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/hughyau/academicforge/algorithmic-artnpx skills add HughYau/AcademicForge --skill algorithmic-artgit clone --depth 1 https://github.com/HughYau/AcademicForgeWrote 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/hughyau/academicforge/algorithmic-art)<a href="https://agentmods.dev/skills/hughyau/academicforge/algorithmic-art"><img src="https://agentmods.dev/badge/skills/hughyau/academicforge/algorithmic-art.svg" alt="Measured on agentmods" 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 | $0.00062 | $0.04180 |
| Opus 5 | $0.00031 | $0.02090 |
| Sonnet 5 | $0.00012 | $0.00836 |
| Haiku 4.5 | $0.00006 | $0.00418 |
Grade A, and why
algorithmic-art 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 5d 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 ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 5d ago First seen · 405 lines · 62 tokens per session scan A 6688fc81bb20
algorithmic-art is a skill published in the GitHub repository HughYau/AcademicForge (2,512 stars, last pushed 5d ago), with no licence file. It adds 62 tokens to every session and 4,180 once invoked, about $0.0003 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
paper-writing
Use when and ONLY when the user explicitly requests paper writing and G4 (write-ready) gate has passed — handles LaTeX structure, senior-level writing, reference verification, and iterative refinement.
fable4sci
Transform dense technical, scientific, cybersecurity, computing, network, or life-science research questions into clear source-domain-neutral fables for non-specialists. Use when the user asks to allegorize, fable-ize, turn complex research into a metaphorical story, explain without jargon, produce both plain and more…
experiment-execution
Use when G2 (plan freeze) and G3 (execution readiness) gates have passed — handles all implementation, baseline reproduction, experimentation, and iteration for both Type M and Type D projects; this is Phase 4.
method-framework-design
Use when research direction is validated (G1 passed) and the project needs method design, analysis framework, evaluation protocol, or story line — this is Phase 3 and must complete before any code or experiments.
research-direction-exploration
Use when a user expresses research intent but has not yet identified a specific research direction, completed a literature review, or confirmed resource readiness — covers Phase 1 from topic discovery through G1 gate.
results-integration
Use when core experiments or analyses are complete and results need to be organized into a coherent report for user review before paper writing.