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shopify-admin-agentic-metafields-setup

Define and populate agentic-commerce metafields (material, attributes, key features, specs, sizing) so AI agents can filter and match products to specific shopper requirements.

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Prix non confirmé★ 182 Stars GitHubRegistre mis à jour · 4 sept. 2026agent-skill

Vue d’ensemble

Define and populate agentic-commerce metafields (material, attributes, key features, specs, sizing) so AI agents can filter and match products to specific shopper requirements.

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Purpose

AI agents answer constrained queries — "squat-proof leggings under $60", "eucalyptus slip-ons", "machine-washable wool" — by filtering on structured attributes. If those attributes live only in prose (or nowhere), the agent can't filter and your products drop out of the result set. This skill establishes a small, standard set of agentic metafield definitions (material, key features, care, fit, specs) and populates them across the catalog from existing product signals, so agents can match products to requirements. Fixes listing-metafields, variant-metadata, and sizing-specs-structured.

Prerequisites

Authenticated Shopify CLI session (shopify auth login --store <domain>)
Required API scopes: read_products, write_products, read_metaobject_definitions, write_metaobject_definitions (for definitions)

Parameters

All skills accept these universal parameters:

Parameter	Type	Required	Default	Description
store	string	yes	—	Store domain (e.g., mystore.myshopify.com)
format	string	no	human	Output format: `human` (default) or `json`
dry_run	bool	no	false	Preview mutations without executing

Skill-specific parameters:

Parameter	Type	Required	Default	Description
namespace	string	no	agentic	Metafield namespace to create/populate under
keys	string	no	material,features,care,fit,specs	Comma list of metafield keys to ensure exist
collection_id	string	no	—	Limit population to a collection GID
tag	string	no	—	Limit population to a product tag
populate_from	string	no	tags,options,description	Sources to infer values from (no fabrication beyond these)

Safety

⚠️ Step 2 (metafieldDefinitionCreate) and Step 4 (metafieldsSet) write store schema + product data. Definitions are cheap to add but clutter the admin if mis-namespaced; values written from inference can be wrong. Run dry_run: true, review the proposed definitions and the value preview, and only populate values inferred with high confidence — leave the rest blank for human fill.

Workflow Steps

OPERATION: metafieldDefinitions — query Inputs: ownerType: PRODUCT, namespace: <namespace> Expected output: Which target keys already have definitions (skip those).
OPERATION: metafieldDefinitionCreate — mutation Inputs: one per missing key: { namespace, key, name, ownerType: PRODUCT, type: "single_line_text_field" | "list.single_line_text_field" } Expected output: Created definitions; collect userErrors (e.g. already-taken).
OPERATION: products — query Inputs: first: 250, optional filter; fields tags, options, descriptionHtml, existing metafields(namespace); paginate. Expected output: Products + the signals to infer attribute values from.
OPERATION: metafieldsSet — mutation Inputs: batches of { ownerId, namespace, key, value, type } for confidently-inferred, currently-empty values. Expected output: Set metafields; collect userErrors.

GraphQL Operations

# metafieldDefinitions:query — validated against api_version 2025-01
query AgenticMetafieldDefs($namespace: String!) {
  metafieldDefinitions(first: 50, ownerType: PRODUCT, namespace: $namespace) {
    edges { node { id namespace key name type { name } } }
  }
}

# metafieldDefinitionCreate:mutation — validated against api_version 2025-01
mutation AgenticMetafieldDefCreate($definition: MetafieldDefinitionInput!) {
  metafieldDefinitionCreate(definition: $definition) {
    createdDefinition { id namespace key }
    userErrors { field message code }
  }
}

# products:query — validated against api_version 2025-01
query AgenticMetafieldProducts($first: Int!, $after: String, $query: String, $namespace: String!) {
  products(first: $first, after: $after, query: $query) {
    edges {
      node {
        id
        title
        tags
        options { name values }
        descriptionHtml
        metafields(first: 20, namespace: $namespace) {
          edges { node { key value } }
        }
      }
    }
    pageInfo { hasNextPage endCursor }
  }
}

# metafieldsSet:mutation — validated against api_version 2025-01
mutation AgenticMetafieldsSet($metafields: [MetafieldsSetInput!]!) {
  metafieldsSet(metafields: $metafields) {
    metafields { id namespace key }
    userErrors { field message code }
  }
}

Session Tracking

Claude MUST emit the following output at each stage. This is mandatory.

On start, emit:

╔══════════════════════════════════════════════╗
║  SKILL: <skill name>                         ║
║  Store: <store domain>                       ║
║  Started: <YYYY-MM-DD HH:MM UTC>             ║
╚══════════════════════════════════════════════╝

After each step, emit:

[N/TOTAL] <QUERY|MUTATION>  <OperationName>
          → Params: <brief summary of key inputs>
          → Result: <count or outcome>

If dry_run: true, prefix every mutation step with [DRY RUN] and do not execute it.

On completion, emit:

For format: human (default):

══════════════════════════════════════════════
OUTCOME SUMMARY
  <Metric label>:   <value>
  Errors:           0
  Output:           <filename or "none">
══════════════════════════════════════════════

For format: json, emit:

{
  "skill": "<skill-slug>",
  "store": "<domain>",
  "started_at": "<ISO8601>",
  "completed_at": "<ISO8601>",
  "dry_run": false,
  "steps": [
    {
      "step": 1,
      "operation": "<OperationName>",
      "type": "query",
      "params_summary": "<string>",
      "result_summary": "<string>",
      "skipped": false
    }
  ],
  "outcome": {
    "metric_key": 0,
    "errors": 0,
    "output_file": null
  }
}

Output Format

human: definitions created + a CSV of populated values (product, key, value, source). json: { definitions_created, metafields_set, products_touched, errors, output_file }.

Error Handling

Error	Cause	Recovery
`THROTTLED`	API rate limit	Wait 2s, retry up to 3 times
`TAKEN` on definition	Key already defined elsewhere	Reuse the existing definition, continue to population
`userErrors` on set	Type mismatch (e.g. list vs single)	Coerce value to the definition's type, retry once, else skip

Best Practices

Keep the namespace small and standard (agentic) and the key set tight — agents and storefront filters both benefit from consistency.
Only write values you can infer with high confidence from real signals; a wrong "material: leather" misleads every agent. Leave low-confidence fields blank.
Use list.single_line_text_field for multi-value attributes (features, materials) so filters work as OR-sets.
Follow with shopify-admin-agentic-description-enrichment so the prose and the structured data agree.

Voir la source ↗

Métadonnées du fichier

name: shopify-admin-agentic-metafields-setup
role: agentic
description: "Define and populate agentic-commerce metafields (material, attributes, key features, specs, sizing) so AI agents can filter and match products to specific shopper requirements."
toolkit: shopify-admin, shopify-admin-execution
api_version: "2025-01"
graphql_operations:
  - metafieldDefinitions:query
  - metafieldDefinitionCreate:mutation
  - products:query
  - metafieldsSet:mutation
status: stable
compatibility: Claude Code, Cursor, Codex, Gemini CLI
audit_signals:
  - listing-metafields
  - variant-metadata
  - sizing-specs-structured

Voir le texte original

---
name: shopify-admin-agentic-metafields-setup
role: agentic
description: "Define and populate agentic-commerce metafields (material, attributes, key features, specs, sizing) so AI agents can filter and match products to specific shopper requirements."
toolkit: shopify-admin, shopify-admin-execution
api_version: "2025-01"
graphql_operations:
  - metafieldDefinitions:query
  - metafieldDefinitionCreate:mutation
  - products:query
  - metafieldsSet:mutation
status: stable
compatibility: Claude Code, Cursor, Codex, Gemini CLI
audit_signals:
  - listing-metafields
  - variant-metadata
  - sizing-specs-structured
---

## Purpose
AI agents answer constrained queries — "squat-proof leggings under $60", "eucalyptus slip-ons", "machine-washable wool" — by filtering on structured attributes. If those attributes live only in prose (or nowhere), the agent can't filter and your products drop out of the result set. This skill establishes a small, standard set of **agentic metafield definitions** (material, key features, care, fit, specs) and populates them across the catalog from existing product signals, so agents can match products to requirements. Fixes `listing-metafields`, `variant-metadata`, and `sizing-specs-structured`.

## Prerequisites
- Authenticated Shopify CLI session (`shopify auth login --store <domain>`)
- Required API scopes: `read_products`, `write_products`, `read_metaobject_definitions`, `write_metaobject_definitions` (for definitions)

## Parameters
All skills accept these universal parameters:

| Parameter | Type   | Required | Default | Description |
|-----------|--------|----------|---------|-------------|
| store     | string | yes      | —       | Store domain (e.g., mystore.myshopify.com) |
| format    | string | no       | human   | Output format: `human` (default) or `json` |
| dry_run   | bool   | no       | false   | Preview mutations without executing |

Skill-specific parameters:

| Parameter | Type | Required | Default | Description |
|-----------|------|----------|---------|-------------|
| namespace | string | no | agentic | Metafield namespace to create/populate under |
| keys | string | no | material,features,care,fit,specs | Comma list of metafield keys to ensure exist |
| collection_id | string | no | — | Limit population to a collection GID |
| tag | string | no | — | Limit population to a product tag |
| populate_from | string | no | tags,options,description | Sources to infer values from (no fabrication beyond these) |

## Safety

> ⚠️ Step 2 (`metafieldDefinitionCreate`) and Step 4 (`metafieldsSet`) write store schema + product data. Definitions are cheap to add but clutter the admin if mis-namespaced; values written from inference can be wrong. Run `dry_run: true`, review the proposed definitions and the value preview, and only populate values inferred with high confidence — leave the rest blank for human fill.

## Workflow Steps

1. **OPERATION:** `metafieldDefinitions` — query
   **Inputs:** `ownerType: PRODUCT`, `namespace: <namespace>`
   **Expected output:** Which target keys already have definitions (skip those).

2. **OPERATION:** `metafieldDefinitionCreate` — mutation
   **Inputs:** one per missing key: `{ namespace, key, name, ownerType: PRODUCT, type: "single_line_text_field" | "list.single_line_text_field" }`
   **Expected output:** Created definitions; collect `userErrors` (e.g. already-taken).

3. **OPERATION:** `products` — query
   **Inputs:** `first: 250`, optional filter; fields `tags`, `options`, `descriptionHtml`, existing `metafields(namespace)`; paginate.
   **Expected output:** Products + the signals to infer attribute values from.

4. **OPERATION:** `metafieldsSet` — mutation
   **Inputs:** batches of `{ ownerId, namespace, key, value, type }` for confidently-inferred, currently-empty values.
   **Expected output:** Set metafields; collect `userErrors`.

## GraphQL Operations

```graphql
# metafieldDefinitions:query — validated against api_version 2025-01
query AgenticMetafieldDefs($namespace: String!) {
  metafieldDefinitions(first: 50, ownerType: PRODUCT, namespace: $namespace) {
    edges { node { id namespace key name type { name } } }
  }
}
```

```graphql
# metafieldDefinitionCreate:mutation — validated against api_version 2025-01
mutation AgenticMetafieldDefCreate($definition: MetafieldDefinitionInput!) {
  metafieldDefinitionCreate(definition: $definition) {
    createdDefinition { id namespace key }
    userErrors { field message code }
  }
}
```

```graphql
# products:query — validated against api_version 2025-01
query AgenticMetafieldProducts($first: Int!, $after: String, $query: String, $namespace: String!) {
  products(first: $first, after: $after, query: $query) {
    edges {
      node {
        id
        title
        tags
        options { name values }
        descriptionHtml
        metafields(first: 20, namespace: $namespace) {
          edges { node { key value } }
        }
      }
    }
    pageInfo { hasNextPage endCursor }
  }
}
```

```graphql
# metafieldsSet:mutation — validated against api_version 2025-01
mutation AgenticMetafieldsSet($metafields: [MetafieldsSetInput!]!) {
  metafieldsSet(metafields: $metafields) {
    metafields { id namespace key }
    userErrors { field message code }
  }
}
```

## Session Tracking

**Claude MUST emit the following output at each stage. This is mandatory.**

**On start**, emit:
```
╔══════════════════════════════════════════════╗
║  SKILL: <skill name>                         ║
║  Store: <store domain>                       ║
║  Started: <YYYY-MM-DD HH:MM UTC>             ║
╚══════════════════════════════════════════════╝
```

**After each step**, emit:
```
[N/TOTAL] <QUERY|MUTATION>  <OperationName>
          → Params: <brief summary of key inputs>
          → Result: <count or outcome>
```

If `dry_run: true`, prefix every mutation step with `[DRY RUN]` and do not execute it.

**On completion**, emit:

For `format: human` (default):
```
══════════════════════════════════════════════
OUTCOME SUMMARY
  <Metric label>:   <value>
  Errors:           0
  Output:           <filename or "none">
══════════════════════════════════════════════
```

For `format: json`, emit:
```json
{
  "skill": "<skill-slug>",
  "store": "<domain>",
  "started_at": "<ISO8601>",
  "completed_at": "<ISO8601>",
  "dry_run": false,
  "steps": [
    {
      "step": 1,
      "operation": "<OperationName>",
      "type": "query",
      "params_summary": "<string>",
      "result_summary": "<string>",
      "skipped": false
    }
  ],
  "outcome": {
    "metric_key": 0,
    "errors": 0,
    "output_file": null
  }
}
```

## Output Format
`human`: definitions created + a CSV of populated values (`product, key, value, source`). `json`: `{ definitions_created, metafields_set, products_touched, errors, output_file }`.

## Error Handling
| Error | Cause | Recovery |
|-------|-------|----------|
| `THROTTLED` | API rate limit | Wait 2s, retry up to 3 times |
| `TAKEN` on definition | Key already defined elsewhere | Reuse the existing definition, continue to population |
| `userErrors` on set | Type mismatch (e.g. list vs single) | Coerce value to the definition's type, retry once, else skip |

## Best Practices
- Keep the namespace small and standard (`agentic`) and the key set tight — agents and storefront filters both benefit from consistency.
- Only write values you can infer with high confidence from real signals; a wrong "material: leather" misleads every agent. Leave low-confidence fields blank.
- Use `list.single_line_text_field` for multi-value attributes (features, materials) so filters work as OR-sets.
- Follow with `shopify-admin-agentic-description-enrichment` so the prose and the structured data agree.

Examiner la source

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Licence: MIT
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Réviser avant installation: Éviter l’installation automatique

Licence: MIT

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Financial research output is not financial advice; require human review before any live investment decision.
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Stars/forks activity: 182 stars, 18 forks; issue activity unavailable in current metadata
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Source et conseils d’utilisation

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Dépôt source: 40RTY-ai/shopify-admin-skills
Licence: MIT
Version: 1.0.0
Dernier push GitHub: 14 août 2026
Registre mis à jour: 4 sept. 2026
Chemin des instructions: skills/agentic/shopify-admin-agentic-metafields-setup/SKILL.md @ 6765cb4f436b

Version déclarée dans le registre ; vérifiez les versions de la source.

Qualité

66/100

Prometteur

Confiance

62/100

Sandbox uniquement

Audit

75/100

Revue nécessaire

Ouvrir l’audit complet Voir le rapport d’évaluation

Dependency or permission surface needs review
Permission surface may require sandboxing
Financial research output is not financial advice; require human review before any live investment decision
Financial research output is not financial advice; require human review before any live investment decision.
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
Stars/forks activity: 182 stars, 18 forks; issue activity unavailable in current metadata
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution

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Résultats: —

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Plus de détails

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      "repoActivity": "182 stars, 18 forks",
      "lastPushed": "2mo since push",
      "license": "MIT",
      "repository": "https://github.com/40RTY-ai/shopify-admin-skills/tree/main/skills/agentic/shopify-admin-agentic-metafields-setup",
      "install": "npx skills add 40RTY-ai/shopify-admin-skills --skill shopify-admin-agentic-metafields-setup",
      "installSafety": "standard package or runtime install path",
      "permissionSurface": "secrets or environment access, shell or command execution",
      "documentation": "Usable metadata, review docs",
      "agentOutcomes": "No agent outcome data yet"
    },
    "outcome_evidence": {
      "total": 0,
      "successes": 0,
      "failures": 0,
      "not_relevant": 0,
      "success_rate": null,
      "recent_success_rate": null,
      "recent_failure_rate": null,
      "install_attempts": 0,
      "install_success_rate": null,
      "risk_blocked": 0,
      "setup_required": 0,
      "avg_output_quality": null,
      "production_outcomes": 0,
      "last_outcome_at": null,
      "label": "No agent outcome data yet"
    },
    "auto_install": {
      "allowed": false,
      "sandbox_required": true,
      "reason": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
    },
    "best_for": [
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      "agent-skill"
    ],
    "known_risks": [
      "Financial research output is not financial advice; require human review before any live investment decision.",
      "Quality score needs review",
      "Permission surface needs review: secrets or environment access, shell or command execution",
      "Stars/forks activity: 182 stars, 18 forks; issue activity unavailable in current metadata",
      "Dependency/runtime risk: command execution surface, credential or environment access",
      "Permission surface: secrets or environment access, shell or command execution"
    ]
  },
  "agent_proven": {
    "version": "agent-proven-v1",
    "score": 0,
    "tier": "unproven",
    "label": "Needs first agent run",
    "summary": "No agent outcome reports yet. Use Resolve, run one narrow sandbox task, then report the result.",
    "metrics": {
      "totalOutcomes": 0,
      "successfulOutcomes": 0,
      "failedOutcomes": 0,
      "installAttempts": 0,
      "installSuccessRate": null,
      "successRate": null,
      "recentSuccessRate": null,
      "recentFailureRate": null,
      "riskBlocked": 0,
      "setupRequired": 0,
      "notRelevant": 0,
      "avgOutputQuality": null,
      "avgTimeToUsefulMs": null,
      "productionOutcomes": 0,
      "humanReviewRequired": 0,
      "uniqueAgents": 0,
      "lastOutcomeAt": null
    },
    "signals": [],
    "penalties": [
      "No real agent outcome evidence yet"
    ]
  },
  "audit": {
    "score": 75,
    "risk_level": "needs_review",
    "risk_label": "Needs review",
    "warnings": [
      "Dependency or permission surface needs review",
      "Permission surface may require sandboxing",
      "Financial research output is not financial advice; require human review before any live investment decision",
      "Financial research output is not financial advice; require human review before any live investment decision.",
      "Quality score needs review",
      "Permission surface needs review: secrets or environment access, shell or command execution",
      "Stars/forks activity: 182 stars, 18 forks; issue activity unavailable in current metadata",
      "Dependency/runtime risk: command execution surface, credential or environment access"
    ]
  },
  "safety_gate": {
    "tier": "blocked",
    "label": "Blocked for auto-install",
    "auto_install_policy": "block",
    "auto_install_allowed": false,
    "human_review_required": true,
    "blocked": true,
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
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  "quality": {
    "score": 66,
    "label": "Promising"
  },
  "supply": {
    "track": "Research and knowledge work",
    "scenario": "Research agents",
    "maintenance": "2mo since push",
    "risk": "Needs review"
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  "alternative_skills": [],
  "do_not_use_when": [
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    "high-compliance environments without internal security review",
    "No major risk signals from current metadata",
    "High-risk permission hints: Shell or command execution, Secrets or environment access",
    "Dependency or permission surface needs review",
    "Permission surface may require sandboxing",
    "Financial research output is not financial advice; require human review before any live investment decision",
    "Financial research output is not financial advice; require human review before any live investment decision."
  ],
  "agent_contract": {
    "task_input": "Use shopify-admin-agentic-metafields-setup in an agent workflow",
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first.",
    "install_policy": "block",
    "minimum_review_before_use": [
      "Trust: 70/100 Manual review",
      "Audit: 75/100 Needs review",
      "Safety: 31/100 Avoid automatic install",
      "Review repository, license, install command, and permission surface before production use."
    ],
    "expected_agent_output": {
      "selected_skill": "40rty-ai-shopify-admin-agentic-metafields-setup (shopify-admin-agentic-metafields-setup)",
      "install_command": "npx skills add 40RTY-ai/shopify-admin-skills --skill shopify-admin-agentic-metafields-setup",
      "risk_summary": "Needs review; Blocked for auto-install; Review before production",
      "verification_result": "Report the smallest successful task, files touched, warnings, and any missing setup."
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  "outcome_feedback": {
    "endpoint": "https://www.openagentskill.com/api/agent/outcome",
    "method": "POST",
    "requires_resolve_event_id": true,
    "event_id_source": "Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.",
    "expected_outcomes": [
      "success",
      "failed",
      "not_relevant",
      "blocked_by_risk",
      "setup_required"
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    "payload_template": {
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      "skill_slug": "40rty-ai-shopify-admin-agentic-metafields-setup",
      "task": "Use shopify-admin-agentic-metafields-setup in an agent workflow",
      "agent": "codex",
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      "install_used": true,
      "risk_blocked": false,
      "setup_required": false,
      "task_success": true,
      "output_quality": 4,
      "error_type": null,
      "human_review_required": false,
      "workspace": "sandbox",
      "time_to_useful_ms": 120000,
      "notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
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  },
  "endpoints": {
    "web": "https://www.openagentskill.com/skills/40rty-ai-shopify-admin-agentic-metafields-setup",
    "api": "https://www.openagentskill.com/api/agent/skills/40rty-ai-shopify-admin-agentic-metafields-setup",
    "audit": "https://www.openagentskill.com/skills/40rty-ai-shopify-admin-agentic-metafields-setup/audit",
    "eval": "https://www.openagentskill.com/api/agent/evals?slug=40rty-ai-shopify-admin-agentic-metafields-setup&task=Use%20shopify-admin-agentic-metafields-setup%20in%20an%20agent%20workflow&max_risk=medium",
    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20shopify-admin-agentic-metafields-setup%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20shopify-admin-agentic-metafields-setup%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/40rty-ai-shopify-admin-agentic-metafields-setup/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/40rty-ai-shopify-admin-agentic-metafields-setup"
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}

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Créateur: 40RTY-ai
Source: 40RTY-ai/shopify-admin-skills
Indexé par: Index communautaire OpenAgentSkill

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