Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/JosiahSiegel/claude-plugin-marketplaceWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/commands/josiahsiegel/claude-plugin-marketplace/adf-create-pipeline)<a href="https://agentmods.dev/commands/josiahsiegel/claude-plugin-marketplace/adf-create-pipeline"><img src="https://agentmods.dev/badge/commands/josiahsiegel/claude-plugin-marketplace/adf-create-pipeline/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/commands/josiahsiegel/claude-plugin-marketplace/adf-create-pipeline"><img src="https://agentmods.dev/badge/commands/josiahsiegel/claude-plugin-marketplace/adf-create-pipeline.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00020 | $0.00911 |
| Opus 5 | $0.00010 | $0.00456 |
| Sonnet 5 | $0.00004 | $0.00182 |
| Haiku 4.5 | $0.00002 | $0.00091 |
Grade A, and why
adf-create-pipeline scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 125 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ADF Pipeline Generator
Generate Azure Data Factory pipeline JSON files following best practices and avoiding common pitfalls.
Task
Create a complete, valid ADF pipeline JSON based on the provided requirements.
Arguments
$ARGUMENTS: Pipeline name followed by requirements description- Example:
PL_SalesETL Copy sales data from Azure SQL to Parquet in ADLS, partitioned by date - Example:
PL_DailyLoad Load multiple tables using ForEach with config lookup - Example:
PL_APIIngestion Fetch data from REST API with pagination and error handling
- Example:
Pipeline Generation Rules
Required Structure
{
"name": "<PipelineName>",
"properties": {
"activities": [],
"parameters": {},
"variables": {},
"annotations": [],
"folder": { "name": "<FolderName>" }
}
}
Naming Conventions
- Pipelines:
PL_<Domain>_<Action>(e.g.,PL_Sales_DailyLoad) - Activities:
<Type>_<Purpose>(e.g.,Copy_SalesToParquet,ForEach_Tables) - Variables:
var<Name>(e.g.,varCounter,varResults) - Parameters:
<Name>(e.g.,ProcessDate,TableList)
Activity Nesting Rules
NEVER create prohibited combinations:
- ForEach cannot contain: ForEach, Until, Validation
- IfCondition cannot contain: ForEach, If, Switch, Until, Validation
- Switch cannot contain: ForEach, If, Switch, Until, Validation
- Until cannot contain: Until, ForEach, Validation
If nested control flow is required, use Execute Pipeline pattern.
Best Practices to Apply
- Always include retry policy for Copy, Web, and external activities
- Use parameters for environment-specific values (servers, databases, paths)
- Add dependsOn with explicit dependency conditions
- Include timeout values appropriate for the operation
- Use Key Vault references for secrets (never hardcode)
- Add annotations for documentation and tagging
- Set secureInput/secureOutput when handling sensitive data
Common Patterns
Copy with Lookup Config:
{
"activities": [
{
"name": "Lookup_GetTables",
"type": "Lookup",
"typeProperties": {
"source": { "type": "AzureSqlSource", "sqlReaderQuery": "SELECT * FROM Config.Tables" },
"dataset": { "referenceName": "DS_Config", "type": "DatasetReference" },
"firstRowOnly": false
}
},
{
"name": "ForEach_Tables",
"type": "ForEach",
"dependsOn": [{ "activity": "Lookup_GetTables", "dependencyConditions": ["Succeeded"] }],
"typeProperties": {
"items": { "value": "@activity('Lookup_GetTables').output.value", "type": "Expression" },
"isSequential": false,
"batchCount": 20,
"activities": [...]
}
}
]
}
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 125 lines · 20 tokens per session scan A e305991f627b
adf-create-pipeline is a command published in the GitHub repository JosiahSiegel/claude-plugin-marketplace (54 stars, last pushed 2mo ago), licensed MIT. It adds 20 tokens to every session and 911 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other commands, from other repositories
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Build AI features with the first-party Laravel AI SDK (Laravel 13+); use the laravel:ai-sdk skill exactly as written.
audit-prompt
Evaluate an existing prompt for clarity, effectiveness, and edge cases.
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Generates a detailed image generation prompt from a document or content description. Good output: a prompt that is specific, visual, non-abstract, includes style/composition/lighting guidance, and is calibrated to the specified dimensions and style options.
dare-llm-integration
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prompt-create
Create a new prompt following ground rules.