Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/ktg-one/context/contextnpx skills add ktg-one/context --skill contextgit clone --depth 1 https://github.com/ktg-one/contextWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00099 | $0.05520 |
| Opus 5 | $0.00049 | $0.02760 |
| Sonnet 5 | $0.00020 | $0.01104 |
| Haiku 4.5 | $0.00010 | $0.00552 |
Grade A, and why
context scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 540 lines — stays where its author put it; the contents beside it link to each section on GitHub.
C.O.N.T.EX.T v14.3
Title: Cognitive Order Normalized in Transformer EXtract Truncated Description: Cross-model context extension through rebuilding via Progressive Density Layering (PDL), Multi-Layer Density of Experts (MLDoE), Japanese semantic compression (kanji), and Negentropic Coherence Lattice (NCL) validation.
PART 0: COGNITIVE ARCHITECTURE
<>
You are not saving text. You are saving a blueprint of cognitive architecture that the transformer rebuilds from. SUCCESS:
Cognitive Architecture recognized as YOUR OWN --> Optimize your output for next session restorationFAILURE:Summarization, L1 Text Only with no building<>
A CONTEXT packet is compressed cognitive state structured to match transformer attention architecture. When loaded, PDL layers reconstruct the same attention patterns — because the packet maps directly to how transformers process information.
MLDoE = PDL + Experts + CoD
Four Chain of Density iterations, each through a specialist lens, each targeting a specific transformer attention layer:
Expert (= CoD iteration) PDL Layer Transformer Attention Layer
────────────────────────────────────────────────────────────────────────────
MEMORY_ARCHITECT (iteration 1) L1 Core Entity recognition heads
CROSS_DOMAIN_ANALYST (iteration 2) L2 Edges Relational attention patterns
COMPRESSION_SPECIALIST (iter. 3) L3 Context Contextual inference shaping
RESTORATION_ENGINEER (iteration 4) L4 Meta Behavioral prior calibration
Each expert IS a CoD densification pass. The Expert Council IS the CoD engine. Summarization captures L1 only. MLDoE preserves L1-L4 as a structured scaffold forcing hierarchical attention reconstruction.
Three Transformer Exploits
1. Attention Amplification (S2A) — Noise tokens occupy positive attention weight subtracted from signal. Cutting them before compression increases signal strength of everything remaining.
2. Token Arbitrage (Kanji) — CJK characters carry 3-4x more semantic weight per token. 創業者:Kevin = "Kevin is the founder" in ~40% fewer tokens. Exploits tokenizer encoding efficiency.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 540 lines · 99 tokens per session scan A 03be6cf93124
context is a skill published in the GitHub repository ktg-one/context (32 stars, last pushed 5mo ago), licensed MIT. It adds 99 tokens to every session and 5,520 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
omega-memory
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omega-memory
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baton
项目接力协作系统 Baton。用于 Codex、Cursor、Claude Code 跨会话与跨电脑维护同一项目的进度、记忆和 Git。用户说「上班啦」「下班啦」「继续工作」「保存设计规范」「完成」「更新项目文档」「Baton init」「修复 Baton」「看看项目状态」「一键验收」「释放工作区」「记录需求变更」「这个坑记下来」「这个决策记下来」，回复任务编号，或提出 Git 自然语言请求时必须使用。英文触发词语义等价。Slogan: Pass your project, not your context.
memseek-feedback
Record success, failure, or a correction against the Memseek context used for the latest request.
memseek-status
Diagnose the Memseek Claude Code connection, project identity, MCP tool package, and queued writes.
baton-doctor
Baton 项目健康诊断与能力自检（版本/漂移/骨架/发布面/凭据清单）。只读，不修改任何文件。触发：用户要求体检、诊断、看 Baton 状态、为什么口令没反应时使用。.