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    Repair AI Tool Calling, Actions & Side Effects

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    Repair AI Tool Calling, Actions & Side Effects is a bounded ToolForge Labs workflow.

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    Repair AI Tool Calling, Actions & Side Effects

    Repair AI Tool Calling, Actions & Side Effects

    Example session with this skill installed

    An AI agent can call create_ticket, refund_order, and send_email. We found cases where a tool retry sends two emails, stale tool arguments are accepted after a schema change, and a refund action can be attempted before the user-approval step is recorded. Repair the tool-calling contract without granting the model any new authority.

    • Read your context and instructions
    • Compiled the repair ai tool

    The likely issue is a contract mismatch around AI tool selection, argument schemas, action authorization, idempotency, retries, approval gates, side effects, tool errors, result binding, and multi-step agent state. I would first define the invariant: The model may propose an action, but only validated arguments plus current application-side authorization/approval may execute it, and each externally visible side effect must have one durable idempotency identity. Then I would trace the smallest owning boundary, apply or propose a bounded repair/review step, and add deterministic normal, boundary, failure and replay tests. The workflow preserves existing user approvals, authorization policy, destructive-action boundaries, billing rules, and external side-effect controls. If repository/workspace files are available, the findings can be mapped to exact paths and focused local validation; with partial evidence, the result stays an explicit diagnosis/evidence map with assumptions and no invented execution claims.

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

    About this skill

    Repair AI Tool Calling, Actions & Side Effects is a bounded ToolForge Labs workflow. Diagnose, review, and repair AI tool selection, argument schemas, action authorization, idempotency, retries, approval gates, side effects, tool errors, result binding, and multi-step agent state. Works from full repositories/workspaces or partial evidence. Produces bounded fixes or evidence gates with deterministic validation while preserving existing product/security policy and avoiding invented execution claims. It works from a full repository/workspace or partial evidence such as snippets, logs, configuration, traces, documents, tickets, and architecture notes. The skill separates verified facts from assumptions, avoids invented execution claims, and produces concrete repair/review steps with focused deterministic validation or traceable evidence.

    Find more skills like this on the Claude Skills Marketplace, the Codex Skills Marketplace and the Gemini Skills Marketplace, or browse Claude Skills for Agents & Orchestration.

    Requires repository access or specific logs/snippets. Does not perform live production execution or bypass existing authorization policies.

    How to use

    1. 1

      Connect your agent to the Agensi MCP

      Copy the prompt and paste it into Claude, ChatGPT, Cursor or any agent that supports MCP. It sets up the connection for you. Manual setup

    2. 2

      Sign in to Agensi

      Your agent shows a sign-in link. Sign in and click Allow.

    3. 3

      Ask for what you need

      Your agent loads this skill and uses it on your task. It always asks before buying a skill.

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    Permissions

    Terminal / Shell
    Read Files
    Write Files

    Use a clean branch or controlled review workspace. Supply repository/workspace files, logs, traces, configuration, documents, or evidence needed for the task. Review patches/findings before merge or operational action; keep production credentials and destructive production actions out of the workflow.

    Listed1 month ago
    Updated9 days ago

    What's inside

    Frequently Asked Questions

    This skill identifies and fixes failures in how AI agents interact with external systems, addressing issues like incorrect API argument formatting, broken tool-call loops, failed authorization logic, and state management errors during multi-step tasks.

    It is highly flexible and works with full codebases or partial data such as logs, trace exports, and configuration files, making it compatible with TypeScript-based agents and any framework that utilizes tool-calling schemas.

    Unlike generic debuggers, this skill focuses on 'deterministic validation,' meaning it produces concrete code fixes and evidence gates that adhere to your existing security policies without hallucinating execution results.

    You receive the full ToolForge Labs workflow designed to diagnose tool-calling errors, along with structured templates for reviewing side effects, idempotency checks, and agent state transitions.

    The skill is designed to be plug-and-play within the ToolForge ecosystem; you simply provide access to your repository or paste relevant execution traces to begin the diagnostic and repair process.

    New in Agents & Orchestration