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    Business Process Bottleneck Investigator

    1

    Audit agent memories for contradictions, staleness, and provenance to produce a clean, resolved context pack.

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    Business Process Bottleneck Investigator

    Business Process Bottleneck Investigator

    Example session with this skill installed

    Audit these two memory logs for a Python project. Log A: 'Uses Flask 2.0, Dec 2023'. Log B: 'Migrated to FastAPI, Feb 2024'. Generate a resolved context pack.

    • Read your context and instructions
    • Compiled the business process bottleneck

    I have audited the provided memory logs for the Python project. I identified one primary temporal conflict regarding the web framework and one superseded architectural record.

    Memory Audit Report

    Claim IDEntitySourceFindingRecommendation
    MEM-001Web FrameworkLog AOutdated (Flask 2.0)Supersede by MEM-002
    MEM-002Web FrameworkLog BCurrent (FastAPI)Retain (Feb 2024 timestamp)
    MEM-003Auth StrategyLog ANo newer recordRetain (Qualified: Unknown freshness)

    Resolved Context Pack

    {
      "project_metadata": {
        "primary_framework": "FastAPI",
        "migration_date": "2024-02",
        "status": "active"
      },
      "active_context": [
        {
          "id": "MEM-002",
          "text": "The project is migrated to FastAPI as of Feb 2024.",
          "provenance": "Log B"
        },
        {
          "id": "MEM-003",
          "text": "Standard JWT authentication is implemented.",
          "provenance": "Log A",
          "flags": ["freshness_unknown"]
        }
      ]
    }
    

    Next steps

    1. Verify if the JWT authentication mentioned in Log A (MEM-003) is compatible with the FastAPI migration.
    2. Archive the Flask-specific middleware instructions from Log A to prevent context pollution.

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

    About this skill

    Context bloat and stale agent memories lead to hallucination and conflicting outputs. This skill audits agent memory records for contradictions, outdated facts, and weak provenance before you reuse the context.

    What it does

    • Conflict detection identifies direct contradictions and scope mismatches between memory logs.
    • Staleness assessment evaluates record freshness based on timestamps, volatility, and task sensitivity.
    • Provenance evaluation weighs authority and corroboration to resolve conflicting claims.
    • Deduplication finds redundant entries and merges them without losing source evidence.
    • Context packaging generates a clean, resolved context set ready for agent injection.

    How it works

    1. Supply a collection of memory records, logs, or long-term storage entries.
    2. The skill extracts entities, dates, and claims while preserving source references.
    3. It runs a multi-pass audit for temporal updates and scope conflicts.
    4. It categorizes each entry with specific recommendations like retain, supersede, or quarantine.
    5. You receive a resolved context pack and a list of unresolved questions requiring human verification.

    Frameworks & tools

    Works with any JSON or Markdown-based agent memory store, vector DB exports, or long-term scratchpads.

    Why this beats prompting it yourself

    Standard prompts often default to "most recent is best," which destroys authoritative historical data. This skill uses a multi-factor weighting system that considers provenance and scope, ensuring project-specific nuances aren't erased by recency bias.

    Use cases

    • Auditing long-running agent logs for a complex software project.
    • Merging disparate memory files from different sub-agents into a single source of truth.
    • Identifying stale configuration instructions that were superseded in later sessions.

    Known limitations

    Evaluates only supplied records. It cannot verify external "truth" without provided authoritative evidence or timestamps.

    Requires timestamps for freshness analysis. Cannot verify facts against live external data. Does not auto-delete records without authorization.

    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 any payment.

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    Security scan

    Security scan passed 18 days ago

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    Listed18 days ago

    What's inside

    Frequently Asked Questions

    This skill solves the 'context bloat' problem where AI agents become confused by contradictory, outdated, or redundant information stored in their long-term memory or scratchpads.

    It is framework-agnostic and works with any agent system that can export its memory or logs in JSON or Markdown formats, including vector database exports.

    Unlike simple prompts that often prioritize the newest data, this skill uses a multi-factor weighting system that balances timestamps with authority (provenance) to ensure critical historical data isn't accidentally deleted.

    You receive the core auditing logic, a pre-configured workflow for processing memory records, and templates for generating the final resolved context packs.

    The skill provides a 'Quarantine' list of unresolved contradictions that require human intervention, ensuring you maintain full control over ambiguous data.

    Setup is straightforward: you simply point the skill toward your existing memory logs or DB exports; no complex API integrations or fine-tuning are required to begin the audit.

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