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/Sahib-Sawhney-WH/sahibs-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/sahib-sawhney-wh/sahibs-claude-plugin-marketplace/agent)<a href="https://agentmods.dev/commands/sahib-sawhney-wh/sahibs-claude-plugin-marketplace/agent"><img src="https://agentmods.dev/badge/commands/sahib-sawhney-wh/sahibs-claude-plugin-marketplace/agent/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/sahib-sawhney-wh/sahibs-claude-plugin-marketplace/agent"><img src="https://agentmods.dev/badge/commands/sahib-sawhney-wh/sahibs-claude-plugin-marketplace/agent.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 | $0.00014 | $0.01229 |
| Opus 5 | $0.00007 | $0.00615 |
| Sonnet 5 | $0.00003 | $0.00246 |
| Haiku 4.5 | $0.00001 | $0.00123 |
Grade A, and why
agent 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 — 219 lines — stays where its author put it; the contents beside it link to each section on GitHub.
DAPR Agent Generator
Create intelligent, durable AI agents powered by LLMs using the DAPR Agents framework.
Behavior
When the user runs /dapr:agent:
-
Determine Agent Type
- Assistant: Basic LLM-powered agent with tools
- Durable: Workflow-backed agent with fault tolerance
- Service: Headless agent exposed via REST API
- Multi: Multi-agent orchestration system
-
Generate Agent Code
- Create agent class with LLM integration
- Add tool definitions with @tool decorator
- Configure memory management
- Set up Dapr components
-
Create Supporting Files
- requirements.txt with dapr-agents dependencies
- Component YAML for state/conversation
- Dockerfile for containerization
Arguments
| Argument | Description |
|---|---|
assistant <name> |
Create a basic AssistantAgent |
durable <name> |
Create a workflow-backed DurableAgent |
service <name> |
Create a headless AgentService |
multi <name> |
Create a multi-agent system |
--tools |
Comma-separated list of tools to include |
--memory |
Memory type: short-term, long-term, vector |
--llm |
LLM provider: openai, azure, anthropic, ollama |
Examples
Basic Assistant Agent
/dapr:agent assistant weather-bot
Creates an agent that can:
- Process user queries
- Call tools dynamically
- Maintain conversation context
Durable Agent with Tools
/dapr:agent durable order-processor --tools "inventory,payment,shipping"
Creates a fault-tolerant agent with:
- Workflow-backed execution
- Automatic retry on failures
- Persistent state across restarts
Headless Agent Service
/dapr:agent service research-agent --memory vector
Creates a REST API agent with:
- HTTP endpoints for queries
- Vector memory for RAG
- Long-running task support
Multi-Agent System
/dapr:agent multi customer-support --agents "triage,technical,billing"
Creates coordinated agents with:
- Pub/sub communication
- Workflow orchestration
- Specialized agent roles
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 · 219 lines · 14 tokens per session scan A f965284f283a
agent is a command published in the GitHub repository Sahib-Sawhney-WH/sahibs-claude-plugin-marketplace (4 stars, last pushed 8mo ago), licensed MIT. It adds 14 tokens to every session and 1,229 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 2026-08-31.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.