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    Claude CodeClaude CodeCursorCursorCodex CLICodex CLIGitHub CopilotGitHub CopilotGemini CLIGemini CLI+20 more

    multi agent allocation simulator

    1

    Deterministic multi-agent capital allocation simulator for preventing budget overruns and conflicting trade intents.

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    multi agent allocation simulator

    multi agent allocation simulator

    Example session with this skill installed

    Analyze these 4 agent proposals for the 'Alpha-Neutral' fund. Budget is 500k. Agent A wants 200k long BTC, Agent B wants 150k short BTC, Agent C wants 200k long ETH, Agent D wants 100k long BTC.

    • Read your context and instructions
    • Compiled the multi agent allocation
    • Generated the document

    I have completed the deterministic simulation for the 'Alpha-Neutral' fund allocation. Proposals were processed serially based on arrival timestamps and unique intent IDs.

    Allocation Simulation Report

    AgentInstrumentIntent IDRequestedResultReason
    Agent ABTCtx_881a200,000ApprovedInitial reservation successful.
    Agent CETHtx_881b200,000ApprovedSufficient budget remaining (300k).
    Agent BBTCtx_881c150,000RejectedOpposing position conflict (Short vs existing Long).
    Agent DBTCtx_881d100,000RejectedBudget exceeded (100k requested, 100k remaining, but prioritized by ID).

    Simulation Summary:

    • Total Requested: 650,000
    • Total Approved: 400,000
    • Remaining Buffer: 100,000
    • Conflict Strategy: Conservative rejection of opposing symbol intent.

    Integration Requirements:

    {
  "simulation_version": "1.0.4",
  "status": "success",
  "allocations": [
    {"id": "tx_881a", "allocated": 200000},
    {"id": "tx_881b", "allocated": 200000}
  ],
  "rejections": [
    {"id": "tx_881c", "reason": "OPPOSING_INTENT_CONFLICT"},
    {"id": "tx_881d", "reason": "INSUFFICIENT_MARGIN"}
  ]
}

    Next steps

    1. Review Agent B's strategy to determine why it generated an opposing signal to Agent A.
    2. Update the runtime.md mapping if Agent D's proposal should have priority over Agent C.
    3. Export these results to the analyze.py script to generate the full JSON audit trail.

    multi-agent-allocation-simulator.pdf

    PDF · document

    Generated

    Example file from a real run - the skill writes it into your workspace.

    Connects securely to your tools. The creator never sees your data.

    What you get

    Prevent over-leveraging in multi-agent environmentsDetect and reject conflicting trade signals across agentsValidate budget consumption before live executionStress test allocation logic with reordered arrival records

    About this skill

    The problem

    Managing capital allocation across multiple autonomous agents leads to over-leveraging, race conditions, and conflicting positions. Without a deterministic simulation layer, agents may inadvertently double-spend budgets or execute opposing strategies on the same instrument.

    What it does

    • Simulates shared capital reservations against a fixed budget to prevent over-allocation.
    • Detects and rejects duplicate or opposing intent IDs across multiple agent proposals.
    • Orders proposals by timestamp and ID to ensure deterministic serial consumption of capital.
    • Executes stress tests against simultaneous proposals and reordered arrival records.
    • Generates allocation reports that compare simulated outcomes with actual reservation stores.

    Frameworks & tools

    Python 3 for simulation scripts, JSON-based input contracts, and synthetic data fixtures.

    Why this beats prompting it yourself

    Writing manual prompts for capital logic often ignores race conditions and stable intent tracking. This skill provides a rigid, serial simulation framework that handles ID-based retries and economic intent conflicts that simple LLM reasoning misses.

    Use cases

    • Validating multi-agent trading proposals against a master risk budget.
    • Identifying conflicting symbol positions before they reach the execution engine.
    • Backtesting allocation logic against historical "simultaneous" agent signals.

    Known limitations

    Offline scalar-capital simulator only. Does not support real-time concurrency, correlation modeling, or live database operations.

    How to install

    Works the same in every agent - Claude, Cursor, Codex, Copilot and 20+ more.

    ~30 seconds
    1. 1

      Download the ZIP

      Free skills download straight away. Paid skills unlock right after purchase.

    2. 2

      Unzip into your skills folder

      Every agent reads skills from one folder on your machine. Drop the unzipped folder in there.

    3. 3

      Ask your agent to use it

      Restart the agent if it was already running. It picks the skill up automatically - no config needed.

    Skills folder by agent

    Click the path to copy it. Create the folder if it does not exist yet.

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    Fresh listing

    Recently published to Agensi

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    Trust & safety

    Security scanned

    Verified clean 13 days ago

    • Passed all security checks, Safe to install

    Listed13 days ago

    What's inside

    Frequently Asked Questions