Credit Mastery

Build and orchestrate multi-agent AI systems using the Swarms API. Use when creating single agents, multi-agent swarms (sequential, concurrent, hierarchical,...

Security Scan

The skill's documentation describes a multi‑agent orchestration platform that requires API keys and Solana wallet private keys and exposes file-ops and delegation capabilities, but the registry metadata claims no credentials or installs — these inconsistencies and the presence of sensitive key-handling instructions warrant caution.

Purpose & Capability

The package is named 'Credit Mastery' in the registry but the SKILL.md and references implement a full 'Swarms' multi-agent orchestration platform. The declared requirements list no credentials or config paths, yet the instructions repeatedly reference an x-api-key and Solana wallet private keys for token launches and ATP — a clear mismatch between stated metadata and actual capability.

Instruction Scope

Runtime instructions instruct the agent (and the user examples) to send API keys and wallet private keys in requests, to enable autonomous mode (max_loops='auto') with sub-agent delegation, and to use tools that include file operations (create/read/update/delete). They also allow arbitrary MCP URLs/headers which could direct data to external endpoints. These instructions go beyond simple API wrappers and create realistic paths for sensitive data exposure or unintended remote operations.

Install Mechanism

This is instruction-only with no install spec and no code files; nothing will be downloaded or written to disk during installation. That reduces on-disk risk, but does not remove runtime/communication risks described in the docs.

Credentials

The registry lists no required environment variables or credentials, but the documentation relies on an API key (x-api-key) and on supplying wallet private keys to endpoints and headers (ATP). Sensitive fields (private_key, wallet_private_key_header) are used in examples without corresponding declared requirements or guidance on safe handling — this is disproportionate and inconsistent.

Persistence & Privilege

The skill is not marked always:true and doesn't request persistent privileges, which is good. However, the instructions encourage autonomous agent modes (max_loops='auto') and enabling tools that perform file operations and create sub-agents — these runtime capabilities can have high impact if used carelessly. The skill itself does not modify other skills or agent configs according to the metadata.

What to consider before installing

Do not assume this skill is safe just because it has no install or declared env vars. Specific things to consider before installing or using it: - Name/metadata mismatch: the registry lists 'Credit Mastery' but the content is the Swarms orchestration docs — ask the publisher to clarify source and purpose and confirm the intended skill. - API keys & private keys: the docs show sending x-api-key and Solana wallet private_key values in requests/headers. Never paste your main wallet private key or high‑privilege API keys into third‑party services. Use testnet wallets and limited-scope API keys for trials. - Missing declared creds: the skill metadata does not declare the API_KEY or ATP env vars that the docs require — that inconsistency is a red flag. Request an updated metadata manifest that lists required env vars and justifies them. - Autonomous/file ops risk: avoid enabling max_loops='auto' or granting 'selected_tools' that include file operations or create_sub_agent unless you trust the backend and understand exactly what files the agents can access. - External endpoints: MCP URLs and facilitator/settlement URLs are arbitrary in examples — verify any external endpoints you point agents at and prefer allowlisting known domains. If you plan to proceed: get provenance (owner/contact/homepage), use limited-scope API keys, test with throwaway wallets on testnet, and do not provide real wallet private keys or high-privilege secrets until you can verify the service operator and source code.

Swarms AI — Multi-Agent Orchestration

Build production-grade multi-agent systems using the Swarms API platform. Supports single agents, reasoning agents, and swarms of 3–10,000+ agents with 20+ architecture patterns.

Quick Reference

• Base URL: https://api.swarms.world
• Auth: x-api-key header with API key from swarms.world/platform/api-keys
• Docs index: https://docs.swarms.ai/llms.txt
• Python SDK: pip install swarms-client
• Marketplace: swarms.world

Architecture Tiers

Tier	Name	Agents	Endpoint
1	Individual Agent	1	/v1/agent/completions
2	Reasoning Agent	1-2 internal	/v1/reasoning-agent/completions
3	Multi-Agent Swarm	3–10,000+	/v1/swarm/completions

Workflow

1. Single Agent

import requests

payload = {
    "agent_config": {
        "agent_name": "MyAgent",
        "description": "Purpose of the agent",
        "system_prompt": "You are...",
        "model_name": "gpt-4o",  # or claude-sonnet-4-20250514, etc.
        "role": "worker",
        "max_loops": 1,
        "max_tokens": 8192,
        "temperature": 0.5,
        "auto_generate_prompt": False,
        "tools_list_dictionary": None
    },
    "task": "Your task here"
}

response = requests.post(
    "https://api.swarms.world/v1/agent/completions",
    headers={"x-api-key": API_KEY, "Content-Type": "application/json"},
    json=payload
)

2. Multi-Agent Swarm

payload = {
    "name": "My Swarm",
    "description": "What this swarm does",
    "agents": [
        {
            "agent_name": "Agent1",
            "description": "Role 1",
            "system_prompt": "You are...",
            "model_name": "gpt-4o",
            "role": "worker",
            "max_loops": 1,
            "max_tokens": 8192,
            "temperature": 0.5
        },
        {
            "agent_name": "Agent2",
            "description": "Role 2",
            "system_prompt": "You are...",
            "model_name": "claude-sonnet-4-20250514",
            "role": "worker",
            "max_loops": 1,
            "max_tokens": 8192,
            "temperature": 0.5
        }
    ],
    "max_loops": 1,
    "swarm_type": "SequentialWorkflow",  # See architecture table
    "task": "Your task here"
}

response = requests.post(
    "https://api.swarms.world/v1/swarm/completions",
    headers={"x-api-key": API_KEY, "Content-Type": "application/json"},
    json=payload
)

3. Token Launch (Solana)

payload = {
    "name": "My Agent Token",
    "description": "Agent description",
    "ticker": "MAG",
    "private_key": "[1,2,3,...]"  # Solana wallet private key
}

response = requests.post(
    "https://swarms.world/api/token/launch",
    headers={"Authorization": "Bearer API_KEY", "Content-Type": "application/json"},
    json=payload
)
# Returns: token_address, pool_address, listing_url
# Cost: ~0.04 SOL

Available Swarm Architectures

Use the swarm_type parameter:

Type	Description	Best For
SequentialWorkflow	Linear pipeline, each agent builds on previous	Step-by-step processing
ConcurrentWorkflow	Parallel execution	Independent tasks, speed
AgentRearrange	Dynamic agent reordering	Adaptive workflows
MixtureOfAgents	Specialist agent selection	Multi-domain tasks
MultiAgentRouter	Intelligent task routing	Large-scale distribution
HierarchicalSwarm	Nested hierarchies with delegation	Complex org structures
MajorityVoting	Consensus across agents	Decision making
BatchedGridWorkflow	Grid pattern execution	Multi-task × multi-agent
GraphWorkflow	Directed graph of agent nodes	Complex dependencies
GroupChat	Agent discussion	Collaborative brainstorming
InteractiveGroupChat	Real-time agent interaction	Dynamic collaboration
AutoSwarmBuilder	Auto-generate optimal swarm	When unsure of architecture
HeavySwarm	High-capacity processing	Large workloads
DebateWithJudge	Structured debate	Adversarial evaluation
RoundRobin	Round-robin distribution	Even load distribution
MALT	Multi-agent learning	Training systems
CouncilAsAJudge	Expert panel evaluation	Quality assessment
LLMCouncil	LM council for decisions	Group decision making
AdvancedResearch	Research workflows	Deep research
auto	Auto-select best type	Default/unknown

Agent Config Parameters

Param	Type	Default	Description
agent_name	string	—	Unique agent identifier
description	string	—	Agent purpose
system_prompt	string	—	Behavior instructions
model_name	string	gpt-4.1	AI model (gpt-4o, claude-sonnet-4-20250514, etc.)
role	string	worker	Agent role in swarm
max_loops	int/string	1	Iterations ("auto" for autonomous)
max_tokens	int	8192	Max response length
temperature	float	0.5	Creativity (0.0–2.0)
auto_generate_prompt	bool	false	Auto-enhance system prompt
tools_list_dictionary	list	—	OpenAPI-style tool definitions
streaming_on	bool	false	Enable SSE streaming
mcp_url	string	—	MCP server URL
selected_tools	list	all safe	Restrict available tools

Rules

• Always use environment variables for API keys — never hardcode.
• Set appropriate max_loops — use "auto" only when sub-agent delegation is needed.
• Match swarm_type to use case (see architecture table).
• For streaming, set streaming_on: true and parse SSE events (metadata → chunks → usage → done).
• Token launches cost ~0.04 SOL from the provided wallet.
• Batch endpoint (/v1/swarm/batch/completions) requires Pro/Ultra/Premium tier.
• Reasoning agents (/v1/reasoning-agent/completions) require Pro+ tier.

Resource Map

Topic	Reference
Full API architecture & tiers	references/architecture.md
Sub-agent delegation patterns	references/sub-agents.md
ATP payment protocol (Solana)	references/atp-protocol.md
Marketplace publishing	references/marketplace.md
Streaming implementation	references/streaming.md
Tools integration	references/tools.md
All docs pages	https://docs.swarms.ai/llms.txt

Read references only when the task requires that specific depth.