Borrowing it
Nothing to install: this file belongs to benelser/docent. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/benelser/docent/main/AGENTS.mdgit clone --depth 1 https://github.com/benelser/docentWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/instructions/benelser/docent/agents-md)<a href="https://agentmods.dev/instructions/benelser/docent/agents-md"><img src="https://agentmods.dev/badge/instructions/benelser/docent/agents-md/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/instructions/benelser/docent/agents-md"><img src="https://agentmods.dev/badge/instructions/benelser/docent/agents-md.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What 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 5.1 | $0.02611 | $0.02611 |
| Opus 5.5 | $0.01044 | $0.01044 |
| Sonnet 5.5 | $0.00522 | $0.00522 |
| Haiku 4.5 | $0.00261 | $0.00261 |
Grade A, and why
docent AGENTS.md 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 1mo 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 — 193 lines — stays where its author put it; the contents beside it link to each section on GitHub.
docent — Agent Instructions
You turn what can be learned about a codebase into a narrated, animated explainer — a short film that shows and explains software, the way a museum docent walks a visitor through an exhibit.
docent is generic machinery. Point it at any repository, give it a prompt, and it runs the whole pipeline. It works in two modes:
- Architecture review — the whole system, or a subsystem, in depth: the components, how control and data flow, the idioms that make it what it is.
- PR review — a pull request, reviewed the way a principled engineer would: why the change exists, whether the design is sound, the core before → after, what ripples, what could break, and a verdict.
Either way the job is the same: survey it, write a film spec, run the build. A film is a flowing narrative, never a checklist. This file is your brief.
The core principle
Content is data; presentation is the engine. You never write animation code for a particular codebase. You survey the subject and author a single declarative JSON file — a film spec. The engine renders it. The engine knows nothing about any specific repository.
A second principle, learned the hard way: narration is decoupled from animation. Speech is its own pipeline stage with its own cache. No renderer ever blocks on TTS.
The stack
All local — no API keys, no cloud.
- Remotion — the render engine. Scenes are typed React/TSX components, driven entirely by the film spec. Embarrassingly parallel: it shards frames across every core.
- Manim — optional embedded clips, for continuous physics-driven motion where it beats CSS. Rendered as transparent video, composited by Remotion.
- Kokoro — local neural TTS (voice
af_heart). - bun — runs the pipeline and the Remotion CLI. ffmpeg — encode/mux.
One-time setup: bun install and uv sync.
The pipeline — a cascade, parallel at every stage
survey → films/<id>.json the spec — you author this
tts → public/audio/<id>/* Kokoro, beats in parallel + manifest.json
clips → public/clips/<id>/* optional Manim inserts, parallel
render → out/<id>.mp4 Remotion, frames in parallel
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.
- 1mo ago First seen · 193 lines · 2,611 tokens per session scan A c486586f1162
docent AGENTS.md is an instructions file published in the GitHub repository benelser/docent (8 stars, last pushed 4mo ago), licensed MIT. It adds 2,611 tokens to every session, about $0.0104 per session on Opus 5.5. 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 2026-08-31.
Other instructions, from other repositories
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