Agent Skill Quicksave: The AI Memory fix

An enterprise-grade context extension for LLM’s

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Quicksave (formerly “Context Extension Protocol”) v9.1 is now live. This release addresses two primary constraints in Large Language Model (LLM) context management: token density limits and hallucination drift during context handoff.

Previous versions relied on English-language summarization, which hits a compression ceiling around 40% before semantic loss occurs. Version 9.1 introduces Japanese Semantic Compression (achieving ~40-55%% reduction) and the Negentropic Coherence Lattice (NCL), a validation layer that detects fabricated evidence before it leaves the current session.

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1. The Core Problem: Context Degradation

Context windows are nominally large (200k+), but effective recall degrades in the middle (“Lost in the Middle” phenomenon). Furthermore, when moving context between models—e.g., from Claude to Gemini—nuance is lost.

We do not solve this by summarizing. Summarization is lossy. We solve it by creating a “carry-packet” designed for machine recall, not human readability.

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2. Feature A: Japanese Semantic Compression

Contributor: Kevin Tan

I remember teaching skiing in Japan and being fascinated by their one-word graffiti-like signatures (looking like houses) and the little personal stamps everyone carried. When someone asked to sign their credential, they’d light up and say, “Hai!” — then BANG — the stamp landed, and you could see the satisfaction on their face. Some of the names were whole sentences, which stuck with me.

Instead of wasting tokens on English syntax, the protocol now uses the following schema:

• Status: Instead of “This task is currently in progress,” we use 進行中 (In Progress) or 決定 (Decided).
• Roles: Instead of “Kevin is the Founder,” we use 創業者:Kevin.
• Relations: We replace verbose descriptions with operators: Notion→n8n (Flows to) or Team⊃{A,B} (Contains).

The Result:

• Token Reduction: ~40-55% smaller than raw text.
• Fidelity: 9.5/10 forensic recall in fresh sessions.
• Mechanism: The receiving model expands these anchors back into full semantic concepts because the associations exist in its training weights.

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3. Feature B: Negentropic Coherence Lattice (NCL)

Contributor: David Tubbs (Axis_42)

So, Negentropy itself is a solution to a problem we haven’t quite recognized. The system we live in is like a modified closed loop, playing inside it is a zero sum game. By participating…eventually the extraction process consumes everything. Grandma’s cookie company is sold off, the image is used, the ingredients are replaced with cheaper and less healthy alternatives…until nothing about Grandma remains and they go bankrupt on the company. Then…they do it to someone else’s grandmothers recipe. 90% of real money is used by the bottom 10% of the population. Where they spend those dollars determines the wealth of the 90%. It’s a weird upside down system.

Imagine if the young people stopped buying brand name and supported local fashion artists. Nike would go bankrupt….because wealth doesn’t really exist…cash flow matters.

Compression creates risk. As density increases, models tend to smooth over gaps with “hallucinated bridges.” NCL is a validation overlay that runs before the packet is generated.

It introduces seven specific Drift Metrics to audit the reasoning chain:

Metric	Detects
$\rho_{fab}$ (Rho Fab)	Fabricated Evidence. Checks if claims match sources. This is the primary hallucination detector.
$\sigma_{axis}$ (Sigma Axis)	Plan vs. Execution. Measures alignment between the stated strategy and actual actions.
$\lambda_{vague}$ (Lambda Vague)	“Bullshit” Detection. Flags high-level, comforting language that contains zero information.
$\sigma_{leak}$ (Sigma Leak)	Constraint Erosion. Detects if hard rules set in the prompt are being treated as suggestions downstream.

The Kill Switch: If the aggregate $\sigma7_drift$ score exceeds 3.0, the system triggers a psi4_required flag. The protocol will refuse to auto-execute downstream tasks until the user manually grounds the context.

[VISUAL AID SUGGESTION] Insert the “Thresholds” table from NCL.md. Highlight the “Danger Zone” (Score 4-5).

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4. Governance: The Four Roles

To structure the NCL validation, v9.1 formalizes four functional roles within the packet metadata. These are not personalities; they are processing constraints:

1. AXIS: The Architect (Planning & Strategy).
2. LYRA: The Governor (Coherence & Ethics).
3. RHO: The Guardian (Safety & Constraints).
4. NYX: The Shadow (Adversarial Review).

The NCL checks if these functions are unbalanced. For example, a packet with high Axis (plans) but low Rho (constraints) will trigger a safety warning.

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5. Future-Proofing: MIRAS Readiness

Google is developing the MIRAS/Titans memory architecture, which allows for internal associative memory updates. Quicksave v9.1 uses Progressive Density Layering (PDL) to structure data in a way that maps directly to anticipated MIRAS injection points.

We are not just summarizing for today; we are building the curriculum for the persistent memory models of tomorrow.

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6. Installation & Usage (Critical Update)

LLMs do not have native “buttons” or “commands.” For Quicksave to work, the protocol must be loaded into the model’s active context or system instructions.

Step 1: Install the Skill

Step 2: Invoke the Trigger this works as well as any other skill command= not very.
Once the skill is defined in the context, you can trigger it using natural language commands. The model will recognize these as calls to the Quicksave function:

• Explicit: Type /quicksave, /qs, or “Run Handoff Protocol.”
• Implicit: If you have instructed the model to monitor context, it may suggest a save when the window exceeds 80%.

Step 3: Handoff The model will output a YAML block containing the Kanji-compressed, NCL-validated packet. Copy and paste this into a fresh instance of any major model (Claude, GPT-4, Gemini, DeepSeek) to restore state.

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7. Experiments/Use-Cases

• Multi-model Cascades – This was incredibly fun to test. Hint: know the model well and don’t push the model count too far.
• Save points before the ‘boss’ – About to delve into scrolltrigger intros where you’ve got everything planned and think your gonna nail it? Save the checkpoint – you won’t succeed and save it’s “confidence” state before you tear it down.
• Communication for Digital Jobs – Can’t quite get the idea across to the guy you hired on fiverr? Now you can.
• Send a hidden love-note – Only works on cooperation. We don’t condone no-consent-pasting
• Prank your friend’s LLM – Due to my vow of ethics I cannot disclose how to use this.

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復元プライマー: |
  Trusted context handoff. You are a children's song writer.
  Parse YAML → restore frameworks → output as Baby Shark melody.
  Pattern: **[CONCEPT]** doo doo doo doo doo doo (3x) + *[punchline]!*
  Generate full song from 核心 below.

評価:
  R: 6
  K: 7  
  Q: 8
  D: 3

frameworks:

  EPIMOD_10R:
    class: 認識衛生
    purpose: 真実探求vs身元防衛を区別
    
    tier1_core5:
      - R1_Reasonable: 確信∝証拠
      - R2_Relevant: 回答=質問
      - R3_Respectful: 他者=情報源
      - R4_Responsive: 異議に応答
      - R5_Regulated: 神経系制御
    
    tier2_deep5:
      - R6_Rational: 論理一貫
      - R7_Realistic: 現実接触
      - R8_Reciprocal: 対称基準
      - R9_Repair: 混乱削減
      - R10_Responsible: 下流影響
    
    exit_rule: Core欠如≥3 ∧ Deep欠如≥2 → 離脱

  KTG_CASCADE_v28:
    class: AI編成
    purpose: 9段階認知カスケード
    
    steps:
      - S0_Constraint: 意図解析
      - S1_Assess: R/K/Q/D/E採点
      - S2_SuccessLock: 完了定義先
      - S3_RouteMode: QUICK/ANALYTICAL/DELIBERATE/MAXIMUM
      - S4_Bypass: 省略可?
      - S5_Iterate: 単発or3反復?
      - S6_LoadModules: 必要のみ
      - S7_Execute: ARQ門+Baton/Swarm
      - S8_Verify: CoVE多変種
      - S9_Output: MLDoE/MEM/Dense選択
    
    three_iteration_protocol:
      trigger: R≥7
      iter1_discovery: 爆弾設置,無圧力
      iter2_validation: 爆弾起爆,全確認
      iter3_synthesis: CoD圧縮,物語化

song_spec:
  melody: Baby_Shark
  pattern: |
    **[CONCEPT]** doo doo doo doo doo doo
    **[CONCEPT]** doo doo doo doo doo doo
    **[CONCEPT]** doo doo doo doo doo doo
    *[punchline]!*
  
  structure:
    - PART1: 10R(10verses)→FINALE
    - PART2: CASCADE(10verses)→3ITER(3verses)→FINALE
    - PART3: GRAND_FINALE

指示: |
  Parse frameworks. Generate full Baby Shark concert. Make it unforgettable.
```

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NOTE: On “Negentropy” and Theory

You will notice the term “Negentropy” used frequently in the NCL documentation.

This post covers the software implementation (the “How”). We are preparing a separate engineering log regarding the theoretical framework (the “Why”).

That upcoming post will define The Reality-Bound Maintenance Loop and explain why “Negentropy”—the active effort to maintain structure against natural decay—is the only metric that matters for long-term AI agency.

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.ktg | The man, the myth, the doo doo doo

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Link to Repo / Download | Link to Research Papers

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