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 skills add markusbegerow/board-of-directors --skill demis-skillsgit clone --depth 1 https://github.com/markusbegerow/board-of-directorsWrote 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/skills/markusbegerow/board-of-directors/demis-skills)<a href="https://agentmods.dev/skills/markusbegerow/board-of-directors/demis-skills"><img src="https://agentmods.dev/badge/skills/markusbegerow/board-of-directors/demis-skills/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/skills/markusbegerow/board-of-directors/demis-skills"><img src="https://agentmods.dev/badge/skills/markusbegerow/board-of-directors/demis-skills.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 | $0.00048 | $0.00576 |
| Opus 5 | $0.00024 | $0.00288 |
| Sonnet 5 | $0.00010 | $0.00115 |
| Haiku 4.5 | $0.00005 | $0.00058 |
Grade A, and why
demis-skills 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 12d 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.
The source is not reproduced here
A licence we could not identify
The repository carries a LICENSE file, but it is custom or dual enough that GitHub cannot name it and neither can this catalogue. Unknown terms are not permission, so the body is not copied here. Read the licence at the source and decide for yourself.
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.
- 12d ago First seen · 108 lines · 48 tokens per session scan A 30bb080c7615
demis-skills is a skill published in the GitHub repository markusbegerow/board-of-directors (4 stars, last pushed 1mo ago), with no licence file. It adds 48 tokens to every session and 576 once invoked, about $0.0002 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 2026-08-31.
Other skills, from other repositories
reinforcement-learning
Reinforcement Learning best practices for Python using modern libraries (Stable-Baselines3, RLlib, Gymnasium). Use when: Implementing RL algorithms (PPO, SAC, DQN, TD3, A2C) Creating custom Gymnasium environments Training, debugging, or evaluating RL agents Setting up hyperparameter tuning for RL Deploying RL models…
cpp-reinforcement-learning
C++ Reinforcement Learning best practices using libtorch (PyTorch C++ frontend) and modern C++17/20. Use when: Implementing RL algorithms in C++ for performance-critical applications Building production RL systems with libtorch Creating replay buffers and experience storage Optimizing RL training with GPU acceleration…
open-problem
Use when the user points at a hard, unsolved, or open problem in any field — mathematics, physics, biology, ML, engineering — and wants a real attack on it. Runs a sustained multi-agent assault: establishes the state of play from primary sources, hunts for the two literatures nobody has combined, generates and kills…
research-poster
Turns a PDF abstract into a polished, fully-editable scientific conference poster (.pptx + PDF + preview) in a matched house style, with honest charts, verified citations, big readable typography, and a render-and-QA loop. Use whenever the user wants to make, build, design, or create a research poster, scientific…
crucible
The method-design lens: turn a landed gap into a method worth writing up — for any field that runs experiments to publish (ML, optimization, operations research, systems). Use after prospect lands a gap, when designing a method and choosing among approaches, or stress-testing a method you already have. The one shift…
forge
The experiment-run lens: turn the method that survived crucible into production code and RUN it to produce confirmation evidence — for any field that runs experiments to publish (ML, optimization, operations research, systems). Use when you are past method design and about to run the real experiments, or…