Works with the AI Agents you already use

    Claude CodeClaude CodeCursorCursorCodex CLICodex CLIMuseMuseDotsDots+21 more

    Context Save Cli

    2

    Compress noisy chat logs and logs into durable, high-signal memory reports with built-in duplicate suppression.

    $5

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    context-save-cli

    context-save-cli

    Example session with this skill installed

    Run context-save-cli on transcript.md and wiki/index.md. Identify key decisions regarding gpt-4o-mini and the auth validation path, then check for AI-smell.

    • Read your context and instructions
    • Compiled the context-save-cli

    Token Budget: 4500 -> 320 tokens (92% reduction)

    Durable Candidates

    • Decided: Use gpt-4o-mini for routing
    • Path: /src/auth/validate.py verified
    • Patch: Add 'retry_count' to user_config

    Open Questions

    • Should we persist JWTs in local storage or cookies?
      AI-Smell Score: 1 (Minimal)

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

    About this skill

    What it does

    The Context Save CLI is a specialized utility designed to solve the "context window bloat" problem. It uses a bundled Python script to ingest massive transcripts, logs, or agent traces and compress them into high-signal, evidence-preserving reports. Unlike generic summaries, this skill identifies durable deltas—facts, decisions, and outcomes that actually matter for long-term memory—while discarding transient narrations and redundant assistant "self-talk".

    Why use this skill

    As sessions grow longer, LLMs become expensive and prone to hallucination. This skill acts as a middleware for your agent's memory. Instead of feeding the next agent a raw 50KB transcript, you feed it a 2KB compressed report. It includes built-in duplicate suppression (checking against existing wikis), AI-smell detection, and durable memory extraction. It ensures your project documentation stays lean, factual, and free of "AI-generated" fluff without needing multiple separate plugins.

    Supported tools

    • Python CLI: A standalone script for pre-filtering data before LLM processing.
    • Format Support: Outputs in structured Markdown or JSON.
    • Integration: Works with any wiki-style documentation or handoff brief system.

    • Requires Python 3.x installed.
    • Pre-filtering is heuristic; must manually verify edge-case facts.
    • No auto-writing to wikis; output is a patch suggestion only.

    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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    Trust & safety

    Security scan

    Security scan passed 6 months ago

    Permissions

    Terminal / Shell
    Listed6 months ago

    What's inside

    Frequently Asked Questions

    It uses a Python-based utility to strip away conversational fluff and redundant metadata, transforming massive chat logs into dense 'durable deltas' that preserve only essential facts, decisions, and outcomes.

    The bundle includes the specialized Python CLI script, setup documentation, and pre-configured templates for Markdown and JSON output formats.

    Yes, the tool features built-in duplicate suppression that cross-references your current logs against existing project wikis to ensure only new, unique information is recorded.

    It is designed as a standalone middleware utility that works with any agent platform capable of executing Python scripts or ingesting external text files, making it highly compatible across different LLM ecosystems.

    By compressing 50KB transcripts into roughly 2KB high-signal reports, this tool significantly lowers token usage and reduces the risk of hallucinations caused by 'context window bloat' in long-running sessions.

    New in Agents & Orchestration