Ken Imoto
AI Agent Engineer
LLMO · Context Engineering · Harness Engineering
Creator of the LLMO Framework. I design harnesses that automate dev, ops, and marketing with Claude Code.
400K+
Total PV (Qiita+Zenn)
40+
Books (Kindle+Zenn)
4
Research Papers
Now
- Building the LLMO Framework, LLMO Checker, and the Open LLMO Research Initiative
- Documenting AI-Native MEO as the local-business implementation of LLMO
- Applying Harness Engineering to products — dev, ops, and marketing with AI agents
- Publishing books & articles from hands-on practice
Updated July 2026
Publications
🔧 AI DEVELOPMENT
🔍 LLMO / AI SEARCH OPTIMIZATION
🛡️ SECURITY & QUALITY
📊 KNOWLEDGE & DATA
💡 ENGINEERING CULTURE
Latest Articles
- 📝 Article Schema Alone Didn't Make AI Recognize Me as the Author. The Entity Wiring That Did (in 4 JSON-LD Fields). Jul 2026
- 📝 I Forked OpenCut classic to Put an MCP Server on It — Four Traps I Hit Before Claude Could Drive the Video Editor Jul 2026
- 📝 Pre-flight your MCP: four layers to grade a server before you publish it Jul 2026
- 📝 The Skill Eval Repo I Didn't Build: 107 SKILL.md Files, 6 Checks, 21 False Positives Jul 2026
- 📝 Ship a product, get a support button for free: an edge-injected overlay on Cloudflare Workers Jul 2026
- 📝 FUNDING.yml alone won't show a Sponsor button: notes from auditing 42 repos Jul 2026
Papers
⚡ When Free Executors Cost More: The Free-Executor Paradox in Iterative LLM Code-Repair Loops
Four LLM configurations on three Python code-repair tasks (breakage / refactor / feature-add) under a single deterministic correctness judge (mypy + ruff + pytest). The canonical "strong orchestrator + cheap executor" structure (Opus + local Qwen) turns out to be the most expensive cloud arm on every task because prompt-cached re-reads of executor summaries dominate any savings from delegating execution. Haiku-solo wins on dollar cost on the largest task (5.5× cheaper) at a 25% failure rate.
📊 Excess Vocabulary in Japanese AI-Generated Text: A Cross-Model Quantitative Analysis
350 samples from 7 LLMs vs. 977 human articles. 651 statistically significant excess words identified. Newer Claude generations show increasing vocabulary bias (TTR: 0.52→0.29). Cross-domain classifier achieves AUC=0.946 in-domain but fails completely across genres. First Japanese excess vocabulary study with coevolution evidence.
📝 AI Text Slop: Stylistic Convergence Across Six LLMs in Japanese Technical Writing
180 samples (6 models × 10 topics × 3 trials) measured with 16 pattern indicators. RLHF-aligned commercial models score significantly higher than OSS (Cohen's d = 1.01). Vocabulary and structural patterns dissociate ("Swallow Paradox"). Human Qiita articles score higher than AI on structural metrics, revealing cultural confounding.
🔵 AI Blue: Systematic Color Recognition Bias in Vision-Language Models
We tested 4 VLMs × 40 colors × 480 observations. Commercial models miss intermediate hues. 95% of AI-generated UI colors land on blue-purple. First quantitative link between VLM color bias and the "AI Slop" phenomenon.
Research
🔬 LLMO Framework
Optimizing content visibility in AI search engines
🔬 Voice AI 300ms
Breaking the latency barrier for human-AI conversation
🔬 Context Engineering
CLAUDE.md, multi-agent architecture, AI dev workflows
🔬 Generative Agent Simulation
LLM multi-agent social simulation
🔬 Fragrance × AI
AI-powered perfumery and personality-based scent design
Projects & Sites
Propel-Lab — company
AI Systems consultancy focused on LLM development environments and harness engineering for enterprises.
LLMO Framework — open methodology
A practical framework for LLM Optimization (AEO + GEO). Self-hosted documentation site as the canonical source.
mypcrig — PC selection (JA)
A use-case-driven guide for choosing the right PC: AI dev rigs, gaming, laptops, and parts. Bench-driven recommendations.
legacydram — whisky × engineering eyes
A curation media reading every bottle as somebody's commit history. People · Craft · Tasting.
ainativemeo — AI-Native MEO playbook
Local-business optimization for the era when customers ask AI, not search engines, for recommendations. Engineering-layer reading of Google Business Profile, JSON-LD survivability, and per-engine citation behavior. Bilingual EN / JA.
Open Source
🔍 LLMO / AI SEARCH
open-llmo — GitHub organization · MIT
The Open LLMO Research Initiative. Specs, validators, and tooling for measuring how AI-retrievable a URL is — beyond traditional SEO signals.
llmo-checker — TypeScript CLI · MIT
A Lighthouse-style CLI that scores how AI-retrievable a URL is. Runs static checks for llms.txt, JSON-LD, robots policy, and semantic structure, and outputs a 0–100 LLMO Score as JSON. v0.1 draft.
🗄️ RAG & RETRIEVAL
rag-db-advisor — Python · MCP + CLI · MIT
An evidence-based advisor for RAG stack decisions — every claim backed by rag-retriever-bench measurements. Ask which vector backend fits your workload or which operational trap you're about to step on; it answers only from measured evidence, over MCP or CLI.
rag-retriever-bench — Python · MIT
A benchmark harness for RAG retrieval backends: same corpus, same queries, same metrics — swap the database. Seven backends (pgvector, ClickHouse, Qdrant, Weaviate, Milvus, Chroma, LanceDB), deterministic recall / latency / ingestion metrics with no LLM judge, plus a per-backend self-check that catches silent full-scan degradation.
🎙️ VOICE & SPEECH
voice-clone — Python · MIT
A voice-cloning tool built on Qwen3-TTS. Clone a voice from ~3 seconds of audio and have any text read in that voice. WSLg recording, multi-language.
speech-habit-lens — Python CLI · MIT
A 1-minute speech habit analyzer combining AmiVoice ESAS (20 acoustic emotion parameters) with an LLM. Three layers — acoustic, textual, and the cross-layer that links body and language — rendered as a Markdown report.
🛠️ DEV UTILITIES
compact-ops — Claude Code plugin · MIT
A transparent plugin that keeps Claude Code sessions coherent across context compaction: structured state capture before /compact, recovery injection right after compaction and on --resume, plus a self-contained usage warning. Derived from u-ichi's compact-plus.
domain-pre-flight — Python CLI · MIT
A pre-flight check before you register a domain for a new site or app. Structure, history, typosquat, multi-language semantics, LLMO, and trademark deeplinks in one command.