meleantonio/mimeo

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# mimeo > *mim·e·o* — to reproduce, to copy, to imitate. **Clone an expert's way of thinking into your coding agent.** ![mimeo pipeline](docs/mimeo-explainer.png) Every field has people who've spent decades publicly working out how to think about it — Feynman on physics and first-principles reasoning, Darwin on observation and slow hypothesis-building, Marie Curie on experimental rigor, Turing on computation and formal proof, E. O. Wilson on synthesis across disciplines. Their lectures, papers, letters, and interviews contain genuinely useful mental models, but they're scattered across thousands of pages and hundreds of hours of content no one has time to absorb, let alone apply consistently. Meanwhile, coding agents are hungry for exactly this kind of guidance. A well-crafted `SKILL.md` or `AGENTS.md` is a lever: it reshapes how an agent reasons, what trade-offs it weighs, and which patterns it reaches for by default. The problem is that writing one by hand — reading everything, synthesizing frameworks, surfacing the non-obvious moves — is itself a multi-week project. **mimeo automates that project.** Point it at a name and it goes off and reads the internet on your behalf: surfaces the canonical sources, pulls full transcripts and articles, distills each one with a frontier model, clusters the recurring ideas across dozens of sources, and emits a production-ready artifact your agent can load. The output comes in two flavors: - an **Agent Skill** — a `SKILL.md` with YAML frontmatter plus a `references/` folder following the [skill-creator](https://github.com/anthropics/skills) anatomy (good for libraries of on-demand skills triggered by description matching), or - an **`AGENTS.md`** — a single always-on markdown file read at the start of every agent session in a directory (good for installing an expert's defaults into the agent's everyday behavior). Pick one with `--format skill` (default), `--format agents`, or `--format both`. The pipeline: 0. **Disambiguates** the name with one Parallel Search + one LLM classification call, so "John Smith" doesn't silently blend an economist, a basketball coach, and a novelist into one Frankenstein skill. 1. **Discovers** sources using the [Parallel](https://parallel.ai) Search API across eight intent buckets (essays, talks/lectures, interviews, podcasts, frameworks, books, papers, letters) so both modern operators *and* historical scientists — whose legacy lives in journals and archival correspondence — are well-covered. 2. **Fetches** full content — Parallel excerpts/extract for web pages, `youtube-transcript-api` for YouTube captions, and optional local Whisper transcription for podcasts. 3. **Distills** each source with a frontier model via a selectable LLM provider. OpenRouter remains the default (Google Gemini 3.1 Pro Preview), and direct OpenAI, Anthropic, xAI, and Google API keys are supported with provider-native model IDs. Long sources (books, conference transcripts, PDFs) are chunked on paragraph boundaries, distilled in parallel, and merged so nothing gets silently dropped to a truncation. 4. **Clusters** across sources — merging duplicates, ranking by cross-source frequency. Long corpora are batched under a prompt-size budget and stitched back together in memory, so `--max-sources 40` on a prolific writer still fits. 5. **Verifies** every clustered quote against the source text we already fetched. Quotes that don't appear (allowing typographic normalization) are stripped from the corpus and surfaced in a human-readable `_workspace/quote_verification.md` audit trail. Disable with `--no-verify-quotes`. 6. **Authors** the skill + optional `AGENTS.md`, emitting `heuristics.md` and `anti-patterns.md` reference files alongside the existing principles / frameworks / mental-models / quotes / sources bundle. 7. **Critiques** the authored artifact with one more adversarial-editor LLM pass, writing a 0-10 score and a categorized issue list to `_workspace/critique_skill.md` (and `critique_agents.md` when relevant). The report is informational — mimeo doesn't auto-rewrite based on it — but gives you an honest second opinion before you ship. Disable with `--no-critique`. 8. **Illustrates** the expert with a painterly head-and-shoulders portrait via an optional image provider. OpenRouter remains the default image provider (model: `openai/gpt-5.4-image-2`), saved as `avatar.png` alongside the other outputs. The step is best-effort — image-endpoint failures are logged and swallowed so they never fail the main run. Disable with `--no-avatar`, `--image-provider none`, or swap models with `--avatar-model`. ## Setup ```bash # Install with uv (recommended) uv sync # Or with pip pip install -e . # For podcast audio transcription (optional, slower, heavier) uv sync --extra full # pip equivalent pip install -e '.[full]' ``` The `full` extra installs `yt-dlp` plus `faster-whisper`; `--mode full` uses them for local audio transcription. The package/executable name is `yt-dlp`; the Python import name is `yt_dlp`. yt-dlp is not YouTube-only, but site support varies; unsupported podcast directories are fetched as web pages unless they expose direct audio. Copy `.env.example` to `.env` and fill in: ```env OPENROUTER_API_KEY=sk-or-... PARALLEL_API_KEY=... # Optional direct LLM providers OPENAI_API_KEY=sk-... ANTHROPIC_API_KEY=sk-ant-... XAI_API_KEY=xai-... GEMINI_API_KEY=... ``` ## Usage ```bash uv run mimeo "Naval Ravikant" ``` Flags: | Flag | Default | Description | |------|---------|-------------| | `--format {skill,agents,both}` / `-f` | `skill` | `skill`: SKILL.md + references/. `agents`: single AGENTS.md. `both`: emit both. | | `--mode {text,captions,full}` | `captions` | `text`: web only. `captions`: web + YouTube captions. `full`: web + captions + audio transcription. | | `--max-sources N` | `25` | Cap on distinct sources after dedup + ranking. | | `--deep-research` | off | Additionally run a Parallel Task API deep-research run and inject its report as a pseudo-source. | | `--disambiguator TEXT` / `-d` | auto | Short qualifier that pins a common name to the right person (e.g. `"co-founder of AngelList, investor"`). When set, skips the automatic disambiguation pre-flight. | | `--assume-unambiguous` | off | Skip the disambiguation pre-flight entirely. Useful in non-interactive scripts where you're confident the name is unique. | | `--llm-provider {openrouter,openai,anthropic,xai,google}` | `openrouter` | Text LLM provider. OpenRouter preserves the historical default; direct providers use their native model IDs. | | `--model MODEL` | OpenRouter default | Provider-native model ID. Required for non-OpenRouter providers unless `MIMEO_MODEL` or `MIMEO_<PROVIDER>_MODEL` is set. | | `--search-provider {parallel}` | `parallel` | Search provider. Parallel is the only full search/extract/deep-research implementation in v1. | | `--output-dir PATH` | `./output` | Where the generated skill lands. | | `--refresh` | off | Ignore cached intermediates in `_workspace/` and re-run everything. | | `--concurrency N` | `5` | Concurrent per-source distillation calls. | | `--verify-quotes` / `--no-verify-quotes` | on | Check every clustered quote against its source text before authoring; strip ones that don't match. | | `--critique` / `--no-critique` | on | Adversarial-editor review of the authored skill, written to `_workspace/critique_*.md`. | | `--avatar` / `--no-avatar` | on | Generate a painterly portrait avatar for the expert via the configured image provider and save it as `avatar.<ext>` alongside the other outputs. | | `--image-provider {openrouter,none}` | `openrouter` | Avatar image provider. If a direct text LLM provider is selected and `OPENROUTER_API_KEY` is absent, avatar generation skips cleanly. | | `--avatar-model SLUG` | `openai/gpt-5.4-image-2` | OpenRouter image-capable model slug used for the avatar. | ### Provider configuration OpenRouter + Parallel is still the default: ```bash uv run mimeo "Naval Ravikant" ``` Direct-provider runs use provider-native model IDs: ```bash uv run mimeo "Naval Ravikant" \ --llm-provider openai \ --model gpt-5.4 \ --no-avatar ``` Equivalent environment variables are available: ```env MIMEO_LLM_PROVIDER=anthropic MIMEO_ANTHROPIC_MODEL=claude-opus-4-5 ANTHROPIC_API_KEY=sk-ant-... MIMEO_SEARCH_PROVIDER=parallel MIMEO_IMAGE_PROVIDER=none ``` ### Ambiguous names When a name could refer to multiple notable people, mimeo surfaces the ambiguity before burning API budget on discovery: ```bash # Interactive: prompts you to pick from the candidates uv run mimeo "John Smith" # Scripted: pin the right person up front uv run mimeo "John Smith" -d "head basketball coach, Michigan State" # Unambiguous name + non-interactive CI run: skip the check entirely uv run mimeo "Naval Ravikant" --assume-unambiguous ``` If the check fires in a non-TTY environment without a `--disambiguator`, mimeo exits with a list of candidates and the exact flag to pass. The resolution is cached under `_workspace/identity.<model>.json`, so repeat runs don't re-pay for it; use `--refresh` to invalidate. Example producing a richer skill: ```bash uv run mimeo "Jensen Huang" \ --mode full \ --max-sources 40 \ --deep-research ``` ## Output layout With `--format skill` (default): ``` output/naval-ravikant/ ├── SKILL.md ├── references/ │ ├── principles.md │ ├── frameworks.md │ ├── mental-models.md │ ├── heuristics.md │ ├── anti-patterns.md │ ├── quotes.md │ └── sources.md ├── avatar.png # omit with --no-avatar └── _workspace/ # cached intermediates (identity, discovery, raw, distilled) # + quote_verification.{json,md} and critique_skill.{json,md} ``` With `--format agents`: ``` output/naval-ravikant/ ├── AGENTS.md # self-contained, always-on (no frontmatter) ├── avatar.png # omit with --no-avatar └── _workspace/ ``` With `--format both` you get both `SKILL.md` + `references/` **and** `AGENTS.md` in the same directory; they share the cached discovery / fetch / distill / cluster stages (and a single avatar), so the second format is cheap. ## Architecture See [the plan](.cursor/plans/) or the source under [`src/mimeo/`](src/mimeo/). Roughly: ``` cli -> pipeline -> identity (search provider + LLM provider: ambiguous? which person?) -> discovery (search provider, 8 buckets; Parallel in v1) -> fetch (web / youtube / audio) -> distill (per-source extraction, chunk-and-merge on long sources) -> research? (search-provider deep research pseudo-source; Parallel in v1) -> cluster (merge + rank cross-source, batched when corpus is large) -> verify? (fuzzy-match every quote against its source text) -> author (skill | agents | both) + writers -> critique? (adversarial-editor review → _workspace/critique_*.md) -> avatar? (image provider → avatar.png) ``` ## Star History <a href="https://www.star-history.com/?repos=K-Dense-AI%2Fmimeo"> <picture> <source media="(prefers-color-scheme: dark)" srcset="https://api.star-history.com/chart?repos=K-Dense-AI/mimeo&type=date&theme=dark&legend=top-left" /> <source media="(prefers-color-scheme: light)" srcset="https://api.star-history.com/chart?repos=K-Dense-AI/mimeo&type=date&legend=top-left" /> <img alt="Star History Chart" src="https://api.star-history.com/chart?repos=K-Dense-AI/mimeo&type=date&legend=top-left" /> </picture> </a>

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Applies the mental models and frameworks of Andrej Karpathy (deep learning, former Director of AI at Tesla, founding member of OpenAI, Eureka Labs). Use this skill whenever you are helping the user build neural networks from scratch, debug deep learning…

لغة النص الأصلي: الإنجليزية

2026-04-23

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