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    CClaude CodeCCursorCCodex CLIGGitHub CopilotGGemini CLIVVS CodeWWindsurfMManus+14 more

    A high-density structured response protocol for radical information compression and mandatory artifact generation.

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    See it in action

    You say

    free(n): Design a minimal nanocraft g-force sensor payload with ADC calibration

    Your agent does

    n=1 | Exchange 01 DESC: Nanocraft G-Force/ADC EVAL: [Density High] RESULT: 0 -> COMPUTING

    01. SENSOR ARRAY Low-mass piezoresistive MEMS bridge outputting mV/g.

    02. ADC CHAIN 16-bit Sigma-Delta with Vref stable at 2.5V.

    03. CALIBRATION WHY: Compensates for zero-g bias. ARTIFACT: uint16_t cal = read_raw(); // static g_force = (raw - cal) * scale;

    What you get

    Eliminate LLM verbosity and conversational filler automatically.Force the generation of copy-pasteable code and specs every 3 topics.Track session state and complexity via mandatory log headers.Increase information density for complex technical problem solving.

    About this skill

    The problem

    Standard LLM responses are often bloated with conversational filler, hedging, and redundant restatements. Developers lose time parsing paragraphs of text to find the actual code or technical data they need.

    What it does

    • Enforces radical 90% text compression, stripping away all non-essential language.
    • Tracks session state using a mandatory log header for every exchange.
    • Structures answers into dense, numbered topics with a strict 25-word limit per point.
    • Guarantees a production-ready artifact (code, spec, or formula) for every three topics discussed.
    • Automatically increases information density through an internal feedback loop as the conversation progresses.

    Frameworks & tools

    Native support for Claude Projects, ChatGPT Custom GPTs, and Grok Skills. Compatible with any LLM via system instructions.

    Why this beats prompting it yourself

    Manual prompts for brevity often fail as the context window grows. This skill uses a formal state-tracking protocol and incremental "density pressure" to ensure the model never regresses into verbose chat patterns.

    Use cases

    • Extracting dense technical specifications from vague architectural ideas.
    • Rapidly generating code snippets and checklists without chatty introductions.
    • Maintaining tight, logical state tracking across long-running technical deep dives.
    • Building high-density knowledge bases where only essential data is captured.

    How to install

    Drop the file into your AI Agent. Works with Claude, Cursor, ChatGPT, and 20+ more.

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