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40RTY-ai

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shopify-admin-discount-ab-analysis

Compare redemption rates and revenue performance across two or more discount codes over a specified date range.

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

Vue d’ensemble

Compare redemption rates and revenue performance across two or more discount codes over a specified date range.

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Documentation source, pas des instructions pour ce site. Vérifiez les permissions avant d’exécuter des commandes.

Purpose

Compares how different discount codes perform against each other by redemption count and revenue generated. Useful for A/B testing promotional offers without a dedicated analytics app — provide two or more codes and a date range, and the skill queries Shopify for discount metadata and order revenue, then produces a side-by-side comparison table. Read-only: no mutations are executed.

Prerequisites

  • Authenticated Shopify CLI session: shopify auth login --store <domain>
  • API scopes: read_discounts, read_orders

Parameters

ParameterTypeRequiredDefaultDescription
storestringyes—Store domain (e.g., mystore.myshopify.com)
formatstringnohumanOutput format: human or json
dry_runboolnofalsePreview operations without executing mutations
discount_codesarrayyes—Array of 2 or more discount code strings to compare (e.g., ["SAVE10", "WELCOME15"])
date_range_startstringyes—Start date in ISO 8601 (e.g., 2025-01-01)
date_range_endstringyes—End date in ISO 8601 (e.g., 2025-01-31)

Workflow Steps

  1. OPERATION: discountNodes — query Inputs: first: 50, query: "code:<code>" (one query per code in discount_codes) Expected output: Discount metadata: title, code strings, asyncUsageCount, status, startsAt, endsAt per code

  2. OPERATION: orders — query (one paginated query per discount code) Inputs: first: 250, query: "discount_code:<code> created_at:>='<date_range_start>' created_at:<='<date_range_end>'", pagination cursor Expected output: Orders containing the discount code with totalPriceSet; paginate until hasNextPage: false; aggregate: count, sum revenue, compute avg order value

GraphQL Operations

# discountNodes:query — validated against api_version 2025-01
query DiscountNodes($first: Int!, $query: String) {
  discountNodes(first: $first, query: $query) {
    edges {
      node {
        id
        discount {
          ... on DiscountCodeBasic {
            title
            codes(first: 10) {
              edges {
                node {
                  code
                  asyncUsageCount
                }
              }
            }
            usageLimit
            status
            startsAt
            endsAt
          }
          ... on DiscountCodeBxgy {
            title
            codes(first: 10) {
              edges {
                node {
                  code
                  asyncUsageCount
                }
              }
            }
            status
          }
          ... on DiscountCodeFreeShipping {
            title
            codes(first: 10) {
              edges {
                node {
                  code
                  asyncUsageCount
                }
              }
            }
            status
          }
        }
      }
    }
  }
}

# orders:query (by discount code) — validated against api_version 2025-01
query OrdersByDiscountCode($first: Int!, $after: String, $query: String) {
  orders(first: $first, after: $after, query: $query) {
    edges {
      node {
        id
        createdAt
        totalPriceSet {
          shopMoney { amount currencyCode }
        }
        discountCodes
      }
    }
    pageInfo {
      hasNextPage
      endCursor
    }
  }
}

Session Tracking

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

On start, emit:

╔══════════════════════════════════════════════╗
║  SKILL: discount-ab-analysis                 ║
║  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>

On completion, emit:

For format: human (default):

══════════════════════════════════════════════
OUTCOME SUMMARY
  Codes analyzed:   <n>
  Date range:       <start> to <end>
  Errors:           0
  Output:           none
══════════════════════════════════════════════

For format: json, emit:

{
  "skill": "discount-ab-analysis",
  "store": "<domain>",
  "started_at": "<ISO8601>",
  "completed_at": "<ISO8601>",
  "dry_run": false,
  "steps": [
    { "step": 1, "operation": "DiscountNodes", "type": "query", "params_summary": "<n> codes queried", "result_summary": "<n> discount nodes found", "skipped": false },
    { "step": 2, "operation": "OrdersByDiscountCode", "type": "query", "params_summary": "date range <start> to <end>", "result_summary": "<n> orders aggregated", "skipped": false }
  ],
  "outcome": {
    "codes_analyzed": 0,
    "date_range_start": "<start>",
    "date_range_end": "<end>",
    "results": [
      {
        "code": "SAVE10",
        "async_usage_count": 0,
        "orders_in_range": 0,
        "total_revenue": "0.00",
        "avg_order_value": "0.00",
        "revenue_per_use": "0.00"
      }
    ],
    "errors": 0,
    "output_file": null
  }
}

Output Format

A comparison table per code (displayed inline):

CodeUses (asyncUsageCount)Orders in RangeTotal RevenueAvg Order ValueRevenue per Use
SAVE10...............
WELCOME15...............

For format: json, the results array contains one object per code with keys: code, async_usage_count, orders_in_range, total_revenue, avg_order_value, revenue_per_use.

Error Handling

ErrorCauseRecovery
Discount code not foundCode doesn't exist or was deletedVerify code in Shopify admin
No orders returned for a codeNo orders used this code in the date rangeWiden date range or verify code was active
discount_codes has fewer than 2 entriesCan't do A/B with 1 codeProvide at least 2 codes
Rate limit (429)Too many paginated orders queriesWait and retry; reduce date range

Best Practices

  1. asyncUsageCount is the lifetime usage count from the discount object — orders_in_range is what was redeemed in your date window. Both are reported for full context.
  2. For codes with high usage, the orders query will paginate — larger date ranges may produce many API calls. Consider narrowing the date range for faster results.
  3. Revenue per use is the best signal for comparing codes with different usage volumes.
  4. Run this analysis at the end of a campaign period before deciding which discount strategy to repeat.
  5. If asyncUsageCount is 0 for a code, check that the code was active during the date range and correctly applied at checkout.
Voir la source ↗
Métadonnées du fichier
name: shopify-admin-discount-ab-analysis
role: conversion-optimization
description: "Compare redemption rates and revenue performance across two or more discount codes over a specified date range."
toolkit: shopify-admin, shopify-admin-execution
api_version: "2025-01"
graphql_operations:
  - discountNodes:query
  - orders:query
status: stable
compatibility: Claude Code, Cursor, Codex, Gemini CLI
Voir le texte original
---
name: shopify-admin-discount-ab-analysis
role: conversion-optimization
description: "Compare redemption rates and revenue performance across two or more discount codes over a specified date range."
toolkit: shopify-admin, shopify-admin-execution
api_version: "2025-01"
graphql_operations:
  - discountNodes:query
  - orders:query
status: stable
compatibility: Claude Code, Cursor, Codex, Gemini CLI
---

## Purpose
Compares how different discount codes perform against each other by redemption count and revenue generated. Useful for A/B testing promotional offers without a dedicated analytics app — provide two or more codes and a date range, and the skill queries Shopify for discount metadata and order revenue, then produces a side-by-side comparison table. Read-only: no mutations are executed.

## Prerequisites
- Authenticated Shopify CLI session: `shopify auth login --store <domain>`
- API scopes: `read_discounts`, `read_orders`

## Parameters

| Parameter | Type | Required | Default | Description |
|-----------|------|----------|---------|-------------|
| store | string | yes | — | Store domain (e.g., mystore.myshopify.com) |
| format | string | no | human | Output format: `human` or `json` |
| dry_run | bool | no | false | Preview operations without executing mutations |
| discount_codes | array | yes | — | Array of 2 or more discount code strings to compare (e.g., `["SAVE10", "WELCOME15"]`) |
| date_range_start | string | yes | — | Start date in ISO 8601 (e.g., `2025-01-01`) |
| date_range_end | string | yes | — | End date in ISO 8601 (e.g., `2025-01-31`) |

## Workflow Steps

1. **OPERATION:** `discountNodes` — query
   **Inputs:** `first: 50`, `query: "code:<code>"` (one query per code in `discount_codes`)
   **Expected output:** Discount metadata: title, code strings, `asyncUsageCount`, status, `startsAt`, `endsAt` per code

2. **OPERATION:** `orders` — query (one paginated query per discount code)
   **Inputs:** `first: 250`, `query: "discount_code:<code> created_at:>='<date_range_start>' created_at:<='<date_range_end>'"`, pagination cursor
   **Expected output:** Orders containing the discount code with `totalPriceSet`; paginate until `hasNextPage: false`; aggregate: count, sum revenue, compute avg order value

## GraphQL Operations

```graphql
# discountNodes:query — validated against api_version 2025-01
query DiscountNodes($first: Int!, $query: String) {
  discountNodes(first: $first, query: $query) {
    edges {
      node {
        id
        discount {
          ... on DiscountCodeBasic {
            title
            codes(first: 10) {
              edges {
                node {
                  code
                  asyncUsageCount
                }
              }
            }
            usageLimit
            status
            startsAt
            endsAt
          }
          ... on DiscountCodeBxgy {
            title
            codes(first: 10) {
              edges {
                node {
                  code
                  asyncUsageCount
                }
              }
            }
            status
          }
          ... on DiscountCodeFreeShipping {
            title
            codes(first: 10) {
              edges {
                node {
                  code
                  asyncUsageCount
                }
              }
            }
            status
          }
        }
      }
    }
  }
}
```

```graphql
# orders:query (by discount code) — validated against api_version 2025-01
query OrdersByDiscountCode($first: Int!, $after: String, $query: String) {
  orders(first: $first, after: $after, query: $query) {
    edges {
      node {
        id
        createdAt
        totalPriceSet {
          shopMoney { amount currencyCode }
        }
        discountCodes
      }
    }
    pageInfo {
      hasNextPage
      endCursor
    }
  }
}
```

## Session Tracking

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

**On start**, emit:
```
╔══════════════════════════════════════════════╗
║  SKILL: discount-ab-analysis                 ║
║  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>
```

**On completion**, emit:

For `format: human` (default):
```
══════════════════════════════════════════════
OUTCOME SUMMARY
  Codes analyzed:   <n>
  Date range:       <start> to <end>
  Errors:           0
  Output:           none
══════════════════════════════════════════════
```

For `format: json`, emit:
```json
{
  "skill": "discount-ab-analysis",
  "store": "<domain>",
  "started_at": "<ISO8601>",
  "completed_at": "<ISO8601>",
  "dry_run": false,
  "steps": [
    { "step": 1, "operation": "DiscountNodes", "type": "query", "params_summary": "<n> codes queried", "result_summary": "<n> discount nodes found", "skipped": false },
    { "step": 2, "operation": "OrdersByDiscountCode", "type": "query", "params_summary": "date range <start> to <end>", "result_summary": "<n> orders aggregated", "skipped": false }
  ],
  "outcome": {
    "codes_analyzed": 0,
    "date_range_start": "<start>",
    "date_range_end": "<end>",
    "results": [
      {
        "code": "SAVE10",
        "async_usage_count": 0,
        "orders_in_range": 0,
        "total_revenue": "0.00",
        "avg_order_value": "0.00",
        "revenue_per_use": "0.00"
      }
    ],
    "errors": 0,
    "output_file": null
  }
}
```

## Output Format

A comparison table per code (displayed inline):

| Code | Uses (asyncUsageCount) | Orders in Range | Total Revenue | Avg Order Value | Revenue per Use |
|------|------------------------|-----------------|---------------|-----------------|-----------------|
| SAVE10 | ... | ... | ... | ... | ... |
| WELCOME15 | ... | ... | ... | ... | ... |

For `format: json`, the `results` array contains one object per code with keys: `code`, `async_usage_count`, `orders_in_range`, `total_revenue`, `avg_order_value`, `revenue_per_use`.

## Error Handling
| Error | Cause | Recovery |
|-------|-------|----------|
| Discount code not found | Code doesn't exist or was deleted | Verify code in Shopify admin |
| No orders returned for a code | No orders used this code in the date range | Widen date range or verify code was active |
| `discount_codes` has fewer than 2 entries | Can't do A/B with 1 code | Provide at least 2 codes |
| Rate limit (429) | Too many paginated orders queries | Wait and retry; reduce date range |

## Best Practices
1. `asyncUsageCount` is the lifetime usage count from the discount object — `orders_in_range` is what was redeemed in your date window. Both are reported for full context.
2. For codes with high usage, the orders query will paginate — larger date ranges may produce many API calls. Consider narrowing the date range for faster results.
3. Revenue per use is the best signal for comparing codes with different usage volumes.
4. Run this analysis at the end of a campaign period before deciding which discount strategy to repeat.
5. If `asyncUsageCount` is 0 for a code, check that the code was active during the date range and correctly applied at checkout.

Examiner la source

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Licence: 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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  1. 1Lisez la source et confirmez entrées, résultats, dépendances et permissions.
  2. 2Demandez un plan à l’agent. Approuvez la configuration et les coûts avant un test isolé.
  3. 3Vérifiez résultats et fichiers modifiés. Signalez uniquement ce qui a été exécuté et conservez la révision source.

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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/conversion-optimization/shopify-admin-discount-ab-analysis/SKILL.md @ 6765cb4f436b

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Qualité

66/100

Prometteur

Confiance

64/100

Sandbox uniquement

Audit

76/100

Revue nécessaire

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  • 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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      }
    ],
    "handoff_url": "https://www.openagentskill.com/api/skills/40rty-ai-shopify-admin-discount-ab-analysis/install",
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      "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": [
      "research",
      "agent-skill"
    ],
    "known_risks": [
      "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": 76,
    "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": "Research and knowledge work",
    "scenario": "Research agents",
    "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-discount-ab-analysis 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: 72/100 Strong shortlist",
      "Audit: 76/100 Needs review",
      "Safety: 36/100 Avoid automatic install",
      "Review repository, license, install command, and permission surface before production use."
    ],
    "expected_agent_output": {
      "selected_skill": "40rty-ai-shopify-admin-discount-ab-analysis (shopify-admin-discount-ab-analysis)",
      "install_command": "npx skills add 40RTY-ai/shopify-admin-skills --skill shopify-admin-discount-ab-analysis",
      "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-discount-ab-analysis",
      "task": "Use shopify-admin-discount-ab-analysis 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-discount-ab-analysis",
    "api": "https://www.openagentskill.com/api/agent/skills/40rty-ai-shopify-admin-discount-ab-analysis",
    "audit": "https://www.openagentskill.com/skills/40rty-ai-shopify-admin-discount-ab-analysis/audit",
    "eval": "https://www.openagentskill.com/api/agent/evals?slug=40rty-ai-shopify-admin-discount-ab-analysis&task=Use%20shopify-admin-discount-ab-analysis%20in%20an%20agent%20workflow&max_risk=medium",
    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20shopify-admin-discount-ab-analysis%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20shopify-admin-discount-ab-analysis%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/40rty-ai-shopify-admin-discount-ab-analysis/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/40rty-ai-shopify-admin-discount-ab-analysis"
  }
}

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