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shopify-admin-agentic-product-taxonomy

Assign every product a Shopify Standard Product Taxonomy category so AI agents can map a shopper's intent to the right category and surface the store's products.

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Preis unbestätigt★ 182 GitHub-StarsVerzeichnis aktualisiert · 4. Sept. 2026agent-skill

Übersicht

Assign every product a Shopify Standard Product Taxonomy category so AI agents can map a shopper's intent to the right category and surface the store's products.

Vollständige Dokumentation lesen

Quelldokumentation, keine Anweisungen für diese Website. Vor dem Ausführen von Befehlen die Berechtigungen prüfen.

Purpose

AI shopping agents resolve a query ("running shoes", "office chair", "sustainable sneakers") to a taxonomy node, then retrieve products in that node. Products with no Standard Product Taxonomy category are invisible to that mapping — they only surface on exact keyword luck. This skill finds uncategorized (or mis-categorized) products and assigns the correct Shopify standard taxonomy category, inferred from title/type/tags and confirmed against the live taxonomy tree. Fixes category-taxonomy-api and lifts catalog-intent-alignment.

Prerequisites

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

Parameters

All skills accept these universal parameters:

ParameterTypeRequiredDefaultDescription
storestringyes—Store domain (e.g., mystore.myshopify.com)
formatstringnohumanOutput format: human (default) or json
dry_runboolnofalsePreview mutations without executing

Skill-specific parameters:

ParameterTypeRequiredDefaultDescription
collection_idstringno—Limit to a collection GID
tagstringno—Limit to a product tag
only_missingboolnotrueIf true, only assign products with no category; if false, also review mismatches
confidence_floorfloatno0.7Skip products whose best taxonomy match scores below this

Safety

⚠️ Step 4 (productUpdate) sets the category on live products, which affects storefront facets, marketplaces, and tax. A wrong category mis-files a product everywhere. Run dry_run: true, review the proposed product → category mapping, and only auto-assign matches above confidence_floor; queue the rest for human review.

Workflow Steps

  1. OPERATION: products — query Inputs: first: 250, optional filter; fields title, productType, tags, category{ id fullName }; paginate. Expected output: Products with their current category (or null).

  2. OPERATION: taxonomy — query Inputs: search the standard taxonomy tree by candidate terms derived from each product's type/title. Expected output: Candidate taxonomy category nodes (id + fullName) to match against.

  3. COMPUTE (no API): score each product against candidate nodes; pick the best ≥ confidence_floor. Emit the proposed mapping.

  4. OPERATION: productUpdate — mutation Inputs: { id, category: <taxonomyCategoryId> } per confident match. Expected output: Updated product category; collect userErrors.

GraphQL Operations

# products:query — validated against api_version 2025-01
query TaxonomyProducts($first: Int!, $after: String, $query: String) {
  products(first: $first, after: $after, query: $query) {
    edges {
      node {
        id
        title
        productType
        tags
        category { id fullName }
      }
    }
    pageInfo { hasNextPage endCursor }
  }
}
# taxonomy:query — validated against api_version 2025-01
query TaxonomySearch($search: String) {
  taxonomy {
    categories(first: 20, search: $search) {
      edges { node { id fullName isLeaf level } }
    }
  }
}
# productUpdate:mutation — validated against api_version 2025-01
mutation TaxonomyAssign($input: ProductInput!) {
  productUpdate(input: $input) {
    product { id category { id fullName } }
    userErrors { field message }
  }
}

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: count categorized + a CSV (product, old_category, new_category, confidence) and a "needs review" list below the floor. json: { categorized, needs_review, errors, output_file }.

Error Handling

ErrorCauseRecovery
THROTTLEDAPI rate limitWait 2s, retry up to 3 times
No taxonomy matchNiche/ambiguous productAdd to needs-review list, do not guess
userErrors on updateInvalid category idLog, skip, continue

Best Practices

  • Pick the most specific (leaf) node you're confident in — agents match deeper categories more precisely than top-level ones.
  • Keep only_missing: true for the first pass; re-categorizing existing assignments is higher-risk and best reviewed.
  • Aligning the category with your storefront collections compounds the benefit: agents and on-site filters then agree.
  • Re-run shopify-admin-agentic-readiness-audit to confirm the Matchable pillar improved.
Dateimetadaten
name: shopify-admin-agentic-product-taxonomy
role: agentic
description: "Assign every product a Shopify Standard Product Taxonomy category so AI agents can map a shopper's intent to the right category and surface the store's products."
toolkit: shopify-admin, shopify-admin-execution
api_version: "2025-01"
graphql_operations:
  - products:query
  - taxonomy:query
  - productUpdate:mutation
status: stable
compatibility: Claude Code, Cursor, Codex, Gemini CLI
audit_signals:
  - category-taxonomy-api
  - catalog-intent-alignment
Originaltext anzeigen
---
name: shopify-admin-agentic-product-taxonomy
role: agentic
description: "Assign every product a Shopify Standard Product Taxonomy category so AI agents can map a shopper's intent to the right category and surface the store's products."
toolkit: shopify-admin, shopify-admin-execution
api_version: "2025-01"
graphql_operations:
  - products:query
  - taxonomy:query
  - productUpdate:mutation
status: stable
compatibility: Claude Code, Cursor, Codex, Gemini CLI
audit_signals:
  - category-taxonomy-api
  - catalog-intent-alignment
---

## Purpose
AI shopping agents resolve a query ("running shoes", "office chair", "sustainable sneakers") to a taxonomy node, then retrieve products in that node. Products with no Standard Product Taxonomy category are invisible to that mapping — they only surface on exact keyword luck. This skill finds uncategorized (or mis-categorized) products and assigns the correct Shopify standard taxonomy category, inferred from title/type/tags and confirmed against the live taxonomy tree. Fixes `category-taxonomy-api` and lifts `catalog-intent-alignment`.

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

## 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 |
|-----------|------|----------|---------|-------------|
| collection_id | string | no | — | Limit to a collection GID |
| tag | string | no | — | Limit to a product tag |
| only_missing | bool | no | true | If true, only assign products with no category; if false, also review mismatches |
| confidence_floor | float | no | 0.7 | Skip products whose best taxonomy match scores below this |

## Safety

> ⚠️ Step 4 (`productUpdate`) sets the `category` on live products, which affects storefront facets, marketplaces, and tax. A wrong category mis-files a product everywhere. Run `dry_run: true`, review the proposed `product → category` mapping, and only auto-assign matches above `confidence_floor`; queue the rest for human review.

## Workflow Steps

1. **OPERATION:** `products` — query
   **Inputs:** `first: 250`, optional filter; fields `title`, `productType`, `tags`, `category{ id fullName }`; paginate.
   **Expected output:** Products with their current category (or null).

2. **OPERATION:** `taxonomy` — query
   **Inputs:** search the standard taxonomy tree by candidate terms derived from each product's type/title.
   **Expected output:** Candidate taxonomy category nodes (id + fullName) to match against.

3. **COMPUTE (no API):** score each product against candidate nodes; pick the best ≥ `confidence_floor`. Emit the proposed mapping.

4. **OPERATION:** `productUpdate` — mutation
   **Inputs:** `{ id, category: <taxonomyCategoryId> }` per confident match.
   **Expected output:** Updated product category; collect `userErrors`.

## GraphQL Operations

```graphql
# products:query — validated against api_version 2025-01
query TaxonomyProducts($first: Int!, $after: String, $query: String) {
  products(first: $first, after: $after, query: $query) {
    edges {
      node {
        id
        title
        productType
        tags
        category { id fullName }
      }
    }
    pageInfo { hasNextPage endCursor }
  }
}
```

```graphql
# taxonomy:query — validated against api_version 2025-01
query TaxonomySearch($search: String) {
  taxonomy {
    categories(first: 20, search: $search) {
      edges { node { id fullName isLeaf level } }
    }
  }
}
```

```graphql
# productUpdate:mutation — validated against api_version 2025-01
mutation TaxonomyAssign($input: ProductInput!) {
  productUpdate(input: $input) {
    product { id category { id fullName } }
    userErrors { field message }
  }
}
```

## 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`: count categorized + a CSV (`product, old_category, new_category, confidence`) and a "needs review" list below the floor. `json`: `{ categorized, needs_review, errors, output_file }`.

## Error Handling
| Error | Cause | Recovery |
|-------|-------|----------|
| `THROTTLED` | API rate limit | Wait 2s, retry up to 3 times |
| No taxonomy match | Niche/ambiguous product | Add to needs-review list, do not guess |
| `userErrors` on update | Invalid category id | Log, skip, continue |

## Best Practices
- Pick the most specific (leaf) node you're confident in — agents match deeper categories more precisely than top-level ones.
- Keep `only_missing: true` for the first pass; re-categorizing existing assignments is higher-risk and best reviewed.
- Aligning the category with your storefront collections compounds the benefit: agents and on-site filters then agree.
- Re-run `shopify-admin-agentic-readiness-audit` to confirm the Matchable pillar improved.

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Lizenz: MIT

  • Dependency or permission surface needs review
  • Permission surface may require sandboxing
  • 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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Tools sind Metadatenhinweise, keine getestete Kompatibilität. Prompts sind Vorschläge.

Mit einer kleinen Aufgabe beginnen

  1. 1Quelle lesen und Eingaben, Ergebnisse, Abhängigkeiten sowie Berechtigungen prüfen.
  2. 2Agent um einen Plan bitten. Einrichtung und Kosten vor einem isolierten Test genehmigen.
  3. 3Ergebnisse und geänderte Dateien prüfen. Nur tatsächliche Ausführungen melden und die Quellrevision aufbewahren.

Prüfe Abhängigkeiten, API-Schlüssel und externe Kosten in der Quelle. Öffentliche Repositories bedeuten nicht, dass alle Dienste kostenlos sind.

Quelle und Nutzungshinweise

Erfasst

Metadaten und Prüfungen dienen der Orientierung. Beliebtheit, Quellenerfassung und erfolgreiche Ausführung sind verschiedene Fakten.

Quell-Repository
40RTY-ai/shopify-admin-skills
Lizenz
MIT
Version
1.0.0
Letzter GitHub-Push
14. Aug. 2026
Verzeichnis aktualisiert
4. Sept. 2026

Version aus den Verzeichnismetadaten; Releases der Quelle prüfen.

Qualität

66/100

Vielversprechend

Vertrauen

62/100

Nur Sandbox

Audit

75/100

Prüfung nötig

  • Dependency or permission surface needs review
  • Permission surface may require sandboxing
  • 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
Verified installs
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Kopieren ist keine Installation. Zahlen benötigen eine Erfolgsmeldung und garantieren keine allgemeine Qualität.

Agent-Zugang

Die Registry API stellt Entscheidungs-, Vertrauens-, Audit-, Use-Case- und Installationssignale ohne UI-Scraping bereit.

Weitere Details
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    "slug": "40rty-ai-shopify-admin-agentic-product-taxonomy",
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    "description": "Assign every product a Shopify Standard Product Taxonomy category so AI agents can map a shopper's intent to the right category and surface the store's products.",
    "category": "automation",
    "url": "https://www.openagentskill.com/skills/40rty-ai-shopify-admin-agentic-product-taxonomy",
    "repository": "https://github.com/40RTY-ai/shopify-admin-skills/tree/main/skills/agentic/shopify-admin-agentic-product-taxonomy",
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        "id": "cursor",
        "label": "Cursor",
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      }
    ],
    "handoff_url": "https://www.openagentskill.com/api/skills/40rty-ai-shopify-admin-agentic-product-taxonomy/install",
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      "repository": "https://github.com/40RTY-ai/shopify-admin-skills/tree/main/skills/agentic/shopify-admin-agentic-product-taxonomy",
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      "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",
      "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"
    ]
  },
  "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."
  },
  "quality": {
    "score": 66,
    "label": "Promising"
  },
  "supply": {
    "track": "Coding and developer agents",
    "scenario": "Browser automation",
    "maintenance": "2mo since push",
    "risk": "Needs review"
  },
  "alternative_skills": [],
  "do_not_use_when": [
    "teams that need a vendor-supported SLA",
    "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",
    "Quality score needs review",
    "Permission surface needs review: secrets or environment access, shell or command execution"
  ],
  "agent_contract": {
    "task_input": "Use shopify-admin-agentic-product-taxonomy 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-product-taxonomy (shopify-admin-agentic-product-taxonomy)",
      "install_command": "npx skills add 40RTY-ai/shopify-admin-skills --skill shopify-admin-agentic-product-taxonomy",
      "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."
    }
  },
  "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"
    ],
    "payload_template": {
      "event_id": "<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>",
      "skill_slug": "40rty-ai-shopify-admin-agentic-product-taxonomy",
      "task": "Use shopify-admin-agentic-product-taxonomy in an agent workflow",
      "agent": "codex",
      "outcome": "success",
      "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."
    }
  },
  "endpoints": {
    "web": "https://www.openagentskill.com/skills/40rty-ai-shopify-admin-agentic-product-taxonomy",
    "api": "https://www.openagentskill.com/api/agent/skills/40rty-ai-shopify-admin-agentic-product-taxonomy",
    "audit": "https://www.openagentskill.com/skills/40rty-ai-shopify-admin-agentic-product-taxonomy/audit",
    "eval": "https://www.openagentskill.com/api/agent/evals?slug=40rty-ai-shopify-admin-agentic-product-taxonomy&task=Use%20shopify-admin-agentic-product-taxonomy%20in%20an%20agent%20workflow&max_risk=medium",
    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20shopify-admin-agentic-product-taxonomy%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20shopify-admin-agentic-product-taxonomy%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/40rty-ai-shopify-admin-agentic-product-taxonomy/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/40rty-ai-shopify-admin-agentic-product-taxonomy"
  }
}

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Ersteller
40RTY-ai
Indexiert von
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