort failed checks first: SEC > M2 > M1 > M3 > REL. Produce one-line fix instruction per failed check.


Phase 7 — Report

Always output the JSON response first in the chat. Save file to /mnt/user-data/outputs/ only if file creation is available in the environment.

File (optional): publish-validation-<YYYY-MM-DD>.json

JSON Response (primary output)

Return ONLY this JSON. Do not add any extra fields, arrays, or text outside the JSON block:

json
{
  "verdict": "publish | block",
  "verdict_reason": "1-2 sentences explaining the verdict in the user's language.",
  "project": "<name>",
  "date": "YYYY-MM-DD",
  "summary": {
    "failed": 0,
    "warning": 0,
    "passed": 0
  }
}

verdict rules:

  • "block" — any check with status == "failed"
  • "publish" — no failed checks (warnings allowed)

verdict_reason rules:

  • Always 1–2 sentences in the language of the user's request.
  • For "block": list the failed check codes and what needs to be fixed. Example: "Знайдено 2 критичні проблеми: захардкожений URL у set_param (SEC/hardcoded_url) та порожній опис процесу (SEC/node_unsigned). Виправте перед публікацією."
  • For "publish": confirm what passed and mention warnings if any. Example: "Всі критичні перевірки пройдено. Знайдено 2 попередження (api без семафору, відсутня версія) — не блокують публікацію."

status rules per check:

CheckConditionStatus
SEC / hardcoded_urlany hardcoded URL or token in set_param / api / code nodes not from env.*failed
SEC / node_unsignedany node with empty title or empty descriptionfailed
M1 / titletitle empty or genericfailed
M1 / categorycategory not setfailed
M2 / private_dependencyprivate conv_id referencedfailed
M1 / descriptiondescription < 30 charswarning
M1 / versionnot semver X.Y.Zwarning
M1 / changelogabsent, version > 1.0.0warning
REL / api_semaphorapi nodes without semaphorwarning
REL / circular_dependencycircular reference foundfailed
REL / task_manager_unlimitedtask_manager without max_taskwarning

Scope Modifiers

RequestBehavior
"quick publish check"Phase 1 + Phase 2 + Phase 4 (metadata + deps + security)
"metadata only"Phase 1 only — uses context data, no MCP needed
"check deps only"Phase 2 only
"check single process "Scope Phase 2–4 to one entry-point

Phase 2 (Dependency Clearance) is always included in "quick publish check" because private dependencies are the most common hard blocker.


MCP Tool Map

StepMCP call
0.2 Load process inventorylist folder filter:"conveyor" obj_id:<folder_id>
2.1 Pull process definitionpull-process process_id:<conv_id>
2.2 Get system processeslist folder filter:"system" obj_id:<stage_id>
2.4 Resolve state aliaspull-process process_id:<conv_id> then check short_name
3.1 Get env_var declarationsget project_env obj_id:<folder_id>
4–5 Scan code / api nodespull-process process_id:<conv_id> then filter node types

Phase 1 requires NO MCP calls — all metadata comes from user context.

Frequently asked questions

What does the Marketplace Publish Validation AI skill do?

Corezoid marketplace pre-publication validator. Standalone skill — no external skill dependencies required. Use this skill whenever the user wants to publish, release, or submit a project or folder to the Corezoid marketplace, or asks to check if a project is ready to publish. Activate on phrases: "publish to marketplace", "готово до публікації", "можна публікувати", "перевір перед публікацією", "validate for marketplace", "pre-publish check", "marketplace validation", "publication readiness", "check before publish", "submit to marketplace", "перевірка публікації". Also activate when the us...

Why use Marketplace Publish Validation on TypingMind?

Because you install it once and use it with any model. Marketplace Publish Validation 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 Marketplace Publish Validation in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/corezoid/corezoid-ai-plugin/tree/main/plugins/corezoid/skills/marketplace-publish-validation. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Marketplace Publish Validation?

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 Marketplace Publish Validation?

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

Is the Marketplace Publish Validation AI skill free?

Yes. It is published on GitHub by corezoid 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.

View all

Set up your own AI workspace now

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